# Dimension Labs > Dimension Labs is an analytics agent for customer language. Upload calls, support tickets, chats, reviews or surveys, join them to business data such as CRM, orders and renewals, and ask why a metric moved. The agent tags every conversation with typed fields (Dimensions), tests which issues actually move the metric, and returns findings with effect sizes, confidence intervals and links to the source conversations. Self-serve plans start at $299 a month with no demo or sales call. The full text of every page listed here is in one file: https://www.dimensionlabs.io/llms-full.txt ## Product - [Customer Conversation Analytics Platform](https://www.dimensionlabs.io/platform): One AI agent that ingests calls, chats, tickets, reviews and surveys, enriches every record with dozens of Dimensions and tests what moves your metrics. - [Pricing: Plans From $299 a Month](https://www.dimensionlabs.io/pricing): Transparent pricing for the Dimension Labs analytics agent. Plans from $299 a month, no demo or contract on monthly plans, and a Week 4 finding guarantee. - [What Are Dimensions? Structured Labels for Text Data](https://www.dimensionlabs.io/dimensions): Dimensions turn unstructured text into typed, comparable fields: intent, effort, churn risk, root cause and hundreds more, written onto every conversation. - [Integrations: Zendesk, Intercom, Salesforce](https://www.dimensionlabs.io/integrations): Connect Zendesk, Intercom, Salesforce, Qualtrics, Medallia, Snowflake and 30+ more, or start with a single CSV. Every source lands in one schema. - [Security: SOC 2 Type II and ISO 27001](https://www.dimensionlabs.io/security): How Dimension Labs protects customer data: SOC 2 Type II, ISO 27001 and GDPR alignment, segregated workspaces, encryption and enterprise deployment options. - [What Is Causal Intelligence?](https://www.dimensionlabs.io/causal-intelligence): Causal intelligence explained: how to move from correlation to cause in customer data, why dashboards miss it, and how to prove which issue moves a metric. ## Use cases - [Customer Experience Analytics](https://www.dimensionlabs.io/solutions/customer-intelligence): Find which experience issue actually moves your CSAT, NPS or star rating. Dimension Labs reads every review, survey and ticket and ranks issues by effect. - [Product Feedback Analytics and Prioritization](https://www.dimensionlabs.io/solutions/product-intelligence): Rank feature requests and product problems by the revenue and churn behind them, not by volume. Every ticket, review and request, structured and joined. - [Churn and Retention Analytics](https://www.dimensionlabs.io/solutions/revenue-retention-intelligence): Find what customers said before they left. Dimension Labs joins conversations to ARR and renewals to prove which signals predict churn and revenue loss. - [Customer Insight Analysis for Agencies](https://www.dimensionlabs.io/solutions/agency-solutions): Code 40,000 client verbatims in an afternoon. Segregated workspaces, client-specific taxonomies and proven findings that turn projects into retainers. - [Contact Center Analytics and Agent Performance](https://www.dimensionlabs.io/solutions/agent-contact-center-intelligence): Score 100% of calls, chats and tickets. Find which agent behaviors and process failures drive repeat contacts, resolution and CSAT, ranked by effect. - [AI Agent and Chatbot Analytics](https://www.dimensionlabs.io/solutions/ai-agent-analytics): See where your bot or AI agent fails, why customers escalate and what each failure costs. True containment, failure modes and automation opportunities. - [Multi-Location Performance Analytics](https://www.dimensionlabs.io/solutions/multi-location-intelligence): Compare every store, hotel, club or restaurant on the same Dimensions and find why one location outperforms another, and which fix moves the rating. - [Review Analytics and Competitive Benchmarking](https://www.dimensionlabs.io/solutions/review-intelligence): Read every public review of you and your competitors on one scale. Find where you lose, which differences move ratings and when the market shifts. ## Industries - [Hotel Guest Experience Analytics](https://www.dimensionlabs.io/industries/hospitality): Find which guest issue actually costs each property stars. Reviews, surveys and stay comments structured and ranked by effect, not by mention volume. - [Restaurant Guest Feedback Analytics](https://www.dimensionlabs.io/industries/restaurants): See whether speed, accuracy, staff or the operator moves each store's rating. Every review and survey across every location, on one scale. - [Gym and Golf Member Experience Analytics](https://www.dimensionlabs.io/industries/fitness-and-recreation): Find which club issue drives member churn. Reviews, NPS comments and cancellation notes joined to tenure, visits and tier, club by club. - [Retail Customer Feedback Analytics](https://www.dimensionlabs.io/industries/retail): Find which issue drives returns, ratings and repeat purchase across stores and e-commerce. Reviews, chats and return reasons on one scale. - [Fan Experience Analytics for Sports Teams](https://www.dimensionlabs.io/industries/sports-and-entertainment): Find which part of game day moves season ticket renewal and attendance. Fan surveys, reviews and app feedback joined to your ticketing data. - [Insurance Customer Experience Analytics](https://www.dimensionlabs.io/industries/insurance): Find which experience drives policy lapse and complaints. Calls, claim notes and complaints joined to policy, premium and claims data. - [Banking Customer Experience Analytics](https://www.dimensionlabs.io/industries/financial-services): Find which fee, process or channel issue drives account attrition, with compliance flags on every complaint, call and review. - [SaaS Customer Feedback and Churn Analytics](https://www.dimensionlabs.io/industries/software-and-saas): Find which signals predict churn and which product gaps cost revenue. Tickets, calls and reviews joined to ARR, usage and renewals. - [Automotive Dealer Customer Experience Analytics](https://www.dimensionlabs.io/industries/automotive): Find which part of buying, delivery and service moves ratings and repeat business, rooftop by rooftop, from every review and survey. - [Consumer Product Review Analytics for CPG Brands](https://www.dimensionlabs.io/industries/consumer-products): Find which defects and experiences move ratings and returns, SKU by SKU, from retailer reviews, care-line contacts and social posts. - [Conversation Analytics for Contact Centers and BPOs](https://www.dimensionlabs.io/industries/contact-centers-and-bpo): Analyze every call, chat and bot conversation across client programs and prove which agent and process changes moved resolution and CSAT. - [Constituent Feedback Analytics for Public Services](https://www.dimensionlabs.io/industries/public-sector-and-nonprofit): Find which process failures block enrollment and service. Calls, chats and surveys in any language, structured and joined to case outcomes. - [Real Estate Lead Conversation Analytics](https://www.dimensionlabs.io/industries/real-estate): Find which signals in lead texts, calls and chats predict a showing and a closing, and which follow-up patterns lose buyers. - [Player Conversation Analytics for iGaming](https://www.dimensionlabs.io/industries/gaming-and-betting): Find which payment, verification and product issues drive churn among your most valuable players, across every market and language. ## Proof - [Customer Stories: NBA, Travelers, Mars & More](https://www.dimensionlabs.io/customer-stories): How the NBA, Travelers, Mars, Concentrix, Intuit and others turn millions of customer conversations into structured, comparable data they act on. - [How the NBA Turns Fan Surveys Into Fan Intelligence](https://www.dimensionlabs.io/customer-stories/nba): How the NBA analyzes 100,000+ open-ended fan survey responses in hours instead of months, cutting insight cycles from about 60 days to under 6 hours. - [How Stake.com Analyzes 1.5M+ Monthly Player Conversations](https://www.dimensionlabs.io/customer-stories/stake): How Stake.com turns 1.5M+ monthly player conversations into structured intelligence that explains why customers contact support, not just the category. - [Concentrix: Chatbot Intelligence for Retail Journeys](https://www.dimensionlabs.io/customer-stories/concentrix): How Concentrix uses Dimension Labs to structure chatbot conversations and improve self-service performance across retail customer journeys. - [How Dropbox Scaled Self-Service With Conversation Data](https://www.dimensionlabs.io/customer-stories/dropbox): How Dropbox turned conversational data into self-service intelligence, cutting unhandled bot conversations by 43% and prioritizing journey fixes. - [How Mars Petcare Built a Meaning Layer for Conversations](https://www.dimensionlabs.io/customer-stories/mars): How Mars Petcare structured chatbot, chat and survey language across regions, replacing days of manual work per region with instant insight. - [Travelers: 600% ROI From a Unified Conversation Layer](https://www.dimensionlabs.io/customer-stories/travelers): How Travelers unified millions of omnichannel customer interactions in one meaning layer, with 600% ROI and $3.13M in savings within 18 months. - [American Red Cross: Real-Time Insight for Disaster Response](https://www.dimensionlabs.io/customer-stories/arc): How the American Red Cross interprets conversations at scale during disasters to direct support to the people who need it most. - [Covered California: Scaling Health Coverage Enrollment](https://www.dimensionlabs.io/customer-stories/covered-california): How Covered California structured enrollment conversations, launched a new self-service use case in 2 weeks and reached nearly 100% containment on key intents. - [Intuit QuickBooks: Chatbot Intelligence at 1M+ Sessions](https://www.dimensionlabs.io/customer-stories/intuit): How Intuit QuickBooks structured 1M+ monthly chatbot sessions, cutting human escalations by 57% and the not-handled intent rate by 35.3%. - [How the Seattle Mariners Analyze Fan Feedback](https://www.dimensionlabs.io/customer-stories/seattle-mariners): How the Seattle Mariners analyze 100% of open-text fan feedback consistently across all 81 home games, turning comments into structured intelligence. - [Ylopo: Conversation Intelligence for Real Estate Leads](https://www.dimensionlabs.io/customer-stories/ylopo): How Ylopo structured real estate lead conversations, saving $250,000+ in resource costs and improving scheduled-call conversion by more than 20%. - [Artifacts Gallery: Reports Built by the Agent](https://www.dimensionlabs.io/artifacts): Browse reports, live monitors, apps and decks the Dimension Labs agent built from public reviews and customer data for brands like Panera, Zendesk and NBA. - [Duolingo Customer Analysis: 502,269 Reviews](https://www.dimensionlabs.io/artifacts/duolingo-customer-analysis): What 502,269 Duolingo app reviews said before the stock fell: a Dimension Labs analysis of churn signals, pricing friction and product sentiment. ## Research - [The Dimensionality of Meaning: Research Preview](https://www.dimensionlabs.io/research/the-dimensionality-of-meaning): Early validation of text dimensionality: how many distinct meanings coexist in one customer conversation, and why single-label analytics misses most of them. - [The Signal Is the Ceiling: Limits of LLM-Predicted Ratings](https://www.dimensionlabs.io/research/the-signal-is-the-ceiling): Part 2: AI annotation reliability exceeds human benchmarks, but LLM-predicted ratings have an upper limit set by the signal in the text itself. - [What Sentiment Analysis Can't See](https://www.dimensionlabs.io/research/what-sentiment-cant-see): Across 70,000 support conversations, predicted satisfaction beats sentiment at measuring whether customers were helped and what went wrong. - [Dimensionality in Satisfaction Ratings](https://www.dimensionlabs.io/research/dimensionality-in-satisfaction-ratings): How LLMs decompose support conversations into satisfaction drivers, revealing lower customer satisfaction than surveys alone suggest. - [LLM Predictive Scoring: Inferring Ratings From Text](https://www.dimensionlabs.io/research/llm-predictive-scoring-and-validation-inferring-experience-ratings-from-unstructured-text): Part 1: LLM annotation recovers self-reported experience ratings from raw text across 10,000 MLB fan surveys. Methods, validation and results. ## Comparisons - [Dimension Labs vs Enterpret, Chattermill, Qualtrics and More](https://www.dimensionlabs.io/compare): An honest comparison of Dimension Labs with Enterpret, Chattermill, Qualtrics, Medallia, Thematic and Unwrap: pricing, setup, method and when to pick each. - [Dimension Labs vs Chattermill: An Honest Comparison](https://www.dimensionlabs.io/compare/chattermill): Dimension Labs vs Chattermill compared on pricing, setup time, analysis method and data sources, including when Chattermill is the better choice for your team. - [Dimension Labs vs Enterpret: An Honest Comparison](https://www.dimensionlabs.io/compare/enterpret): Dimension Labs vs Enterpret compared on pricing, setup time, analysis method and data sources, including when Enterpret is the better choice for your team. - [Dimension Labs vs Medallia: An Honest Comparison](https://www.dimensionlabs.io/compare/medallia): Dimension Labs vs Medallia compared on pricing, setup time, analysis method and data sources, including when Medallia is the better choice for your team. - [Dimension Labs vs Qualtrics: An Honest Comparison](https://www.dimensionlabs.io/compare/qualtrics): Dimension Labs vs Qualtrics compared on pricing, setup time, analysis method and data sources, including when Qualtrics is the better choice for your team. - [Dimension Labs vs Thematic: An Honest Comparison](https://www.dimensionlabs.io/compare/thematic): Dimension Labs vs Thematic compared on pricing, setup time, analysis method and data sources, including when Thematic is the better choice for your team. - [Dimension Labs vs Unwrap: An Honest Comparison](https://www.dimensionlabs.io/compare/unwrap): Dimension Labs vs Unwrap compared on pricing, setup time, analysis method and data sources, including when Unwrap is the better choice for your team. ## Guides - [Predictive Scoring Lessons: Step-by-Step Guide](https://www.dimensionlabs.io/learn/all-lessons): A free lesson series on predictive scoring with LLMs: how it works, which dimensions to score, methods, validation and what it makes possible. - [Why Gym Members Say They Will Cancel: Evidence From 9,186 Reviews](https://www.dimensionlabs.io/blog/why-gym-members-say-they-will-cancel): In 9,186 public reviews of a gym chain, staff problems had the biggest effect on bad ratings, while half of billing complaints said the member was cancelling or warning others. - [Which Complaints Cause Bad Reviews? Hotel, Gym and Café Data](https://www.dimensionlabs.io/blog/which-complaints-cause-bad-reviews): Public reviews of a hotel group, a gym chain and a café chain show the most-mentioned complaint is rarely what drives 1 and 2 star reviews. - [Semantic Clustering Demystified: A Practical Guide](https://www.dimensionlabs.io/blog/semantic-clustering-demystified): What semantic clustering is, how it groups unstructured text by meaning rather than keywords, and how to apply it to customer feedback at scale. - [What Does a Conversation Designer Do?](https://www.dimensionlabs.io/blog/conversation-designer): What a conversation designer does: designing chatbot and voice assistant flows, the skills the role needs, and how to measure whether conversations work. - [What Is Textual Data and Why It Matters to Support Teams](https://www.dimensionlabs.io/blog/textual-data): What textual data is, why support tickets and chats are full of it, and how to turn unstructured customer text into metrics your team can act on. - [What Is a Conversational Agent? Types and Examples](https://www.dimensionlabs.io/blog/conversational-agent): A conversational agent is a dialogue system that understands and responds in natural language. How they work, the main types and how to measure them. - [Chatbot Analytics: 13 Metrics Every Business Should Track](https://www.dimensionlabs.io/blog/chatbot-analytics-to-improve-chatbot-performance): The 13 chatbot analytics metrics that matter, from containment and fallback rate to CSAT and goal completion, and how to use them to improve your bot. - [A Guide to Unstructured Data Management](https://www.dimensionlabs.io/blog/unstructured-data-management): How to manage unstructured data: collecting, storing, governing and analyzing text, audio and documents so they become usable alongside structured data. - [The Ultimate Guide to Unstructured Data Analytics](https://www.dimensionlabs.io/blog/unstructured-data-analytics): What unstructured data analytics is, the methods and tools behind it, and how to turn text, calls and reviews into structured insight your team trusts. - [Predictive Churn: Models, Data and the Missing Why](https://www.dimensionlabs.io/blog/predictive-churn): How predictive churn models work, which data they need, and why adding what customers actually said explains the risk scores your CRM produces. - [How to Build a Customer Experience Dashboard in 4 Steps](https://www.dimensionlabs.io/blog/customer-experience-dashboard): Build a customer experience dashboard in four steps: pick the metrics, structure your support data, track sentiment over time and act on what changes. - [How Knowledge Graphs and Agentic Workflows Use Unstructured Data](https://www.dimensionlabs.io/blog/how-knowledge-graphs-and-agentic-workflows): How knowledge graphs and agentic workflows give unstructured data context, and why they are reshaping analytics beyond rows, columns and dashboards. - [The Analytics Stack in 2026: Layers, Tools and Trends](https://www.dimensionlabs.io/blog/analytics-stack): The modern analytics stack in 2026: every layer from ingestion to activation, the leading tools, current trends and the unstructured data gap most stacks miss. - [Conversation Analytics: What It Is and Why It Matters](https://www.dimensionlabs.io/blog/conversation-analytics): Conversation analytics uses AI and NLP to analyze customer calls, chats and messages. What it measures, how it works and how teams use it to improve CX. - [The Meaning Layer for Unstructured Data](https://www.dimensionlabs.io/blog/the-meaning-layer-for-unstructured-data): Why the next decade of data belongs upstream: a meaning layer that structures unstructured text before it reaches the warehouse, BI and AI tools. - [Top 10 Text Analytics Tools for Unstructured Data](https://www.dimensionlabs.io/blog/top-10-text-analytics-tools-to-unlock-insights-from-unstructured-data): The top 10 text analytics tools for unstructured data, compared on features, strengths and use cases, to help you pick the right one for customer feedback. - [LLM Analytics: How to Improve AI Accuracy and Performance](https://www.dimensionlabs.io/blog/how-llm-analytics-help-improve-ai): How LLM analytics measures and improves AI accuracy and performance in customer-facing apps: what to track, common failure modes and how to fix them. - [What Is Intercom? Customer Engagement Through Chat](https://www.dimensionlabs.io/blog/what-is-intercom-empowering-customer-engagement-through-chat-systems): What Intercom is, how its chat, bots and help desk features work, and how to analyze Intercom conversations to improve customer engagement. - [The Missing Layer in the Modern Data Stack: Unstructured Data](https://www.dimensionlabs.io/blog/the-missing-layer-in-the-modern-data-stack-unstructured-data): Most enterprise data is unstructured, yet the modern data stack was built for rows and columns. What a missing unstructured data layer looks like. - [Data Taxonomy: The Power of Unstructured Text Analytics](https://www.dimensionlabs.io/blog/data-taxonomy-unveiling-the-power-of-unstructured-text-analytics): What a data taxonomy is, how to design one for unstructured text, and how it makes customer feedback consistent, comparable and easy to analyze. - [Chatbots vs Conversational AI: A Complete Guide](https://www.dimensionlabs.io/blog/chatbots-vs-conversational-ai-a-complete-guide): Chatbots vs conversational AI explained: how rule-based bots differ from AI agents, where each fits and how to measure which one serves customers better. - [What Is Customer Observability?](https://www.dimensionlabs.io/blog/customer-observability): Customer observability applies monitoring principles to every customer interaction, so teams see what customers experience and why metrics change. ## Docs - [Documentation](https://docs.dimensionlabs.io/docs/): product and API documentation --- # Full text of the pages listed above ## Customer Conversation Analytics Platform | Dimension Labs URL: https://www.dimensionlabs.io/platform Dimension Labs Platform # The AI agent for customer conversations Know why your metrics move, using the conversations your analytics can’t touch. Start now › Watch it work › 01 · Ask Ask in plain English Type the question you actually have. The agent writes the queries, runs them and answers with a chart, not a paragraph. 02 · Report Get a report and a deck Say “turn this into a report for leadership.” It builds the report and the slide deck, checks every figure and saves them to your project. 03 · Blend Every channel, one answer Support tickets, surveys, reviews and calls are read, structured and joined to revenue before you ask anything. Dimension Labs · Project: Zendesk Revenue Intelligence ### Built for speed Weeks of analyst work from one question. Answers in minutes, not a ticket queue. ### Built for automation Stop staring at charts hoping a pattern shows up. Ask why and get evidence you can act on. ### Built for scale No more sampling a fraction of your conversations. Get 100% coverage, automatically. 1 · Connect ## Any source. Any channel. Live or historic. Connected in minutes, not months. Upload a file or connect Zendesk, Intercom, Salesforce, Qualtrics, Snowflake and more. All integrations Zendesk Support tickets 1,840 rows Connected Salesforce Accounts and renewals 290 rows Connected Qualtrics NPS survey 140 rows Connected Medallia CSAT survey 180 rows Connected Snowflake Orders and refunds 2.4M rows Connected CSV upload Google reviews 9,186 rows Connected 2 · Enrich ## Build the Meaning Layer with Dimensions on every record. Churn risk, buyer intent, predicted CSAT, root cause and more. A human-in-the-loop process keeps every field honest. Browse example Dimensions Software Hospitality Retail Sports Insurance AI agents enrichment · one record Raw text We're running a 60-day parallel evaluation of another vendor before we sign the renewal . Honestly, we were promised SSO in Q2 and it still isn't here , and this ticket has been open for three weeks . Support ticket · enterprise account · ARR joined Dimensions written to the record - Competitor Mentioned yes · evaluation underway - Churn & Cancellation Risk high - Lifecycle Stage renewal - Urgency & Deadline 60 days - Expectation Gap roadmap promise missed - Feature Request SSO - Resolution Status open · 3 weeks Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. 3 · Understand ## Ask why. Get causal answers, charts and evidence-backed reports on demand. Agent-aided analysis that writes the queries, tests the drivers and ships the report. Every claim links back to the transcripts behind it. How the causal analysis works The agent on 80 enterprise accounts 0:53 The enrichment pipeline ## Enrichment at scale. Every day. Parallel workers read every record and write Dimensions with the same schema on every run, so this month compares cleanly to last year. Results go to the agent, your warehouse and your BI tools. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon Coming soon ## Your Dimensions, inside Claude and ChatGPT. We’re building MCP servers for the major AI assistants. Ask in the assistant your team already uses and get answers built on the same enrichment, governed data and causal tests. Your AI assistant · preview In development Which customer signals actually predicted churn last year? Use our Dimension Labs data. tool call dimension_labs.find_drivers { "outcome": "churned", "population": "enterprise accounts", "controls": ["tier", "tenure", "ticket_volume"] } returned · governed Dimensions, tested against the outcome - alt_vendor_evaluation 6.5× p<0.001 - promise_not_delivered 4.7× p<0.001 - repeat_issue 2.6× significant Accounts that wrote “we’re evaluating alternatives” were 6.5 times more likely to leave. It’s the earliest warning in your data: for most of the accounts that left, support heard it before any survey did. What you get ## The layer your stack never had. ### Unified Meaning Layer Every channel normalized into one data model. One source of truth for what your customers are saying. ### Per-record Dimension extraction Every record enriched with structured Dimensions. Intent, sentiment, root cause, risk. At scale. No sampling. ### Business-native context Models calibrated to your industry, your taxonomy and your business logic. You define the meaning. We extract it. ### Full explainability and lineage Every insight traces back to the source record, the extraction logic and the model version. If you can’t explain it, you can’t govern it. ### Versioning and governance Meaning evolves as your business does. Full version control, change history and audit trails, built for compliance from day one. ### Built for agents and knowledge graphs Agents can’t reason over raw text. Graphs can’t build from noise. Both need structured, governed meaning at scale. Enterprise grade security ## Built to handle Fortune 500 customer data. With the controls enterprise security teams ask for, on every plan. Security at Dimension Labs - SOC 2 Type II Audited controls over security, availability and confidentiality. - ISO 27001 Certified information security management. - GDPR and CCPA Compliant, with data processing agreements for customers. - PII redaction Personal data redacted at ingest, before enrichment. - Single or multi-tenant Cloud hosted, with single-tenant and on-premise options for enterprise. - SSO and roles Single sign-on and role-based permissions, plus full data deletion on request. ## FAQ What does Dimension Labs actually do? You sit on millions of customer conversations you can’t use for decisions. We join that language to CRM, usage and financial data, then an AI agent answers why metrics moved and writes the report. Why can’t I just use ChatGPT for this? ChatGPT can talk about a file you upload. It can’t enrich hundreds of thousands of conversations, join what people said to whether they churned, or ship a sourced executive report. We build the Meaning Layer first, then the agent runs on it. How accurate are the insights? Every insight is traceable back to the exact conversations that produced it. We extract structured fields from every record through validated pipelines, with classification accuracy exceeding 99%. When a report says churn risk spiked in a segment, you can click through to the verbatims and verify it yourself. One customer described being "burned by ChatGPT" before switching to us. They stayed because every claim we surface can be sourced and validated. What kinds of questions can Dimension Labs actually answer? Questions your current tools can’t. Which conversation signals predict churn before usage metrics decline. What themes in support tickets correlate with renewal outcomes. Which product complaints are statistically significant and tied to real revenue impact. What changed since last month and why. We recently generated a full executive-level analysis for a professional sports franchise, correlating fan feedback with satisfaction scores, identifying the exact drivers of low experience, and delivering board-ready recommendations, all from a single question to our AI agent. What data sources do you support? Any text-based conversational data: call transcripts, live chat and chatbot logs, support tickets, open-ended survey responses, CRM notes and emails. We also ingest structured data like CRM records, product usage, financial data and satisfaction scores so you can correlate what customers say with what they actually do. How is this different from the analytics tools we already have? Your current tools can tell you sentiment is down or ticket volume is up. They can’t tell you why, which customers are affected, or what to do about it. Dimension Labs enriches every individual conversation, connects patterns across your entire dataset, joins them with your structured business data, and lets anyone ask a question in plain English and get an answer backed by evidence. One enterprise customer spent six months trying to build this internally with their data team and couldn’t operationalize a single repeatable pipeline. They were live with us in two weeks. Do we need to replace our BI stack? No. You can use Dimension Labs standalone with built-in querying, auto-generated charts, dashboards and full executive reports. Or plug structured output into Snowflake, Databricks or BigQuery so your data team can query it alongside everything else they already use. We’re not replacing your stack. We’re giving it a data source it’s never had access to. How long does implementation take? Most customers are live within one to two weeks. Enrichment begins immediately once your data is connected. Customers routinely tell us what used to take a research team weeks now takes a single question to our AI agent. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Pricing: Plans From $299 a Month | Dimension Labs URL: https://www.dimensionlabs.io/pricing Plans from $299 a month Pricing # Start now. Scale when it proves out. Plans from $299 a month. No demo, no sales call, no contract on monthly plans. Upgrade when it proves out. - No demo or sales call - No contract on monthly plans - Week 4 finding guarantee - Unlimited reports on every plan - SOC 2 Type II · ISO 27001 · GDPR Not sure which plan? Estimate your ROI ↓ Matches your volume ## Explore $299 /month Billed monthly · about $0.30 a record Start with Explore › Upload a file, ask why a number moved, and get the answer in minutes. For one team putting the agent to work. Includes - 1,000 records a month - 10K enrichments a month - Unlimited agent reports - Pre-built Dimensions library - 25K rows of context, free Matches your volume ## Discover Week 4 guarantee $999 /month Billed monthly · about $0.20 a record Start with Discover › Custom Dimensions for your business. A causal finding from your data by Week 4, or Month 1 back. Everything in Explore, plus - 5,000 records a month - 50K enrichments a month - Custom Dimensions - 100K context rows The full product Matches your volume ## Prove Week 4 guarantee $2,999 /month Billed monthly · about $0.12 a record Start with Prove › Where the full product lives. Join what customers said to your CRM and revenue data, and prove what moves your numbers with statistical evidence. Everything in Discover, plus - 25,000 records a month - The Causal Join: up to 3 sources - Causal findings with confidence intervals - Snowflake, Databricks and BigQuery export - 250K enrichments, 500K context rows Matches your volume ## Scale $6,999 /month Billed monthly · about $0.07 a record Start with Scale › When one use case proves the value and you’re ready to run it across the organization. Everything in Prove, plus - 100,000 records a month - Unlimited data sources - Subgroup analysis: who is most affected and why - 1M enrichments a month - Priority support SLA Enterprise · Unlimited · From $150,000 a year ## For Fortune 500 and large enterprise. Dedicated CSM, custom causal methodology, on-premise deployment, HIPAA, GDPR and CCPA alignment. Talk to sales › We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The first month What happens after you click 4 Weeks ## A finding by Week 4. Or your first month back. On Discover and Prove, you get one specific causal finding from your own data, with statistical evidence, by the end of Week 4. If you don't, we refund Month 1 in full. No questions asked. - 1 Day 1 Upload a file Drop in a CSV of tickets, reviews or surveys, or connect a source. The agent reads every record and answers your first question with a chart. - 2 Week 1 Your Dimensions Add custom Dimensions in plain English. Month one includes 3x your limits, so you can load history. - 3 Week 4 A causal finding On Discover and Prove, one specific causal finding from your own data, with statistical evidence. Or Month 1 back. - 4 Ongoing On schedule Reports rerun on fresh data and land in your inbox, or export to your warehouse and BI tools. [02] ROI calculator Your numbers, your assumptions ## What it’s worth to you. Move the sliders. Estimate the analyst time the agent takes over, and the revenue a better retention decision protects. We recommend a plan from your volume. Customer conversations a month 500 200K Team hours a month spent reading, tagging and reporting on feedback Loaded cost of an analyst hour Share of that work the agent takes over Include revenue you could retain Annual recurring revenue Annual churn rate Share of churning revenue you save by fixing the real cause Estimated annual value $0 Analyst time $0 Retained revenue $0 Plan cost / year $0 0x Return on plan cost 0 Payback Recommended for your volume Prove Start with Prove › How we calculate: analyst time = hours × hourly cost × share taken over × 12. Retained revenue = ARR × churn rate × share saved. Plan cost is 12 months of the monthly price for the smallest plan that covers your monthly conversations. Estimates from your inputs, not a quote. For reference, Travelers reported 600% ROI within 18 months. [03] In their words 16 from customer stories ## Teams that stopped guessing. In their own words. 600% ROI within 18 months, and $3.13M in total savings. “Dimension Labs gives us that 50,000-foot view we needed to figure out what’s going on out there.” Data Science Business Partner, Travelers “You’re going in blind without Dimension Labs.” Covered California CX Manager, Covered California 60 days → 6 hrs Insight review cycles cut from about 60 days to under 6 hours. “What used to take days of manual work per region is now available instantly.” Customer Analytics Leader, Mars Petcare “Dimension Labs has become the default insight layer across the company.” CX Manager, Stake.com 57% Fewer human escalations, and a 35.3% drop in the not-handled intent rate. “Dimension Labs is key for us to both report outcomes to the business and prioritize where to dedicate resources to enhancing our customer journey.” Maureen O’Sullivan, Program Manager, Dropbox $250K+ Saved in resource costs through workflow automation, and over 20% better scheduled-call conversion. Ylopo “When disaster strikes, we have to quickly interpret conversations at scale and adjust to new requests and topics. Dimension Labs gives us the insights to provide the support needed to those who need it most.” Hailey Burgess, Product Manager, American Red Cross “We can finally understand why customers are contacting us, not just what category their issue falls into.” Stake.com 43% Fewer unhandled bot conversations. “The partnership with Dimension Labs has been instrumental in supporting our improvements in self-service performance and customer journey optimization.” Jenny Burr, Speech Science & Analytics, Concentrix “Dimension Labs helped us move from static reporting to dynamic fan intelligence.” NBA Insights Leader 2 weeks To launch a new self-service use case, with nearly 100% containment on key intents. Covered California “Dimension Labs has enabled us to move beyond anecdotal feedback and into measurable, operational insight.” CX & Insights Executive, Mars Petcare Read the customer stories › [04] Compare plans Everything in each plan Explore $299/mo Discover $999/mo Prove $2,999/mo Scale $6,999/mo Enterprise From $150K/yr Records per month 1,000 5,000 25,000 100,000 Unlimited Enrichments included 10,000 50,000 250,000 1M Custom Extra enrichments $0.040 each $0.025 each $0.015 each $0.010 each Negotiated Context data (rows stored) 25K 100K 500K 2M Custom AI agent and unlimited reports Custom Dimensions – Week 4 finding guarantee – – – The Causal Join – – Up to 3 sources Unlimited Unlimited Causal findings with confidence intervals – – Subgroup causal analysis – – – Warehouse export – – Support Community Email Email and chat Priority SLA Dedicated CSM SOC 2 Type II, ISO 27001, GDPR HIPAA alignment and on-premise option – – – – Explore Discover Prove Scale Talk to sales ## Common questions What exactly is a record? A record is one discrete customer interaction: a ticket, chat, call transcript, email thread, survey response or review. Rows that share a session ID count as one. Files you add only as context, like a CRM export or store list, are free up to your plan’s limit: 25K rows on Explore, 100K on Discover, 500K on Prove, 2M on Scale. What is the Causal Join and why is it behind Prove? The Causal Join connects what customers said to your business metrics: who churned, what revenue moved, which contracts renewed. It needs at least two data sources, and it is what makes a finding causal rather than descriptive. It is how Travelers found $3.13M in savings. It is available on Prove and above. What happens if I go over my monthly record limit? Over your conversation limit, you move to the next plan, prorated. Extra enrichments (one Dimension on one conversation) cost $0.04, $0.025, $0.015 or $0.01 on Explore, Discover, Prove and Scale. You get an alert at 80%, and we never stop your data. Month one includes 3x your limits to load history. What is the Week 4 deliverable guarantee? On Discover and Prove, you get one specific causal finding from your own data, with statistical evidence, by the end of Week 4. If you don’t, we refund Month 1 in full. No questions asked. Reports are unlimited on every plan. Do you offer free trials or POCs? Explore starts at $299/month with no contract on monthly plans, so you can put it to work on real data right away. On Discover and Prove, the Week 4 guarantee replaces the traditional POC. Enterprise deals over $100K can run a paid POC at $15,000 to $25,000 against signed success criteria. Does this replace our Snowflake or BI tools? No. We are the missing layer between your raw conversation data and the warehouse you already have. Dimension Labs enriches every conversation and pushes the output to Snowflake, Databricks or BigQuery, where your BI tools (Tableau, Looker, Power BI) query it alongside everything else. We add to your stack. We don’t replace it. Is our data secure? Dimension Labs is SOC 2 Type II and ISO 27001 certified and GDPR aligned. Enterprise adds HIPAA alignment and an on-premise option. See the security page for details. Dimension Labs / Start with one file From $299 a month ## Put the agent to work on your data today. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › Prove $2,999/month Start now › --- ## What Are Dimensions? Structured Labels for Text Data URL: https://www.dimensionlabs.io/dimensions Dimension Labs Text dimensionality # Structure for the 80% of data that has none. Structured data has shape: rows, columns, schemas. Calls, chats, tickets and surveys have none, no consistent fields to filter, trend or build metrics on. Dimensions are the stable attributes inside that text, written onto every record as typed fields. Start now › See them in artifacts › ### Why BI tools can't analyze unstructured data There is nothing to group by. A transcript is one long string. Until each one carries the same set of fields, you can only read samples of it. ### How we help We automate the extraction pipeline: no hand-tagging, no prompt engineering per project. Dimensions aren't fixed. Adjust the logic as needs evolve and the change propagates consistently across your full dataset. ### How Dimensions are used Dimensions are the schema layer for unstructured data. Once text has them, they behave like any structured field: queryable, filterable, joinable. That is what makes reports, dashboards and monitors possible on raw conversations. ## Example Dimensions A sample of the Dimensions we build, across 11 categories and ready on every plan. We create hundreds more, and custom Dimensions are written in plain English. All Sentiment & Emotion Intent & Journey Experience & Operations Product & Features Pricing & Value Revenue & Growth Risk, Trust & Compliance Competitive & Market People & Segmentation AI & Automation Verbatim & Summaries Emotional Intensity Sentiment & Emotion How strongly the person feels, independent of whether it’s positive or negative, surfaces your highest-passion and highest-risk voices. Grievance Severity Sentiment & Emotion The stakes behind a complaint, from mild annoyance to serious harm, as framed by the customer. Overall Sentiment Sentiment & Emotion The true feeling expressed in the words themselves, extracted from the prose, not the star rating or survey score. Predicted CSAT Sentiment & Emotion A modeled 1–5 satisfaction score inferred purely from the qualitative text of an interaction. Predicted NPS Band Sentiment & Emotion The likely promoter / passive / detractor classification read from language, even when no score was given. Customer Intent Intent & Journey The primary reason the person reached out or showed up. Effort Level Intent & Journey How hard the customer had to work to get value or resolution. First-Time vs. Repeat Intent & Journey Whether this is a new or returning customer, read from the narrative rather than a CRM flag. Interaction Type Intent & Journey Whether the exchange is a complaint, question, praise, cancellation, or sales inquiry. Lifecycle Stage Intent & Journey Where the person sits in their journey with you, prospect, new, onboarding, active, at-risk, or churned. Purchase Motivation Intent & Journey The dominant reason the customer bought or visited. Resolution Status Intent & Journey Where the issue stands by the end of the interaction, resolved, pending, or unresolved. Transaction / Visit Type Intent & Journey The kind of transaction the text is about, sales vs. service, purchase vs. return, dine-in vs. delivery. Use Case Intent & Journey The specific business or personal use case the person references. Accessibility & Courtesy Experience & Operations How well specific needs were accommodated and the courtesy shown. Atmosphere & Ambiance Experience & Operations The energy, mood, and sensory experience of the venue. Core Quality Verdict Experience & Operations The customer’s assessment of the core product or offering, food, course, device reliability. Digital & App Experience Experience & Operations Experience with the app, online ordering, or self-service surfaces not visible in structured data. Facility & Cleanliness Experience & Operations The physical condition and cleanliness of the space. Order & Service Accuracy Experience & Operations Whether the order or request was fulfilled correctly. Primary Friction Experience & Operations The single most operationally important thing that went wrong. Primary Topic Experience & Operations The single dominant subject of the conversation. Service Recovery Experience & Operations What happened when the customer raised an issue or asked for help. Service Speed & Wait Experience & Operations What the customer says about timeliness and wait time. Staff Interaction Quality Experience & Operations The character of the human or service interaction, from exceptional to hostile. Engagement Mechanic Response Product & Features How the customer reacts to gamification and engagement features, streaks, rewards, notifications. Feature Discussed Product & Features The specific feature or capability the person engaged with. Feature Request Product & Features A specific improvement, fix, or missing capability the customer asks for. New-Launch Feedback Product & Features Early signal on a newly launched or updated product, critical for catching launch-quality issues fast. Product / Module Referenced Product & Features Which product, model, brand, or module the text is about. Product vs. Service Attribution Product & Features Whether dissatisfaction stems from the product itself or from how it was serviced. Reliability & Defect Type Product & Features The specific hardware or software failure, and where in the lifecycle it occurred. Billing Dispute Pricing & Value The specific billing or charge dispute described. Dollar Amount Referenced Pricing & Value Exact monetary amounts named in the text, disputes, fees, promises, refunds. Fee & Premium Change Pricing & Value The direction and size of a price, fee, or premium change the customer calls out. Price–Value Perception Pricing & Value How the customer frames what they got for what they paid. Pricing & Paywall Friction Pricing & Value Reaction to subscriptions, paywalls, ads, and monetization changes. ROI & Value Realization Pricing & Value The concrete business outcome the customer credits to your product. Account Health Score Revenue & Growth A composite health read inferred from all the signals in a message. Buying Signal Revenue & Growth Positive purchase intent and where the deal sits in the sales process. Churn & Cancellation Risk Revenue & Growth Language suggesting the customer may leave, not renew, or switch away. Expansion & Adoption Signal Revenue & Growth Language pointing to more seats, new use cases, upsell, or deeper adoption. Loyalty Signal Revenue & Growth An explicit loyalty-program or brand-loyalty stance, the retention stake. Objection Revenue & Growth The concern or hesitation blocking the deal. Recommendation Signal Revenue & Growth Whether the person would recommend you, or actively warns others off. Return & Refund Signal Revenue & Growth Whether the customer returned, refunded, or is considering it. Actionability Tier Risk, Trust & Compliance The operational owner who could act on the feedback, the routing tag that turns voice into a task. Compliance Flag Risk, Trust & Compliance Language or situations that should be routed for compliance review. Data & Account Loss Risk, Trust & Compliance Reports of lost data, progress, or access. Escalation Risk Risk, Trust & Compliance Risk of escalation to a supervisor, social media, legal action, or a regulator. Expectation Gap Risk, Trust & Compliance The distance between what the customer was promised or expected and what they actually got. Regulatory & Legal Exposure Risk, Trust & Compliance Mentions of lawyers, regulators, complaints bodies or rights that could create legal or regulatory exposure. Safety-Critical Event Risk, Trust & Compliance Reports of injury, hazard, contamination or anything else that puts a person’s safety at risk. Trust & Honesty Signal Risk, Trust & Compliance Whether the customer feels they were told the truth, or believes they were misled, ignored or deceived. Urgency & Deadline Risk, Trust & Compliance How time-sensitive the request is, including any date, renewal or deadline the customer names. Competitive Context Competitive & Market How the customer positions you against the alternatives they are weighing or already use. Competitor Feature Comparison Competitive & Market A specific capability the customer says a competitor does better, worse or differently. Competitor Mentioned Competitive & Market Which competitor the customer names, and whether they are evaluating, switching to or leaving it. Incumbent / Current Setup Competitive & Market The tools, vendors or process the customer uses today, and what they would replace. Market Condition Reference Competitive & Market Outside forces the customer cites, such as the economy, budgets, seasonality or industry change. Customer Segment People & Segmentation The type of customer speaking, such as consumer, small business or enterprise, read from what they say. Group / Companion Type People & Segmentation Who the customer was with, such as family, kids, a partner, colleagues or a large group. Location Referenced People & Segmentation The store, site, city or region the customer is talking about. Named Employee People & Segmentation Staff members the customer names, and whether the mention is praise or a complaint. Persona / Role People & Segmentation The customer’s role or job, such as owner, admin, buyer or end user, as they describe it. Purchase Channel People & Segmentation Where the customer bought or engaged, such as in store, online, in app, by phone or through a partner. AI Appropriateness Sentiment AI & Automation How the customer feels about being served by AI at all, from welcome to unacceptable. AI Failure Mode AI & Automation How an AI assistant went wrong, such as misunderstanding, a wrong answer, a loop or a dead end. AI vs. Human Quality AI & Automation How the customer compares the AI’s help with help from a person. Automation Opportunity AI & Automation Requests that are routine enough to be handled by self-service or automation. Bot Containment AI & Automation Whether the bot resolved the request on its own or the customer had to go elsewhere. Human-Escalation Attempt AI & Automation Each time the customer asks for a person, and whether they got one. Self-Service Attempt AI & Automation What the customer tried on their own before reaching out, such as help articles, the app or the bot. Delight Phrase Verbatim & Summaries The exact words that capture what the customer loved, ready to quote. Key Quote Verbatim & Summaries The single sentence that best captures the conversation, kept word for word. Pain Point Phrase Verbatim & Summaries The exact words that capture what went wrong, ready to quote. Sentiment Verbatim Verbatim & Summaries The passage that carries the strongest feeling, kept word for word as evidence for the score. Situation Summary Verbatim & Summaries A short, plain summary of what happened and where it stands. ### What we're learning about language Five published papers on how reliably AI recovers what people meant and rated. See our research ### Dimensions at work Every artifact in the gallery was built on Dimensions, from Panera store diagnostics to NBA fan intelligence. Check out artifacts Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Integrations: Zendesk, Intercom, Salesforce | Dimension Labs URL: https://www.dimensionlabs.io/integrations Integrations # Start with one file. Connect the rest when you're ready. Bring in conversations from support, surveys, contact centers and bots, plus the business data that says what happened next. Ada and Intercom connect in one click. Everything else by API, scheduled file or upload. Start now › Read the docs › ## Support and CX Zendesk tickets, comments, CSAT ratings and requester fields Intercom conversations, Fin and bot handoffs, tags and ratings One click Salesforce cases, accounts, opportunities and renewal dates LiveChat chat transcripts, agent assignments and ratings Drift conversations, playbook paths and lead fields Slack shared-channel and community conversations ## Surveys and feedback Qualtrics open-ended responses, NPS, CSAT and embedded data Medallia survey verbatims, scores and location hierarchy SurveyMonkey open-text answers and scored questions QuestionPro open-ended responses and score questions ## Contact center Genesys call transcripts, queues, dispositions and handle time Amazon Connect contact transcripts, contact flows and attributes Avaya Cloud call and chat interactions with routing data 8x8 call transcripts and contact-center interaction data Twilio SMS and voice conversations SMS two-way text conversations ## Chatbots and voice agents Ada bot conversations, answers served and handoffs One click Kore.ai virtual assistant conversations and intents Yellow.ai bot conversations, intents and handoffs Cognigy voice and chat agent conversations Rasa assistant conversations, intents and actions Microsoft Bot Framework bot activity and conversation logs Amazon Lex bot sessions, intents and slots Amazon Alexa skill sessions and utterances Oracle Digital Assistant assistant conversations and skills Botcopy web chat assistant conversations Capacity support automation conversations Facebook Messenger Messenger conversations with your brand Kik bot conversations on Kik X (Twitter) direct messages and replies to your brand OpenAI assistant and GPT conversation logs ## Data warehouse and files Snowflake read business tables in, write Dimensions back Databricks read business tables in, write Dimensions back BigQuery read business tables in, write Dimensions back Amazon S3 scheduled file drops CSV and Excel upload any export, reviews, verbatims or transcripts REST API send any conversation from your own systems Dimension Labs / Start with one file From $299 a month ## Upload a file. Ask why. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Security: SOC 2 Type II and ISO 27001 | Dimension Labs URL: https://www.dimensionlabs.io/security Security # Built to handle Fortune 500 customer data. Insurers, sports leagues and global consumer brands trust Dimension Labs with millions of customer conversations. Here is how we protect them. Talk to us › Enterprise grade security ## Built to handle Fortune 500 customer data. With the controls enterprise security teams ask for, on every plan. Security at Dimension Labs - SOC 2 Type II Audited controls over security, availability and confidentiality. - ISO 27001 Certified information security management. - GDPR and CCPA Compliant, with data processing agreements for customers. - PII redaction Personal data redacted at ingest, before enrichment. - Single or multi-tenant Cloud hosted, with single-tenant and on-premise options for enterprise. - SSO and roles Single sign-on and role-based permissions, plus full data deletion on request. ## Security questions Which certifications do you hold? SOC 2 Type II and ISO 27001. We are GDPR and CCPA compliant and sign data processing agreements with customers. Where is my data hosted? In the cloud, in multi-tenant or single-tenant deployments. Enterprise customers can choose an on-premise deployment. How is personal data handled? Personally identifiable information is redacted at ingest, before enrichment. You can request full deletion of your data at any time. Can results go back to our own warehouse? Yes. Enriched data and Dimensions can be written back to Snowflake, Databricks or BigQuery, where your existing access controls apply. Do you support SSO and permissions? Yes. Single sign-on and role-based permissions are supported, with webhooks and API connections for ingestion. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## What Is Causal Intelligence? | Dimension Labs URL: https://www.dimensionlabs.io/causal-intelligence Research # What is Causal Intelligence? Every company tracks metrics. Revenue. Churn. NPS. CSAT. Retention by cohort.And every quarter, someone in the room asks: "But why?"Why did mid-market churn spike 18%? Why did NPS drop in enterprise? Why are partner-chan… Dimension Labs Research 6 min read ### The Question No One Can Answer Every company tracks metrics. Revenue. Churn. NPS. CSAT. Retention by cohort. And every quarter, someone in the room asks: "But why?" Why did mid-market churn spike 18%? Why did NPS drop in enterprise? Why are partner-channel customers retaining at half the rate of direct? The data team pulls up the dashboard. The numbers are there. The trend is clear. But the dashboard stops at what. It was never built to answer why . So the room does what it always does. Someone reads 50 tickets. Someone asks CX for a "sentiment summary." Someone puts three anecdotes on a slide and calls it root cause analysis. This is how most companies make their most important decisions: on a sample of a sample, filtered through interpretation, disconnected from the financial data that quantifies impact. There is a name for the intelligence that is missing. We call it Causal Intelligence. ### The Hierarchy of Business Intelligence Not all intelligence is equal. Most of what companies have today sits at the bottom of a hierarchy they do not realize exists. Descriptive. What happened? Revenue declined 12%. NPS dropped from 47 to 39. This is what dashboards provide. Necessary. Not sufficient. Diagnostic. Where did it happen? Which segments, cohorts, geographies? This narrows the search. It still does not identify the cause. Predictive. What will happen next? Based on usage patterns, this account has a 73% probability of churning. Useful, but opaque. You know that the customer will leave. You do not know why , which means you do not know what to do about it. Causal. Why did it happen, and can you prove it? Customers who experience repeat transfers in support churn at 3.1x the baseline rate. This pattern is concentrated in mid-market, affects 34 active accounts representing $4.7M in ARR, and is statistically significant at p < 0.01 after controlling for plan type and tenure. That is a fundamentally different category of answer. Specific, quantified, actionable. Until now, nearly impossible to produce at scale. The computer scientist Judea Pearl formalized a related framework called the Ladder of Causation , which describes three levels of causal reasoning. Level 1 is association: observing patterns. Level 2 is intervention: predicting what happens if you take an action. Level 3 is counterfactual: reasoning about what would have happened under different circumstances. Most enterprise analytics operates entirely on Level 1. Causal Intelligence operates on Levels 2 and 3. ### Why Causation Is So Hard The reason companies are stuck at the descriptive level is structural, not intellectual. Causal answers require two types of data working together: structured data (the metrics: revenue, churn, usage, NPS) and unstructured data (the explanations: what customers actually said in conversations, tickets, surveys, and emails). Structured data tells you the score. Unstructured data tells you the story behind the score. You need both to establish causation. These two datasets have never been connected. They live in different systems, different formats, different teams. Your CRM knows the customer churned on March 3rd. The support transcript from February 12th, where they said "I've called three times, keep getting transferred, and I'm evaluating your competitor," is sitting in Zendesk. Invisible to every analytics system in your company. Traditional analytics operates entirely on the structured side. Traditional NLP operates entirely on the unstructured side. Neither crosses the gap. The gap is where causation lives. ### What Makes Intelligence Causal Causal Intelligence requires three things that have never existed together in a single system. 1. Per-record structural enrichment. Every conversation, not a sample, must be transformed from raw text into multiple structured fields. We call these fields Dimensions. These are not tags. They are typed, queryable variables: str , float , enum . They populate columns in a schema. You can filter them, group by them, run SQL against them, and join them with any table in your warehouse. This is what turns a transcript into evidence. 2. The structured-unstructured join. Enriched conversation data must be joined with the customer's structured business data. This is the step that makes everything causal. When you know that the customer who said "I'm evaluating your competitor" is a $340K ARR account with a contract renewing in 47 days, that is not a sentiment score. That is a revenue-risk finding with a dollar amount and a deadline. When you can see that customers who mention "repeat transfers" in the first 60 days churn at 2.8x the baseline rate (n=412, p<0.01), that is not a topic trend. That is a causal mechanism with statistical proof. The join is what separates "customers are frustrated" from "this specific frustration pattern is costing us $4.7M in annual revenue, concentrated in 34 accounts, fixable with a specific operational change." 3. Statistical rigor. Not every pattern is causal. Not every correlation survives scrutiny. We apply the same methods used in econometrics and epidemiology to extract causal estimates from observational data. Backdoor adjustment. Given a causal DAG, we identify confounders and adjust for them. If contract_tenure influences both support contact likelihood and churn probability, we control for it to isolate the causal effect of support experience. Average Treatment Effect (ATE). We estimate the average causal effect of a Dimension on an outcome across the population. "Customers who experience root_cause: repeat_transfer churn at 2.8x the baseline (ATE = +0.23, 95% CI [0.18, 0.28], p < 0.01), after adjusting for plan type, tenure, and segment." Heterogeneous treatment effects. Using methods like EconML's LinearDML with gradient boosting, we estimate conditional average treatment effects (CATE): "The causal impact of repeat transfers on churn is strongest in mid-market accounts in their first 90 days (CATE = +0.34) and weakest in enterprise accounts with tenure >2 years (CATE = +0.08)." Robustness checks. Every causal finding is subjected to refutation: random common cause tests, placebo treatment tests, subset validation. If a finding does not survive, it is not reported as causal. We show effect sizes, confidence intervals, and p-values. Not narrative summaries. ### The Meaning Layer Running causal inference once, on one dataset, in a notebook: that is a research project. Running it continuously, at scale, across every customer interaction, with governed and versioned methodology: that is infrastructure. The Meaning Layer is that infrastructure. Fivetran solved ingestion. dbt solved transformation. Snowflake solved warehousing. Each layer assumed data arrives as tables and columns. None were designed for transcripts, chat logs, or survey verbatims. The Meaning Layer is the missing transformation layer for unstructured data. Dimension definitions are versioned like dbt models. Extraction logic is auditable. Outputs are deterministic and reproducible. The schema evolves with the business, with full lineage tracking. Every month, new conversations flow in, Dimensions update, and the causal dataset refreshes. Reports that took weeks become one-click operations. Q1 findings are directly comparable to Q2 because the methodology is encoded in the system, not in someone's head. ### What This Changes When Causal Intelligence is operational, the questions a company can answer shift from descriptive to mechanistic. The retention team stops reviewing random cancellation surveys and starts seeing: these are the 23 accounts most likely to churn this quarter, here are the conversation patterns causing it, here is the revenue at risk per account. The product team stops debating which features to prioritize based on who complained loudest and starts seeing: these four product issues are causally linked to churn, here is the revenue impact of each, fixing issue #1 would reduce mid-market churn by an estimated 15%. The executive team stops asking "why did metrics move" and starts receiving monthly Causal Intelligence reports: consistent, comparable, statistically validated, traceable to source conversations. ### Why Now Three things converged to make this possible. Language models can now extract structured variables from text at scale. Processing a transcript dropped from dollars to fractions of a cent. Accuracy is high enough for production use. Causal inference tooling matured. DoWhy, EconML, and CausalML provide estimation and refutation methods. What was missing was structured language data to feed them. Enterprises accumulated years of conversation data. Support transcripts, chat logs, call recordings, survey responses, all sitting unused. The raw material already exists. It just has not been structured. We built the system that connects all three: language models for extraction, the Meaning Layer for persistence and joining, and causal inference for estimation and proof. The result is Causal Intelligence. Not a new dashboard. Not a better summary. The ability to answer the question every company asks and no tool has been able to answer. Why? Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Customer Experience Analytics | Dimension Labs URL: https://www.dimensionlabs.io/solutions/customer-intelligence Dimension Labs Use case # Customer, guest and fan experience analysis Upload your reviews, surveys, tickets and calls. Find which experience issue is really moving your score, and which is just loud. Start now › See pricing › Ask Which experience issue is actually costing us stars? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## They told you what’s wrong. Your dashboard ranked it by volume. Every tool you own ranks by volume. Marriott’s guests named front desk check-in constantly. It ranked #2 and moved star rating almost none. Right now - 01 The loudest complaint gets fixed first - 02 Sentiment says people are unhappy, never what it costs you - 03 Comparing locations stalls on inconsistent language - 04 A score drops and the answer takes four weeks - 05 Teams act on the average instead of the real driver Knowing what they said is easy. Knowing which part actually moves your score is the job. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for this use case. Hospitality Restaurants Retail Fitness enrichment · one record Raw text We booked a suite for our anniversary and arrived to hair in the sink and a stained carpet . When I asked to move, the manager was dismissive and made us feel like the problem . Then a $39 resort fee we were never told about . We will not be back . Google review · 1 star · property joined Dimensions written to the record - Facility & Cleanliness broadly dirty - Primary Friction cleanliness - Staff Interaction Quality dismissive · named role - Service Recovery refused - Fee & Premium Change undisclosed fee · $39 - Price–Value Perception poor - Loyalty Signal will not return Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one file. Reviews, surveys, tickets, calls, whatever your customers, guests or fans have already told you. Every record comes back enriched with structured Dimensions: root cause, effort, sentiment, escalation risk. - Start with one CSV or connect every channel you have - Join every conversation to account, agent and outcome data - Build custom Dimensions in plain English - Have the agent build context on your business dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, runs the analysis, controls for the things that would confound it, and returns the finding with an effect size and confidence interval. It checks its own work. - Rank issues by causal impact, not complaint volume - Prove which fix would actually move your rating - Put a dollar figure on a specific experience failure - See what separates your best and worst locations dimension labs · ask Example finding Which experience issue is actually costing us stars? Cleanliness moved one-star risk by 34 points. Front desk, mentioned almost three times as often, moved it by about two. 8,504 public Google reviews · Marriott-family hotels in Orlando 3.3 · Deliver ### Ask for the report. Get the report. Say what you need and the agent builds it: executive summary, causal findings, charts, recommendations. Set it to rerun weekly or monthly on fresh data and it lands in your inbox. Export as HTML, PDF or slides. - Produce the monthly readout without building it - Send a link leadership can open and interrogate - Track whether the fix you shipped moved the metric - Export to PDF, slides or your BI tool dimension labs · report The priority list, rearranged by cause Exhibit from “What your CX tools can’t tell you”. Left: the standard CX-analytics ranking. Right: a DoWhy causal model on 8,504 rated public reviews, holding hotel, tier, language, review length and reviewer fixed. Every effect survives placebo, random-cause and subset tests. [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Home Depot Multi-Location Review Analysis Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Gamba Osaka Fan Behavior Intelligence (Japanese) Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Home Depot Multi-Location Review Analysis Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Gamba Osaka Fan Behavior Intelligence (Japanese) Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Home Depot Multi-Location Review Analysis Report AutoNation Customer Review Analysis Report Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Home Depot Multi-Location Review Analysis Report AutoNation Customer Review Analysis Report Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof “Dimension Labs has become the default insight layer across the company.” CX Manager, Stake.com Read the Stake story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Product Feedback Analytics and Prioritization | Dimension Labs URL: https://www.dimensionlabs.io/solutions/product-intelligence Dimension Labs Use case # Product feedback and prioritization Upload every request, ticket and review you have. Find which product problems are actually costing you customers, and which are just loud. Start now › See pricing › Ask Which product problems are costing us customers? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## You have 900 feature requests. Four of them are costing you money. Counting requests rewards whoever complains most. On a ranked list, 400 trial users and 9 of your largest accounts look the same. Right now - 01 The roadmap gets argued from anecdote - 02 Requests get counted, never weighted by what they cost - 03 Bug reports and churn data sit in systems that never meet - 04 Shipping a fix proves nothing Prioritization is a causal problem. Every tool you own treats it as a ranking one. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for this use case. Software Consumer products Automotive AI agents enrichment · one record Raw text We're running a 60-day parallel evaluation of another vendor before we sign the renewal . Honestly, we were promised SSO in Q2 and it still isn't here , and this ticket has been open for three weeks . Support ticket · enterprise account · ARR joined Dimensions written to the record - Competitor Mentioned yes · evaluation underway - Churn & Cancellation Risk high - Lifecycle Stage renewal - Urgency & Deadline 60 days - Expectation Gap roadmap promise missed - Feature Request SSO - Resolution Status open · 3 weeks Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one file of tickets, reviews or verbatims. Every record comes back enriched with structured Dimensions: product area, feature mention, root cause, severity, churn risk. Not a summary of the pile. A typed field on every row. - Unify feedback from support, surveys, reviews and sales into one enriched dataset - Join every interaction with account tier, product usage and revenue data - Build custom Dimensions specific to your product taxonomy - Have the agent build context on your business and products dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent runs the analysis across your feedback and your revenue data, controls for segment and tenure, and returns the finding with an effect size, a confidence interval and a dollar figure attached. - Separate the issues that drive churn from the noise - Attach revenue at risk to a specific product issue - See which requests come from accounts that actually leave - Measure whether a release moved retention dimension labs · ask Example finding Which product problems are costing us customers? Accounts that wrote “we’re evaluating alternatives” left at 91%. Everyone else left at 14%. 80 enterprise accounts · support, chat and survey conversations 3.3 · Deliver ### Ask for the report. Get the report. Ask for the prioritization readout before planning starts and the agent builds it: ranked drivers, the revenue attached to each, charts, recommendations. Rerun it monthly and watch the ranking move as you ship. - Automate monthly product intelligence reports for leadership - Share causal evidence across product, support and exec teams - Track product issue trends over time with versioned methodology - Export to any format or downstream system dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Zendesk Churn Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Owlet Product Voice Intelligence Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Zendesk Churn Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Owlet Product Voice Intelligence Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Mobile Review Analysis Report Owlet Product Voice Intelligence Report Zendesk Churn Analysis Report Duolingo Interactive Insights App App Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Duolingo Mobile Review Analysis Report Owlet Product Voice Intelligence Report Zendesk Churn Analysis Report Duolingo Interactive Insights App App Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof “Structured, reusable conversational data is foundational to our future strategy.” Mars See customer stories › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Churn and Retention Analytics | Dimension Labs URL: https://www.dimensionlabs.io/solutions/revenue-retention-intelligence Dimension Labs Use case # Revenue, churn and retention analysis Upload your calls, tickets and renewal conversations. Join them to your CRM and find out which interaction is actually driving churn, with the revenue attached. Start now › See pricing › Ask Which signals predicted churn, measured against who renewed? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Your CRM knows who left. Your calls know why. Travelers joined 4M conversations to renewal records. They found $3.13M. Right now - 01 Churn scores flag accounts, never explain them - 02 The reason they left is in a transcript nobody queries - 03 Revenue at risk cannot be traced to a specific failure - 04 Retention programs get built on assumption - 05 Save plays run without evidence that they work Your warehouse has the numbers. Your conversations have the reason. Nothing you own joins them. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for this use case. Software Insurance Fitness Banking enrichment · one record Raw text We're running a 60-day parallel evaluation of another vendor before we sign the renewal . Honestly, we were promised SSO in Q2 and it still isn't here , and this ticket has been open for three weeks . Support ticket · enterprise account · ARR joined Dimensions written to the record - Competitor Mentioned yes · evaluation underway - Churn & Cancellation Risk high - Lifecycle Stage renewal - Urgency & Deadline 60 days - Expectation Gap roadmap promise missed - Feature Request SSO - Resolution Status open · 3 weeks Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one file of calls, tickets or verbatims. Every record is enriched with Dimensions like churn risk, competitive intent and expansion signal, then joined to your CRM, billing and usage data on a shared key. - Unify support, sales, survey and renewal data into one enriched dataset - Join every conversation with ARR, contract dates, segment and usage data - Build custom Dimensions specific to your revenue model - Have the agent build context on your business dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent isolates which interaction patterns causally drive cancellation, controlling for plan, tenure and segment. Findings that fail the robustness checks are never reported as causal. - Prove what drives churn instead of what correlates with it - Quantify revenue at risk from one operational pattern - See how drivers differ by segment, region or product line - Surface expansion signals your CRM cannot see dimension labs · ask Example finding Which signals predicted churn, measured against who renewed? Support saw it first for 25 of 29 lost accounts. The surveys never fired for 9. 80 enterprise accounts · 205-day median warning 3.3 · Deliver ### Ask for the report. Get the report. Ask for the retention readout and the agent builds it: findings, revenue at risk, charts, recommendations. Then it keeps running, tracks the actions you took, and verifies whether the number actually moved. - Produce the retention readout without building it - Set a target and track progress against it continuously - Verify whether the fix changed the outcome - Share a link your CFO can interrogate dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Zendesk Churn Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report Zendesk Churn Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Zendesk Churn Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Zendesk Churn Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof “Dimension Labs gives us that 50,000-foot view we needed to figure out what’s going on out there.” Data Science Business Partner, Travelers Read the Travelers story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Customer Insight Analysis for Agencies | Dimension Labs URL: https://www.dimensionlabs.io/solutions/agency-solutions Dimension Labs Use case # Accelerated analysis for agencies Upload the client’s verbatims, tickets or transcripts. Get the coded dataset and the finding in an afternoon, not three weeks. Start now › See pricing › Ask Code these 40,000 verbatims and tell me what’s driving the score. We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Every project starts with 40,000 records and a deadline. Your analysts hand-code verbatims into a taxonomy built from scratch for every client. It is the least billable work you do, and the slowest. Right now - 01 Analysts hand-code verbatims for weeks - 02 A new taxonomy gets built from scratch on every single project - 03 Margin disappears into hours you cannot bill for - 04 Turnaround caps how many projects you can take - 05 You deliver themes when the client wanted proof Clients do not pay for coding hours. They pay for the finding. Everything before it is cost. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for this use case. Hospitality Consumer products Restaurants Retail enrichment · one record Raw text We booked a suite for our anniversary and arrived to hair in the sink and a stained carpet . When I asked to move, the manager was dismissive and made us feel like the problem . Then a $39 resort fee we were never told about . We will not be back . Google review · 1 star · property joined Dimensions written to the record - Facility & Cleanliness broadly dirty - Primary Friction cleanliness - Staff Interaction Quality dismissive · named role - Service Recovery refused - Fee & Premium Change undisclosed fee · $39 - Price–Value Perception poor - Loyalty Signal will not return Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Drop in one client’s file and every record comes back coded: topic, root cause, sentiment, effort, intent. Each client gets a separate workspace, so data never crosses between accounts. Backfill three years of history in the same run. - Run every client in a separate, segregated workspace - Code a full back catalogue, not a sample - Build the client’s own taxonomy in plain English - Have the agent build context on your client automatically dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent runs the analysis, controls for what would confound it, and returns the finding with an effect size and confidence interval. Defensible when the client’s own analytics team pushes back. - Take a proven finding into the room, not a theme cloud - Rank drivers by impact, not by mention volume - Join the client’s CRM or sales data when they share it - Show the exact verbatims behind every finding dimension labs · ask Example finding Code these 40,000 verbatims and tell me what’s driving the score. Coded dataset, ranked drivers with effect sizes, and the verbatims behind each finding, in one run. One client file · separate workspace 3.3 · Deliver ### Ask for the report. Get the report. Say what the deliverable needs to contain and the agent builds it: executive summary, findings, charts, recommendations. Rerun it monthly on fresh data and a one-off project becomes a retainer. Export as HTML, PDF or slides. - Produce the client deck without building it - Turn one-off projects into monthly retainers - Run the same methodology across every client - Export to PDF, slides or their BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof “Structured, reusable conversational data is foundational to our future strategy.” Mars Read the Mars story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Contact Center Analytics and Agent Performance | Dimension Labs URL: https://www.dimensionlabs.io/solutions/agent-contact-center-intelligence Dimension Labs Use case # Contact center and agent performance Read every call, chat and ticket your agents handle. Find which behaviors and process failures move resolution, repeat contacts and CSAT. Start now › See pricing › Ask Which agent behaviors actually move CSAT? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## QA scores two percent of calls. The other ninety-eight decide your CSAT. Sampled QA tells you whether an agent followed the script. It can’t tell you which part of the conversation made the customer call back. Right now - 01 QA reviews a small sample by hand - 02 Scorecards grade script adherence, not outcomes - 03 Repeat contacts have no named cause - 04 Coaching is based on anecdotes - 05 Process failures get blamed on agents Every conversation is already recorded. The answer is in there, unread. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for this use case. Contact center AI agents Insurance Banking enrichment · one record Raw text This is my fourth call about the same bill . The last agent said a credit would post in 48 hours and it never did . You're the first one who actually listened , thank you, can you note that on the account ? Call transcript · billing queue · agent joined Dimensions written to the record - First-Time vs. Repeat repeat · 4th contact - Billing Dispute credit not posted - Expectation Gap promised credit missing - Staff Interaction Quality positive · this agent - Named Employee current agent - Resolution Status pending · note added Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of calls, chats and tickets, or connect every channel. Every record comes back with typed Dimensions like effort, resolution status, repeat contact, staff interaction quality and escalation risk, written the same way every run, then joined to agent, queue, handle time and resolution data. - Read every record, not a sample - Join each record to agent, queue, handle time and resolution data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on resolution, repeat contact and CSAT, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which agent behaviors actually move CSAT - Find the process failures that cause repeat contacts - See what your top agents do that others don’t - Score 100% of conversations, not a sample dimension labs · ask Example finding Which agent behaviors actually move CSAT? Coaching targets ranked by measured effect on repeat contact, with the calls behind each one. Every call, chat and ticket · agent and queue joined 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your operations lead needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## Concentrix Establishes Structured Chatbot Intelligence to Optimize Retail Customer Journeys Concentrix is a leading global provider of customer experience (CX) solutions and technology, supporting some of the world’s most recognized brands.… Read the customer story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## AI Agent and Chatbot Analytics | Dimension Labs URL: https://www.dimensionlabs.io/solutions/ai-agent-analytics Dimension Labs Use case # AI agent and chatbot analytics Read every conversation your bot or AI agent has. Find where automation fails, why customers ask for a person, and what each failure costs. Start now › See pricing › Ask Where does the bot fail, and what does it cost? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Containment looks great. The customers who gave up don’t show up in it. A conversation that ends without a handoff counts as contained, including the ones where the customer quit and called instead. Bot dashboards count turns. They don’t read them. Right now - 01 Containment counts abandonment as success - 02 Failure modes are found by reading transcripts by hand - 03 Nobody knows which intents are safe to automate next - 04 Escalations have no named cause - 05 Model changes ship without measuring their effect We started in conversational AI analytics, in 2016. This is the problem we know best. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for this use case. AI agents Contact center Retail Software enrichment · one record Raw text I asked for a person three times before it let me through. It kept sending me the same help article , which doesn't cover my situation . Twenty minutes to change one address . Bot conversation · handed off · resolution joined Dimensions written to the record - Human-Escalation Attempt 3 attempts - Bot Containment failed - AI Failure Mode loop · repeated answer - Self-Service Attempt article not relevant - Effort Level high · 20 minutes - Automation Opportunity address change Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of bot conversations, AI agent transcripts and handoff tickets, or connect every channel. Every record comes back with typed Dimensions like bot containment, AI failure mode, human-escalation attempt, self-service attempt and automation opportunity, written the same way every run, then joined to intent, resolution, handoff and account data. - Read every record, not a sample - Join each record to intent, resolution, handoff and account data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on true containment, escalation and churn, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Where does the bot fail, and what does it cost - Separate real containment from customers who gave up - Rank the next intents to automate by value - Measure whether a model or prompt change worked dimension labs · ask Example finding Where does the bot fail, and what does it cost? Failure modes ranked by the escalations and repeat contacts they cause, with the transcripts behind each one. Every bot and agent conversation · handoffs joined 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your automation team needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## Intuit QuickBooks Establishes Structured Chatbot Intelligence Across 1M+ Monthly Sessions Intuit QuickBooks supports more than seven million small businesses worldwide, providing financial management tools across accounting, payroll, invoicing, and payments.… Read the customer story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Multi-Location Performance Analytics | Dimension Labs URL: https://www.dimensionlabs.io/solutions/multi-location-intelligence Dimension Labs Use case # Multi-location performance Compare every hotel, store, club or restaurant on the same Dimensions. Find why one location outperforms another, and which fix would move the rating. Start now › See pricing › Ask Why does one location outperform another? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Two locations, same brand, same training. A full star apart. Location reports rank sites by score. They can’t tell you what the better site does differently, because every location’s comments are coded differently, or not at all. Right now - 01 Sites are ranked by score, never explained - 02 Every region codes feedback its own way - 03 Best practices spread by anecdote - 04 Regional managers can’t compare like with like - 05 The fix that worked in one site never gets tested in another The difference between your best and worst location is written down, thousands of times. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for this use case. Restaurants Hospitality Fitness Retail enrichment · one record Raw text Waited 25 minutes in the drive-thru and they still forgot the kids' meals . The shift lead at the window was great about it , but this is the third time at this location . Google review · 2 stars · store joined Dimensions written to the record - Service Speed & Wait 25 min · drive-thru - Transaction / Visit Type drive-thru - Order & Service Accuracy items missing - Staff Interaction Quality positive · named role - Service Recovery attempted - First-Time vs. Repeat repeat · 3rd incident - Location Referenced this store Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of reviews, surveys and tickets from every location, or connect every channel. Every record comes back with typed Dimensions like location referenced, service speed and wait, staff interaction quality, facility and cleanliness and order accuracy, written the same way every run, then joined to location, region, format, staffing and sales data. - Read every record, not a sample - Join each record to location, region, format, staffing and sales data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on rating, revisit and revenue per location, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Why does one location outperform another - Rank each site’s drivers by their effect on rating - Find the operators and practices worth copying - Track whether a fix moved one site’s score dimension labs · ask Example finding Why does one location outperform another? See the Chick-fil-A customer review analysis: 24 Dimensions across public Google Maps reviews, compared location by location. Published artifact · public reviews 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your regional managers needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Home Depot Multi-Location Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Gamba Osaka Fan Behavior Intelligence Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Home Depot Multi-Location Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Gamba Osaka Fan Behavior Intelligence Report Chick-fil-A Customer Review Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Home Depot Multi-Location Review Analysis Report American Dream Mall Visitor Analysis Report Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Chick-fil-A Customer Review Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Home Depot Multi-Location Review Analysis Report American Dream Mall Visitor Analysis Report Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## See it on real data. See the artifacts › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Review Analytics and Competitive Benchmarking | Dimension Labs URL: https://www.dimensionlabs.io/solutions/review-intelligence Dimension Labs Use case # Review intelligence and competitive benchmarking Read every public review of you and your competitors. Compare on the same Dimensions, and find which differences actually move ratings. Start now › See pricing › Ask Where do we lose to competitors, and why? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Your competitors’ customers are telling you how to win. Publicly. Reputation tools count stars and reply to reviews. They don’t read your competitors’ reviews on the same scale as yours, so you can’t see where you actually lose. Right now - 01 Benchmarks stop at star averages - 02 Competitor reviews are read anecdotally, if at all - 03 Every source rates on a different scale - 04 Response teams triage, nobody analyzes - 05 Market shifts show up a quarter late Every review site is a free, continuous survey of your whole market. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for this use case. Automotive Hospitality Consumer products Retail enrichment · one record Raw text The car was delivered with a check engine light on . Registration still hasn't arrived after 60 days , and every call goes to a different advisor . Wouldn't recommend them to anyone . Review · vehicle purchase · deal joined Dimensions written to the record - Reliability & Defect Type check engine · at delivery - Expectation Gap condition on delivery - Urgency & Deadline registration · 60 days - Effort Level high · no single owner - Recommendation Signal detractor - Predicted NPS Band detractor Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of Google, Trustpilot, app store and marketplace reviews, or connect every channel. Every record comes back with typed Dimensions like competitor mentioned, core quality verdict, price-value perception, primary friction and recommendation signal, written the same way every run, then joined to location, brand, date and market data. - Read every record, not a sample - Join each record to location, brand, date and market data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on rating and recommendation, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Where do we lose to competitors, and why - Compare every brand on the same Dimensions - See which differences actually move ratings - Watch for market shifts every week dimension labs · ask Example finding Where do we lose to competitors, and why? See the Carvana multi-source review analysis: 25 Dimensions across five public review sources. Published artifact · public reviews 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your brand and strategy teams needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report Home Depot Multi-Location Review Analysis Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report NBA Fan Experience Intelligence Report Gamba Osaka Fan Behavior Intelligence Report Chick-fil-A Customer Review Analysis Report Eastern Bank Board Diagnostic Report Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report Home Depot Multi-Location Review Analysis Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report NBA Fan Experience Intelligence Report Gamba Osaka Fan Behavior Intelligence Report Chick-fil-A Customer Review Analysis Report Eastern Bank Board Diagnostic Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report Home Depot Multi-Location Review Analysis Report Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report Home Depot Multi-Location Review Analysis Report Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## See it on real data. See the artifacts › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Hotel Guest Experience Analytics | Dimension Labs URL: https://www.dimensionlabs.io/industries/hospitality Dimension Labs Industry # Guest experience analytics for hotels Read every review, survey and stay comment across your portfolio. Find which guest issue actually moves your rating at each property. Start now › See pricing › Ask Which guest issue is actually costing us stars? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## The most-mentioned issue is rarely the one that costs you stars. In public reviews of Marriott-family hotels in Orlando, front desk check-in was mentioned almost three times as often as cleanliness. Cleanliness moved one-star risk by 34 points. Front desk moved it by about two. Right now - 01 Brand-standard audits miss what guests actually notice - 02 Every property’s survey vendor codes differently - 03 Fees and staff issues get averaged away - 04 Owners ask why a property dropped, nobody can prove it - 05 Fixes are funded by complaint volume Your guests already wrote the renovation plan. It’s ranked by volume instead of by effect. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for hotels and hospitality. Hospitality Restaurants Fitness enrichment · one record Raw text We booked a suite for our anniversary and arrived to hair in the sink and a stained carpet . When I asked to move, the manager was dismissive and made us feel like the problem . Then a $39 resort fee we were never told about . We will not be back . Google review · 1 star · property joined Dimensions written to the record - Facility & Cleanliness broadly dirty - Primary Friction cleanliness - Staff Interaction Quality dismissive · named role - Service Recovery refused - Fee & Premium Change undisclosed fee · $39 - Price–Value Perception poor - Loyalty Signal will not return Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of reviews, post-stay surveys and guest messages, or connect every channel. Every record comes back with typed Dimensions like facility and cleanliness, staff interaction quality, fee and premium change, service recovery and loyalty signal, written the same way every run, then joined to property, brand tier, room type and RevPAR. - Read every record, not a sample - Join each record to property, brand tier, room type and RevPAR - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on rating and one-star risk, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which guest issue is actually costing us stars - Compare every property on the same Dimensions - Put a figure on undisclosed fees - See which service recovery actually saves the stay dimension labs · ask Example finding Which guest issue is actually costing us stars? Cleanliness moved one-star risk by 34 points. Front desk, mentioned almost three times as often, moved it by about two. 8,504 rated public Google reviews · Marriott-family hotels in Orlando 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your owners and asset managers needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report The priority list, rearranged by cause Exhibit from “What your CX tools can’t tell you”. Left: the standard CX-analytics ranking. Right: a DoWhy causal model on 8,504 rated public reviews, holding hotel, tier, language, review length and reviewer fixed. Every effect survives placebo, random-cause and subset tests. [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Panera Bread Store Performance Diagnostic Report Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Panera Bread Store Performance Diagnostic Report Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## See it on real data. See the artifacts › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Restaurant Guest Feedback Analytics | Dimension Labs URL: https://www.dimensionlabs.io/industries/restaurants Dimension Labs Industry # Guest feedback analytics for restaurants Read every review and survey across every store. Find whether speed, accuracy, staff or the operator is really moving your rating, location by location. Start now › See pricing › Ask Which issue is costing each store its rating? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Same menu, same training. The difference is somewhere in the comments. Restaurant dashboards report wait time and star averages by store. They don’t tell you why two stores with the same menu are a full star apart. Right now - 01 Store rankings stop at the star average - 02 Drive-thru, delivery and dine-in feedback are read separately - 03 Accuracy issues are hidden in free text - 04 Operator performance is judged by anecdote - 05 Menu launches are judged by sales alone Your guests describe every visit. The pattern across thousands of stores is already there. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for restaurants and food service. Restaurants Retail Hospitality enrichment · one record Raw text Waited 25 minutes in the drive-thru and they still forgot the kids' meals . The shift lead at the window was great about it , but this is the third time at this location . Google review · 2 stars · store joined Dimensions written to the record - Service Speed & Wait 25 min · drive-thru - Transaction / Visit Type drive-thru - Order & Service Accuracy items missing - Staff Interaction Quality positive · named role - Service Recovery attempted - First-Time vs. Repeat repeat · 3rd incident - Location Referenced this store Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of Google reviews, surveys and delivery-app feedback, or connect every channel. Every record comes back with typed Dimensions like service speed and wait, order and service accuracy, staff interaction quality, transaction type and location, written the same way every run, then joined to store, format, daypart and sales data. - Read every record, not a sample - Join each record to store, format, daypart and sales data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on rating, revisit and same-store sales, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which issue is costing each store its rating - Separate drive-thru, delivery and dine-in drivers - See what top operators do differently - Measure how guests received a new menu item dimension labs · ask Example finding Which issue is costing each store its rating? See the Panera store performance diagnostic: 27 Dimensions across public reviews, compared store by store. Published artifact · public reviews 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your operators and franchise leads needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Panera Bread Store Performance Diagnostic Report Chick-fil-A Customer Review Analysis Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Chick-fil-A Customer Review Analysis Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Chick-fil-A Customer Review Analysis Report Panera Bread Store Performance Diagnostic Report Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Chick-fil-A Customer Review Analysis Report Panera Bread Store Performance Diagnostic Report Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## See it on real data. See the artifacts › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Fitness and Leisure Member Experience Analytics | Dimension Labs URL: https://www.dimensionlabs.io/industries/fitness-and-recreation Dimension Labs Industry # Member experience analytics for fitness and leisure Read every member review, survey and cancellation reason. Find which issue at which club actually drives churn. Start now › See pricing › Ask Which club issue actually drives member churn? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Members tell you why they’re leaving months before they cancel. Cancellation reasons are a dropdown. The real reasons, broken equipment, crowding, a cancellation process that feels like a trap, are written in reviews and surveys nobody joins to churn. Right now - 01 Cancellation reasons come from a dropdown - 02 Equipment and cleanliness complaints are read club by club - 03 Churn models flag members but never explain them - 04 Club and location comparisons stall on inconsistent feedback - 05 Retention offers go out without evidence Churn is a lagging number. What members said last month is the leading one. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for fitness and leisure. Fitness Hospitality Restaurants enrichment · one record Raw text Half the treadmills have had out-of-order signs for a month . Tried to cancel at the front desk and was told it has to be done by certified mail . Moving to the gym down the road . Google review · member · club joined Dimensions written to the record - Facility & Cleanliness equipment broken · 1 month - Reliability & Defect Type equipment - Churn & Cancellation Risk cancelling - Effort Level high · mail-only cancel - Trust & Honesty Signal low - Competitor Mentioned nearby gym Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of member reviews, NPS surveys and cancellation notes, or connect every channel. Every record comes back with typed Dimensions like facility and cleanliness, churn and cancellation risk, effort level, staff interaction quality and loyalty signal, written the same way every run, then joined to club, membership tier, visit frequency and tenure. - Read every record, not a sample - Join each record to club, membership tier, visit frequency and tenure - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on cancellation and visit frequency, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which club issue actually drives member churn - Find the members at risk, and the reason - Compare every club or location on the same Dimensions - Test whether a retention offer changed anything dimension labs · ask Example finding Which club issue actually drives member churn? See the Planet Fitness member churn monitor: 28 Dimensions across public Google Maps reviews, tracked as a live causal monitor. Published artifact · public reviews 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your club and regional leaders needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Planet Fitness Causal Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Planet Fitness Causal Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## See it on real data. See the artifacts › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Retail Customer Feedback Analytics | Dimension Labs URL: https://www.dimensionlabs.io/industries/retail Dimension Labs Industry # Customer feedback analytics for retail Read every review, chat and return reason across stores and online. Find which issue actually moves rating, returns and repeat purchase. Start now › See pricing › Ask Which issue drives returns and lost repeat purchase? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Returns tell you what came back. Customers tell you why. Retail feedback is spread across store reviews, support chats, marketplace ratings and return codes. Each team reads its own slice, so nobody sees which issue costs the most. Right now - 01 Store and online feedback live in different tools - 02 Return codes hide the real reason - 03 Delivery failures surface as one-star reviews weeks later - 04 Store comparisons stall on inconsistent coding - 05 Merchandising decisions ignore what customers wrote Every return reason and every review is a vote. Nobody is counting them on one scale. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for retail and e-commerce. Retail Consumer products Restaurants enrichment · one record Raw text Tracking says delivered on Tuesday but nothing is at my door . I've tried the chat three times and nobody answers . Just refund me , I'll buy it somewhere else . Chat transcript · online order · order joined Dimensions written to the record - Order & Service Accuracy delivered, not received - Effort Level high · 3 attempts - Resolution Status unresolved - Return & Refund Signal refund requested - Churn & Cancellation Risk high - Competitor Mentioned implied Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of store reviews, support chats, return reasons and product reviews, or connect every channel. Every record comes back with typed Dimensions like order and service accuracy, return and refund signal, effort level, location referenced and price-value perception, written the same way every run, then joined to store, SKU, order and loyalty data. - Read every record, not a sample - Join each record to store, SKU, order and loyalty data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on rating, returns and repeat purchase, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which issue drives returns and lost repeat purchase - Compare every store on the same Dimensions - Find delivery failures before they hit ratings - See which products customers regret buying dimension labs · ask Example finding Which issue drives returns and lost repeat purchase? See the Home Depot multi-location review analysis: 19 Dimensions across public reviews, compared store by store. Published artifact · public reviews 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your store operations and e-commerce leads needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Home Depot Multi-Location Review Analysis Report American Dream Mall Visitor Analysis Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Home Depot Multi-Location Review Analysis Report American Dream Mall Visitor Analysis Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report American Dream Mall Visitor Analysis Report Home Depot Multi-Location Review Analysis Report Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck American Dream Mall Visitor Analysis Report Home Depot Multi-Location Review Analysis Report Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## Concentrix Establishes Structured Chatbot Intelligence to Optimize Retail Customer Journeys Concentrix is a leading global provider of customer experience (CX) solutions and technology, supporting some of the world’s most recognized brands.… Read the customer story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Fan Experience Analytics for Sports Teams | Dimension Labs URL: https://www.dimensionlabs.io/industries/sports-and-entertainment Dimension Labs Industry # Fan experience analytics for sports and live entertainment Read every fan survey, review and post. Find which part of game day actually moves renewals and attendance, joined to your ticketing data. Start now › See pricing › Ask Which part of game day moves season ticket renewal? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Win-loss explains attendance. Everything else is in the surveys. Teams collect thousands of post-game survey comments a season. Most get read once, if at all, and never get joined to who renewed. Right now - 01 Post-game surveys are summarized, not measured - 02 Season ticket renewal risk is spotted too late - 03 Concessions, entry and app issues are read separately - 04 The effect of losing streaks is assumed, not measured - 05 Sponsors ask for proof of fan sentiment Fans tell you exactly what would bring them back. Tens of thousands of times a season. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for sports and live entertainment. Sports Hospitality Restaurants enrichment · one record Raw text Eleven years as a season ticket holder and the new app wouldn't load my tickets at the gate . We missed the first quarter standing in line. I'm seriously reconsidering renewing . Fan survey · season ticket member · account joined Dimensions written to the record - Loyalty Signal 11-year member - Customer Segment season ticket - Digital & App Experience failed at entry - Primary Friction ticket access - Emotional Intensity high - Churn & Cancellation Risk renewal at risk Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of fan surveys, reviews, app feedback and social posts, or connect every channel. Every record comes back with typed Dimensions like loyalty signal, digital and app experience, service speed and wait, customer segment and churn risk, written the same way every run, then joined to ticketing, attendance, game results and concessions. - Read every record, not a sample - Join each record to ticketing, attendance, game results and concessions - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on renewal, attendance and spend per cap, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which part of game day moves season ticket renewal - Separate the effect of losing from the effect of the experience - Find the members at risk before renewal - Compare fan experience across venues and events dimension labs · ask Example finding Which part of game day moves season ticket renewal? See the NBA fan experience intelligence: 21 Dimensions across survey responses. Published artifact 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your ticketing and fan experience leads needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Gamba Osaka Fan Behavior Intelligence (Japanese) Report NBA Fan Experience Intelligence Report Gamba Osaka Fan Behavior Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report Home Depot Multi-Location Review Analysis Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Gamba Osaka Fan Behavior Intelligence (Japanese) Report NBA Fan Experience Intelligence Report Gamba Osaka Fan Behavior Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report Home Depot Multi-Location Review Analysis Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Gamba Osaka Fan Behavior Intelligence (Japanese) Report NBA Fan Experience Intelligence Report Gamba Osaka Fan Behavior Intelligence Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Gamba Osaka Fan Behavior Intelligence (Japanese) Report NBA Fan Experience Intelligence Report Gamba Osaka Fan Behavior Intelligence Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## How the Seattle Mariners Transformed Fan Feedback into Structured Intelligence Each season, the Seattle Mariners host 81 regular season home games, welcoming 31,000+ fans per night and collecting massive amounts of fan feedback through post-game satisfaction surveys.… Read the customer story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Insurance Customer Experience Analytics | Dimension Labs URL: https://www.dimensionlabs.io/industries/insurance Dimension Labs Industry # Policyholder experience analytics for insurance Read every call, claim note and complaint. Find which experience actually drives lapse and complaints, joined to policy and claims data. Start now › See pricing › Ask Which experience drives policy lapse? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Policyholders tell you they’re shopping before the renewal date. Premium increases, slow claims and unexplained decisions show up in calls and complaints months before a policy lapses. Speech analytics flags keywords. It doesn’t measure which one costs you the policy. Right now - 01 Lapse models score risk without a reason - 02 Claims experience is reviewed one file at a time - 03 Complaint themes are counted, not measured - 04 Premium change conversations aren’t analyzed at all - 05 Chatbot and agent channels are measured separately The reason a policyholder leaves is usually in the last three conversations. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for insurance. Insurance Contact center AI agents enrichment · one record Raw text My premium went up 22% with no claim on the policy and nobody explained why . I've already got a quote from another carrier , so tell me why I should stay . Call transcript · auto policy · policy joined Dimensions written to the record - Fee & Premium Change +22% · no claim - Dollar Amount Referenced 22% - Trust & Honesty Signal low · no explanation - Competitor Mentioned competing quote - Churn & Cancellation Risk high - Objection price, needs a reason to stay Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of call transcripts, claim notes, complaints and chat logs, or connect every channel. Every record comes back with typed Dimensions like fee and premium change, trust and honesty signal, competitor mentioned, resolution status and regulatory exposure, written the same way every run, then joined to policy, premium, claims and tenure data. - Read every record, not a sample - Join each record to policy, premium, claims and tenure data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on lapse, complaint and claims satisfaction, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which experience drives policy lapse - Measure the effect of premium change conversations - Find claims experiences that create complaints - Flag regulatory exposure across every channel dimension labs · ask Example finding Which experience drives policy lapse? See the Rhino causal brief: 26 Dimensions across public Trustpilot and BBB reviews. Published artifact · public reviews 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your retention and claims leaders needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Rhino Causal Brief Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Rhino Causal Brief Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Rhino Causal Brief Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Rhino Causal Brief Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof “Dimension Labs gives us that 50,000-foot view we needed to figure out what’s going on out there.” Data Science Business Partner, Travelers Read the Travelers story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Banking Customer Experience Analytics | Dimension Labs URL: https://www.dimensionlabs.io/industries/financial-services Dimension Labs Industry # Customer experience analytics for banking and financial services Read every complaint, call and review. Find which fee, process or channel issue actually drives attrition, with compliance flags on every record. Start now › See pricing › Ask Which issue drives account attrition? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Complaints are logged for the regulator. Nobody reads them for the business. Banks collect complaints, call notes and reviews to stay compliant. The same records explain attrition, and almost nobody analyzes them that way. Right now - 01 Complaints are categorized for compliance, then filed - 02 Fee disputes are handled one at a time - 03 Branch and digital experience are measured separately - 04 Attrition models can’t explain who leaves - 05 Board reporting lags a quarter behind The complaint log is the most honest customer research a bank owns. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for banking and financial services. Banking Insurance Contact center enrichment · one record Raw text Charged a $35 overdraft fee on a deposit that cleared the same day . The branch said they can't reverse it and the app shows a different balance than the statement . I'll move my accounts . Complaint · checking account · branch joined Dimensions written to the record - Billing Dispute overdraft fee - Dollar Amount Referenced $35 - Service Recovery refused at branch - Digital & App Experience balance mismatch - Trust & Honesty Signal low - Churn & Cancellation Risk moving accounts Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of complaints, call notes, reviews and chat logs, or connect every channel. Every record comes back with typed Dimensions like billing dispute, trust and honesty signal, compliance flag, digital and app experience and churn risk, written the same way every run, then joined to account, product, branch and balance data. - Read every record, not a sample - Join each record to account, product, branch and balance data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on attrition, complaints and primary-bank status, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which issue drives account attrition - Find fee disputes that turn into departures - Compare branches and digital on one scale - Flag compliance exposure on every record dimension labs · ask Example finding Which issue drives account attrition? See the Eastern Bank board diagnostic: 29 Dimensions across reviews and complaints from eight public sources. Published artifact · public sources 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your board and risk committee needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Eastern Bank Board Diagnostic Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## Intuit QuickBooks Establishes Structured Chatbot Intelligence Across 1M+ Monthly Sessions Intuit QuickBooks supports more than seven million small businesses worldwide, providing financial management tools across accounting, payroll, invoicing, and payments.… Read the customer story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## SaaS Customer Feedback and Churn Analytics | Dimension Labs URL: https://www.dimensionlabs.io/industries/software-and-saas Dimension Labs Industry # Customer feedback analytics for software companies Read every ticket, call and review. Find which signals predict churn and which product gaps cost revenue, joined to ARR, usage and renewals. Start now › See pricing › Ask Which signals predicted churn, measured against who renewed? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## The accounts that churned told support first. Health scores are built from usage and surveys. The earliest warning is usually in a support ticket or a call transcript that never gets joined to the renewal. Right now - 01 Health scores flag accounts without a reason - 02 Feature requests are counted, not weighted by revenue - 03 Support and success data never meet - 04 Roadmap debates are settled by the loudest account - 05 Churn post-mortems are written after the fact Your support queue is the earliest churn signal you have. It’s read one ticket at a time. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for software and saas. Software AI agents Contact center enrichment · one record Raw text We're running a 60-day parallel evaluation of another vendor before we sign the renewal . Honestly, we were promised SSO in Q2 and it still isn't here , and this ticket has been open for three weeks . Support ticket · enterprise account · ARR joined Dimensions written to the record - Competitor Mentioned yes · evaluation underway - Churn & Cancellation Risk high - Lifecycle Stage renewal - Urgency & Deadline 60 days - Expectation Gap roadmap promise missed - Feature Request SSO - Resolution Status open · 3 weeks Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of support tickets, call transcripts, reviews and NPS comments, or connect every channel. Every record comes back with typed Dimensions like churn and cancellation risk, feature request, competitor mentioned, expectation gap and lifecycle stage, written the same way every run, then joined to ARR, plan, usage and renewal data. - Read every record, not a sample - Join each record to ARR, plan, usage and renewal data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on churn, expansion and NRR, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which signals predicted churn, measured against who renewed - Rank feature requests by the revenue behind them - Find expansion signals your CRM can’t see - Show which channel saw the warning first dimension labs · ask Example finding Which signals predicted churn, measured against who renewed? See the Zendesk churn analysis: 33 Dimensions across support tickets, chat and onboarding surveys. Published artifact 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your CS and product leaders needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Zendesk Churn Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Eastern Bank Board Diagnostic Report Zendesk Churn Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Eastern Bank Board Diagnostic Report Duolingo Mobile Review Analysis Report Zendesk Churn Analysis Report Duolingo Interactive Insights App App Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Duolingo Mobile Review Analysis Report Zendesk Churn Analysis Report Duolingo Interactive Insights App App Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## How Dropbox Transformed Conversational Data into Scalable Self-Service Intelligence Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. … Read the customer story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Automotive Dealer Customer Experience Analytics | Dimension Labs URL: https://www.dimensionlabs.io/industries/automotive Dimension Labs Industry # Customer experience analytics for automotive retail Read every review, survey and service note. Find which part of buying, delivery and service moves your rating and repeat business. Start now › See pricing › Ask Which part of the process costs us recommendations? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## The sale gets surveyed. Everything after it gets reviewed. Dealer and online retail surveys stop at the sale. Delivery condition, registration delays and service visits show up later, in public reviews, where they shape the next buyer’s choice. Right now - 01 OEM surveys stop at the point of sale - 02 Delivery and title issues surface as reviews - 03 Rooftops are compared by star average - 04 Service and sales feedback live in different systems - 05 Online and in-store buyers are measured differently Every buyer leaves a detailed account of the process. Most of it is public. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for automotive. Automotive Retail Consumer products enrichment · one record Raw text The car was delivered with a check engine light on . Registration still hasn't arrived after 60 days , and every call goes to a different advisor . Wouldn't recommend them to anyone . Review · vehicle purchase · deal joined Dimensions written to the record - Reliability & Defect Type check engine · at delivery - Expectation Gap condition on delivery - Urgency & Deadline registration · 60 days - Effort Level high · no single owner - Recommendation Signal detractor - Predicted NPS Band detractor Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of reviews, sales and service surveys and support tickets, or connect every channel. Every record comes back with typed Dimensions like reliability and defect type, expectation gap, urgency and deadline, effort level and recommendation signal, written the same way every run, then joined to rooftop, vehicle, deal and service data. - Read every record, not a sample - Join each record to rooftop, vehicle, deal and service data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on rating, recommendation and service retention, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which part of the process costs us recommendations - Compare every rooftop on the same Dimensions - Find delivery and title failures early - Track whether a process change moved ratings dimension labs · ask Example finding Which part of the process costs us recommendations? See the Carvana customer voice monitor: 25 Dimensions across reviews from five public sites, refreshed weekly. Published artifact · public reviews 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your rooftop and regional leaders needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report AutoNation Customer Review Analysis Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report AutoNation Customer Review Analysis Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report AutoNation Customer Review Analysis Report Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck AutoNation Customer Review Analysis Report Carvana Customer Voice Monitor Live monitor Carvana Multi-Source Review Analysis Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## See it on real data. See the artifacts › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Consumer Product Review Analytics for CPG Brands | Dimension Labs URL: https://www.dimensionlabs.io/industries/consumer-products Dimension Labs Industry # Product voice analytics for consumer brands Read every product review, care contact and social post. Find which defects and experiences actually move ratings and returns, SKU by SKU. Start now › See pricing › Ask Which defect is costing us ratings and returns? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Your retail partners read your reviews. Your product team should too. Consumer feedback is spread across retailer reviews, care lines and social. Each source is read separately, so a defect pattern can run for months before it’s named. Right now - 01 Retailer reviews are read one listing at a time - 02 Care-line contacts are coded inconsistently - 03 Defect patterns take months to surface - 04 Launches are judged on sales alone - 05 Claims and safety signals are found by chance A product’s real spec sheet is written by the people who use it. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for consumer products and cpg. Consumer products Retail AI agents enrichment · one record Raw text The sensor drops connection every night around 2am , which is the whole point of buying it . Support sent a replacement that does the same thing . Returning it . Product review · 1 star · SKU joined Dimensions written to the record - Reliability & Defect Type connectivity · nightly - Product / Module Referenced sensor - Core Quality Verdict fails core job - Service Recovery replacement · same defect - Return & Refund Signal returning Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of retailer reviews, care-line contacts and social posts, or connect every channel. Every record comes back with typed Dimensions like reliability and defect type, core quality verdict, return and refund signal, product referenced and safety-critical event, written the same way every run, then joined to SKU, batch, retailer and sales data. - Read every record, not a sample - Join each record to SKU, batch, retailer and sales data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on rating, returns and repeat purchase, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which defect is costing us ratings and returns - Compare every SKU on the same Dimensions - See how a launch landed in the first month - Flag safety and claims signals on every record dimension labs · ask Example finding Which defect is costing us ratings and returns? See the Owlet product voice intelligence: 34 Dimensions across product reviews and public filings. Published artifact · public sources 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your brand and quality teams needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Owlet Product Voice Intelligence Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Owlet Product Voice Intelligence Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof “Structured, reusable conversational data is foundational to our future strategy.” Mars Read the Mars story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Conversation Analytics for Contact Centers and BPOs URL: https://www.dimensionlabs.io/industries/contact-centers-and-bpo Dimension Labs Industry # Conversation analytics for contact centers and BPOs Read every call, chat and bot conversation across every client program. Prove which agent and process changes moved resolution and CSAT. Start now › See pricing › Ask Which changes moved resolution for this client? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Your clients want proof the program is working. QA samples won’t give it to them. BPOs run dozens of programs, each with its own taxonomy and scorecard. Proving value at the QBR means hand-built decks from sampled data. Right now - 01 QA reviews a fraction of interactions - 02 Every client program codes differently - 03 QBR decks are built by hand - 04 Bot and human channels are measured separately - 05 Process fixes ship without measuring the effect You already handle every conversation. The analysis should cover every one too. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for contact centers and bpos. Contact center AI agents Retail enrichment · one record Raw text This is my fourth call about the same bill . The last agent said a credit would post in 48 hours and it never did . You're the first one who actually listened , thank you, can you note that on the account ? Call transcript · billing queue · agent joined Dimensions written to the record - First-Time vs. Repeat repeat · 4th contact - Billing Dispute credit not posted - Expectation Gap promised credit missing - Staff Interaction Quality positive · this agent - Named Employee current agent - Resolution Status pending · note added Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of calls, chats, emails and bot conversations, or connect every channel. Every record comes back with typed Dimensions like resolution status, effort level, first-time vs. repeat, bot containment and staff interaction quality, written the same way every run, then joined to program, agent, queue and outcome data. - Read every record, not a sample - Join each record to program, agent, queue and outcome data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on resolution, repeat contact and CSAT, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which changes moved resolution for this client - Score every interaction, not a sample - Run the same method across every program - Produce the QBR without building it dimension labs · ask Example finding Which changes moved resolution for this client? Ranked drivers of repeat contact for each program, with the conversations behind each one, in one run. Every interaction · program and agent joined 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your clients needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## Concentrix Establishes Structured Chatbot Intelligence to Optimize Retail Customer Journeys Concentrix is a leading global provider of customer experience (CX) solutions and technology, supporting some of the world’s most recognized brands.… Read the customer story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Constituent Feedback Analytics for Public Services URL: https://www.dimensionlabs.io/industries/public-sector-and-nonprofit Dimension Labs Industry # Constituent and member feedback analytics for public services and nonprofits Read every call, chat and survey from the people you serve. Find which process failures block enrollment and service, in every language. Start now › See pricing › Ask Which process failures block enrollment? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## When the process fails, people call. Every call explains the failure. Public programs and nonprofits handle surges of conversations around deadlines and emergencies. The patterns that would fix the process are rarely analyzed while they still matter. Right now - 01 Call surges are staffed, not analyzed - 02 Multilingual feedback is read in one language only - 03 Process failures are found after the deadline - 04 Reports to boards and funders are built by hand - 05 Chatbot and phone channels are measured separately Every enrollment question is a signal about where the process breaks. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for public sector and nonprofit. Public services Contact center AI agents enrichment · one record Raw text My coverage ends Friday and I uploaded my income documents twice . The portal keeps saying they're missing . I can't go without insurance for my kids . Enrollment chat · applicant · case joined Dimensions written to the record - Urgency & Deadline coverage ends Friday - Effort Level high · duplicate uploads - Digital & App Experience documents not recognized - Escalation Risk high - Emotional Intensity high - Customer Segment family coverage Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of calls, chats, emails and surveys in any language, or connect every channel. Every record comes back with typed Dimensions like urgency and deadline, effort level, digital and app experience, escalation risk and customer segment, written the same way every run, then joined to case, program, region and outcome data. - Read every record, not a sample - Join each record to case, program, region and outcome data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on enrollment, resolution and retention, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which process failures block enrollment - Read every language on the same Dimensions - Find surges before the deadline, not after - Produce the board and funder report on schedule dimension labs · ask Example finding Which process failures block enrollment? Process failures ranked by their effect on completed enrollment, with the conversations behind each one. Every channel · case data joined 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your board and funders needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## Covered California Establishes a Structured Intelligence Layer to Scale Health Coverage Enrollment Covered California is the state’s official health insurance marketplace, providing access to affordable health coverage for millions of residents. … Read the customer story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Real Estate Lead Conversation Analytics | Dimension Labs URL: https://www.dimensionlabs.io/industries/real-estate Dimension Labs Industry # Lead conversation analytics for real estate Read every lead text, call and chat. Find which signals predict a showing and a closing, and which follow-up patterns lose buyers. Start now › See pricing › Ask Which signals predict a closing? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Lead scores rank by clicks. The buying signal is in the conversation. Real estate platforms handle millions of lead conversations. Pre-approval, timing and agent switching are all said plainly, and almost never captured as data. Right now - 01 Lead scores are built on web activity - 02 Buying signals in texts aren’t captured - 03 Agent follow-up quality isn’t measured - 04 Lost leads have no recorded reason - 05 Nurture sequences aren’t tested against outcomes Buyers tell you their budget, timeline and frustrations in the first three messages. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for real estate. Real estate Software AI agents enrichment · one record Raw text We're pre-approved up to 650 and need to be in before school starts in August . Is the one on Maple still available ? Our current agent stopped responding . SMS thread · inbound lead · CRM joined Dimensions written to the record - Buying Signal pre-approved - Dollar Amount Referenced $650K - Urgency & Deadline before August - Customer Intent showing request - Incumbent / Current Setup has an agent - Competitive Context switch opportunity Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of lead texts, calls, chats and emails, or connect every channel. Every record comes back with typed Dimensions like buying signal, urgency and deadline, customer intent, incumbent setup and dollar amount referenced, written the same way every run, then joined to lead source, agent, showing and closing data. - Read every record, not a sample - Join each record to lead source, agent, showing and closing data - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on showings and closings, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which signals predict a closing - Find the follow-up patterns that lose buyers - Rank leads by what they said, not what they clicked - Measure which nurture sequences work dimension labs · ask Example finding Which signals predict a closing? Buying signals ranked by their effect on showings and closings, with the conversations behind each one. Every lead conversation · CRM joined 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your brokerage and platform leaders needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof ## Ylopo Establishes Structured Conversation Intelligence to Scale Real Estate Lead Conversion Ylopo is a digital marketing and lead generation platform built specifically for real estate businesses.… Read the customer story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Player Conversation Analytics for iGaming | Dimension Labs URL: https://www.dimensionlabs.io/industries/gaming-and-betting Dimension Labs Industry # Player conversation analytics for gaming and betting Read every player chat, ticket and review. Find which payment, verification and product issues drive churn among your most valuable players. Start now › See pricing › Ask Which issues drive VIP churn? We work with enterprises such as NBA, Travelers, Mars and Concentrix. [01] The problem ## Your VIPs tell support before they take their balance elsewhere. Gaming platforms handle millions of player conversations a month. Withdrawal delays, verification loops and bonus disputes are all in there, joined to nothing. Right now - 01 Support volume is staffed, not analyzed - 02 VIP churn is spotted after the fact - 03 Payment and verification friction isn’t measured - 04 Bonus disputes are handled one at a time - 05 Every market and language is read separately The players worth the most are the ones who talk to support the most. [02] Enrich One record, structured ## Language in. Structured data out. Every record is read in full and written back with dozens of typed Dimensions, the same way every run. Here is what that looks like for gaming and betting. Gaming Contact center AI agents enrichment · one record Raw text My withdrawal has been pending for five days . Verification asked for the same document again . I've played here for three years and I'm about to take my balance elsewhere . Live chat · VIP player · account joined Dimensions written to the record - Service Speed & Wait withdrawal · 5 days - Urgency & Deadline high - Effort Level high · repeat verification - Loyalty Signal 3-year player - Customer Segment VIP - Churn & Cancellation Risk high Each colour links a phrase in the raw text to the Dimensions extracted from it. Hover either side. [03] The product 3.1 · Read everything ### Every record, enriched. Start with one export of live chats, tickets, emails and reviews, or connect every channel. Every record comes back with typed Dimensions like service speed and wait, effort level, loyalty signal, customer segment and churn risk, written the same way every run, then joined to player tier, deposits, market and tenure. - Read every record, not a sample - Join each record to player tier, deposits, market and tenure - Add your own Dimensions in plain English - Backfill years of history in the same run dimension labs · blend 3.2 · Prove the cause ### Ask why. Get proof. Ask in plain English. The agent writes the queries, controls for what would confound the answer, and ranks each issue by its measured effect on player churn and deposits, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal. - Which issues drive VIP churn - Measure payment and verification friction - Compare every market and language on one scale - Track whether a fix moved deposits dimension labs · ask Example finding Which issues drive VIP churn? Player issues ranked by their effect on churn and deposits, across every market and language. Every player conversation · account joined 3.3 · Deliver ### Ask for the report. Get the report. Ask for the readout your VIP and operations leads needs and the agent builds it: summary, ranked findings, charts and recommendations, each figure checked. Schedule it weekly or monthly on fresh data, or publish it as a live monitor. - Produce the monthly readout without building it - Publish a live monitor leadership can open and question - Track whether the fix you shipped moved the number - Export to PDF, slides or your BI tool dimension labs · report [04] How the platform works Runs daily ## From raw conversations to a proven answer. Language from every channel and your business data flow into one pipeline that enriches, governs and tests every record, then delivers wherever your team works. Language · 37 sources Zendesk Intercom Salesforce LiveChat Drift Slack Qualtrics Medallia SurveyMonkey QuestionPro Genesys Amazon Connect Avaya Cloud 8x8 Twilio SMS Ada Kore.ai Yellow.ai Cognigy Rasa Microsoft Bot Framework Amazon Lex Amazon Alexa Oracle Digital Assistant Botcopy Capacity Facebook Messenger Kik X (Twitter) OpenAI Email Call recordings App store reviews Google reviews Open-ended surveys Community forums Business + world data Snowflake Databricks BigQuery Amazon S3 Salesforce CRM CSV + Excel REST API Revenue Orders Renewals Usage events Loyalty Store + location data Weather Seasonality Market events Dimension Labs pipeline · Runs daily 01 Ingest + normalize Every channel and language into one schema 02 Enrich Dozens of Dimensions written onto every record 03 Map + govern Validated for drift, versioned, the same fields every run 04 Analyze + prove Joined to outcomes and tested for cause Where it goes Dimension Labs agent Ask, report, schedule Your warehouse Snowflake, Databricks, BigQuery BI tools Tableau, Power BI, Hex Claude and ChatGPT via MCP · coming soon [05] Artifacts Built by the agent ## What you get. Reports, monitors and decks, built by the agent. Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › [06] Proof “Dimension Labs has become the default insight layer across the company.” CX Manager, Stake.com Read the Stake story › The agent on 80 enterprise accounts 0:53 [07] Explore 8 use cases · 14 industries By use case Customer, guest and fan experience Which issue moves your score Product feedback and prioritization What to build, ranked by impact Revenue, churn and retention What customers said before they left Agencies Client analysis in hours, not weeks Contact center performance What separates your best agents AI agent and chatbot analytics Where automation fails, and what it costs Multi-location performance Why one site beats another Reviews and competitive benchmarking You and every competitor, side by side By industry Hotels and hospitality Guest experience, property by property Restaurants and food service Speed, accuracy and the operator effect Fitness and leisure Member churn, club by club Retail and e-commerce Store, site and delivery, on one scale Sports and live entertainment Fan experience and renewal Insurance Claims, renewals and premium change Banking and financial services Fees, trust and attrition Software and SaaS Churn signals and product priorities Automotive Purchase, delivery and service Consumer products and CPG Product voice and defects Contact centers and BPOs Every client program, one method Public sector and nonprofit Constituents, members and donors Real estate Lead conversations to closings Gaming and betting Player conversations at scale Dimension Labs / Start with one file From $299 a month ## Find out what's really moving your number. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Customer Stories: NBA, Travelers, Mars & More | Dimension Labs URL: https://www.dimensionlabs.io/customer-stories Dimension Labs Customer stories # Built for teams who refuse to guess. How the NBA, Travelers, Mars, Concentrix and others turned millions of conversations into structured, comparable data they decide with. Start now › [01] Who uses it 11 stories · 10 industries Read › Read › Read › Read › Read › Read › Read › Read › Read › [02] Featured Insurance Customer story · Travelers Travelers Establishes a Unified Meaning Layer Across Millions of Customer Interactions “Dimension Labs gives us that 50,000-foot view we needed to figure out what’s going on out there.” Data Science Business Partner, Travelers Read the story › Sports NBA How the NBA Transforms Fan Survey Responses into Structured Fan Intelligence The NBA is one of the world’s most recognized professional sports leagues, operating across 30 teams and engaging hundreds of millions of fans globally. Gaming Stake.com How Stake.com Turns 1.5M+ Monthly Player Conversations Into Actionable Intelligence Stake.com operates one of the world’s largest global iGaming platforms, spanning online casino, live dealer, and sportsbook experiences across dozens of markets. Consumer products Mars Petcare How Mars Petcare Built a Meaning Layer for Customer Conversations Mars Petcare is one of the world’s largest pet nutrition and care businesses, operating as part of Mars, Incorporated, a global consumer packaged goods company with over $50B in annual revenue. Sports Seattle Mariners How the Seattle Mariners Transformed Fan Feedback into Structured Intelligence Each season, the Seattle Mariners host 81 regular season home games, welcoming 31,000+ fans per night and collecting massive amounts of fan feedback through post-game satisfaction surveys. Contact centers Concentrix Concentrix Establishes Structured Chatbot Intelligence to Optimize Retail Customer Journeys Concentrix is a leading global provider of customer experience (CX) solutions and technology, supporting some of the world’s most recognized brands. Covered California Public sector Covered California Covered California Establishes a Structured Intelligence Layer to Scale Health Coverage Enrollment Covered California is the state’s official health insurance marketplace, providing access to affordable health coverage for millions of residents. Nonprofit American Red Cross The American Red Cross Establishes a Real-Time Meaning Layer for Disaster Response The American Red Cross (ARC) is one of the world’s leading humanitarian organizations, providing emergency assistance, disaster relief, and preparedness education across the United States. AI agents Intuit QuickBooks Intuit QuickBooks Establishes Structured Chatbot Intelligence Across 1M+ Monthly Sessions Intuit QuickBooks supports more than seven million small businesses worldwide, providing financial management tools across accounting, payroll, invoicing, and payments. Software Dropbox How Dropbox Transformed Conversational Data into Scalable Self-Service Intelligence Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. Ylopo Real estate Ylopo Ylopo Establishes Structured Conversation Intelligence to Scale Real Estate Lead Conversion Ylopo is a digital marketing and lead generation platform built specifically for real estate businesses. In their words “Dimension Labs gives us that 50,000-foot view we needed to figure out what’s going on out there.” Data Science Business Partner, Travelers “Dimension Labs has become the default insight layer across the company.” CX Manager, Stake.com “Structured, reusable conversational data is foundational to our future strategy.” Mars [03] Public analyses Built by the agent ## Not a customer yet? We’ve run the agent on public data for dozens of brands. Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Panera Bread Store Performance Diagnostic Report Marriott Multi-Location Review Analysis Report Marriott Guest Experience Monitor Live monitor Carvana Customer Voice Monitor Live monitor Gamba Osaka Fan Behavior Intelligence (Japanese) Report Carvana Multi-Source Review Analysis Report Zendesk Churn Analysis Report NBA Fan Experience Intelligence Report Home Depot Multi-Location Review Analysis Report Gamba Osaka Fan Behavior Intelligence Report AutoNation Customer Review Analysis Report American Dream Mall Visitor Analysis Report Chick-fil-A Customer Review Analysis Report Duolingo Interactive Insights App App Duolingo Mobile Review Analysis Report Eastern Bank Board Diagnostic Report Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Owlet Product Voice Intelligence Report Austin Firebirds Fan Intelligence Monitor Live monitor Austin Firebirds Fan Voice and Behavior Intelligence Report Report Planet Fitness Member Churn Causal Monitor Live monitor Planet Fitness Causal Analysis Report Rhino Causal Brief Report Dimension Labs What Your CX Tools Can’t Tell You Field brief Dimension Labs What Your CX Tools Can’t Tell You (Spanish) Field brief Dimension Labs Leadership deck Deck Dimension Labs Churn signals Chart Dimension Labs First warning Deck Dimension Labs Health score vs two sentences Chart Dimension Labs QBR deck Deck Dimension Labs Churn by tier Chart Dimension Labs Which channel saw it first Chart Dimension Labs Board readout Deck Reports · Live monitors · Apps · Decks · Charts See every artifact › Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## How the NBA Turns Fan Surveys Into Fan Intelligence URL: https://www.dimensionlabs.io/customer-stories/nba Customer stories # How the NBA Transforms Fan Survey Responses into Structured Fan Intelligence The NBA is one of the world’s most recognized professional sports leagues, operating across 30 teams and engaging hundreds of millions of fans globally. 3 min read ## About the Company The NBA is one of the world’s most recognized professional sports leagues, operating across 30 teams and engaging hundreds of millions of fans globally. During the 2024–25 season alone, more than 22.3 million fans attended games, with millions more engaging digitally through the NBA App, NBA League Pass, NBA ID, and NBA.com. As both a live entertainment brand and a global digital product organization, the NBA relies heavily on Voice of Fan surveys to measure and improve the fan experience across in-venue and digital touchpoints. With feedback spanning physical events, streaming experiences, and mobile interactions, synthesizing fan sentiment at scale became both mission-critical and increasingly complex. ## Before Dimension Labs Across the league, the NBA collected thousands of open-ended survey responses alongside structured metrics like NPS and OSAT. While these metrics provided high-level directional indicators, they lacked the context required to understand what was truly driving fan satisfaction or dissatisfaction. Open-ended responses contained the deeper “why” behind the scores, but analyzing them required manual review and annotation. Insights were compiled into static Word documents and PowerPoint decks, limiting exploration and slowing down action. Before Dimension Labs: - Open-ended survey responses were unstructured and difficult to query at scale - Manual annotation of verbatim comments took up to two months per review cycle - Analysis skewed toward negative sentiment and missed nuanced feedback - Reporting was static and offline, limiting dynamic exploration - High-level metrics like NPS and OSAT lacked contextual drivers The NBA had access to rich fan commentary, but lacked the infrastructure required to consistently transform that language into structured, comparable insight across products and fan segments. ## With Dimension Labs “Dimension Labs helped us move from static reporting to dynamic fan intelligence.”, NBA Insights Leader Using Dimension Labs, the NBA established a structured Meaning Layer for Voice of Fan data, transforming raw survey responses into governed, analytics-ready signals. Every open-ended response is now analyzed at the record level and enriched with multidimensional attributes, including themes, product drivers, emotional clusters, and predictive fan characteristics. This creates a consistent schema that allows fan feedback to be compared across products, time periods, and audience segments. With Dimension Labs, the NBA can now: - Analyze 100,000+ open-ended responses within hours instead of months - Automatically detect 30+ recurring themes and emerging topics - Map sentiment and emotion to specific product drivers such as streaming quality, navigation, and UI - Identify churn signals and upsell indicators directly from fan language - Replace static PowerPoint reporting with interactive, exploratory dashboards This foundation delivered measurable impact: - Insight review cycles reduced from ~60 days to under 6 hours - Up to 50% reduction in manual analyst workload - 100% Voice of Fan coverage across structured and open-ended feedback Fan commentary is no longer summarized and archived; it is structured, queryable, and operational. ## Use case 01 Digital Product & Streaming Experience Intelligence The NBA App and League Pass are critical digital touchpoints for fan engagement. Historically, product teams relied on aggregate metrics and sampled comments to understand performance. Dimension Labs enabled structured analysis of fan language tied directly to core experience drivers. Each survey response is enriched with signals related to: - Loading speed - Navigation - UI appeal - Content uniqueness - Feature richness - Streaming quality This structured enrichment allows the NBA to: - Identify which specific product drivers are increasing or decreasing satisfaction - Detect frustration signals tied to streaming or performance issues - Track week-over-week shifts in product sentiment - Prioritize improvements based on full-population feedback rather than sampled transcripts The shift moved teams from monitoring usage metrics to understanding the emotional and experiential drivers behind fan behavior. ## Use case 02 Predictive Fan Segmentation & Churn Intelligence Beyond descriptive analytics, the NBA uses Dimension Labs to enrich survey responses with predictive dimensions tied to fan behavior and value. Feedback is mapped into dynamic fan personas such as: - Frequent Streamer - Mobile-First Fan - Value-Seeker - Casual Viewer This predictive enrichment enables the NBA to: - Attribute feedback themes to specific fan types - Identify early indicators of churn risk before it appears in downstream metrics - Detect upsell opportunities embedded within survey commentary - Prioritize product and marketing actions by audience value Rather than treating all feedback equally, teams can now understand which fan segments are driving specific issues and which improvements will have the greatest retention or revenue impact. Fan language becomes a forward-looking input into growth strategy, not just a retrospective summary. ## Conclusion The NBA generates feedback across millions of fan interactions spanning in-venue experiences and digital platforms. By establishing Dimension Labs as the Meaning Layer for Voice of Fan data, the league transformed static survey reporting into structured, predictive fan intelligence. Open-ended feedback is now governed, comparable across products, and embedded directly into operational workflows. As fan engagement continues to expand across channels, this foundation ensures that fan language remains measurable, actionable, and strategically aligned with the league’s growth and experience objectives. ## More from customer stories How Mars Petcare Built a Meaning Layer for Customer Conversations Mars Petcare is one of the world’s largest pet nutrition and care businesses, operating as part of Mars, Incorporated, a global consumer packaged goods company with over $50B in annual revenue. How Stake.com Turns 1.5M+ Monthly Player Conversations Into Actionable Intelligence Stake.com operates one of the world’s largest global iGaming platforms, spanning online casino, live dealer, and sportsbook experiences across dozens of markets. How the Seattle Mariners Transformed Fan Feedback into Structured Intelligence Each season, the Seattle Mariners host 81 regular season home games, welcoming 31,000+ fans per night and collecting massive amounts of fan feedback through post-game satisfaction surveys. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## How Stake.com Analyzes 1.5M+ Monthly Player Conversations URL: https://www.dimensionlabs.io/customer-stories/stake Customer stories # How Stake.com Turns 1.5M+ Monthly Player Conversations Into Actionable Intelligence Stake.com operates one of the world’s largest global iGaming platforms, spanning online casino, live dealer, and sportsbook experiences across dozens of markets. 5 min read ## About the Company Stake.com operates one of the world’s largest global iGaming platforms, spanning online casino, live dealer, and sportsbook experiences across dozens of markets. The platform is crypto-native by design, supporting both digital and fiat payment flows across regions. Stake consistently ranks among the top gambling websites globally, attracting over 100 million monthly visits and serving tens of millions of registered users worldwide. Customer support is treated as a strategic investment, with a strong emphasis on live-agent conversations to preserve context, quality, and player confidence. This operating model generates an enormous volume of first-party customer language across products, markets, and customer tiers, creating both a competitive advantage and a data challenge. ## Before Dimension Labs Handling 1M+ customer conversations each month, Stake utilizes the Intercom chat platform, with the majority of these conversations being managed by live agents. These conversations span the full customer journey, including registration, KYC verification, deposits and withdrawals, gameplay and bet placement, and VIP-related inquiries. While these channels are critical to customer experience, the data they produce exists primarily as raw, unstructured text. Conversations could be reviewed individually or sampled, but analyzing them holistically across regions, products, and customer segments wasn’t possible to do at scale. Before Dimension Labs: - Unstructured Language Data: Conversations lacked schema and could not be queried consistently - Slow Insight Cycles: Manual analysis was labor-intensive and limited with most data going unanalyzed - Non-Deterministic Insights: Results varied across analysts and time periods - Siloed Systems: Intercom and CRM data remained fragmented, preventing cross-product and tier-level analysis - Lagging Indicators: Early churn and friction signals were identified too late to act Stake had access to vast amounts of customer data, but lacked the infrastructure required to consistently extract meaning from it. Decision-making at scale relied heavily on experience and intuition. As Stake described it, teams were often “putting a finger in the air and assuming what we were doing was right,” with limited ability to validate product or experience changes against real customer evidence. ## With Dimension Labs “Dimension Labs has become the default insight layer across the company.”, CX Manager, Stake.com Stake implemented Dimension Labs to transform raw customer conversations from Intercom into structured, actionable intelligence. Before they were limited to small samples of data and Intercom’s limited high-level tagging. Now leadership is able to validate product, design, and CX decisions using the intelligence derived from all of their customer interactions, reducing the risk of misaligned investments. Every support conversation is analyzed at the record level and enriched with governed dimensions that capture customer journey stage, product area, issue category, region, payment context, and customer segment. This creates a shared analytical schema that remains consistent across teams, markets, and time. With Dimension Labs, Stake can now: - Structure over 1.5M monthly customer conversations into consistent signals - Unify analysis across 4 global regions, 18 customer tiers, and various products - Enable week-over-week comparability using the same definitions - Embed conversation insights directly into operational workflows - Eliminate manual tagging and ad hoc analysis ### Organization-wide Adoption Dimension Labs has already been adopted across 7 internal departments, with dashboards tailored to each team’s product area and responsibilities. New product and design hires are onboarded into the platform as part of their first weeks, and new teams are regularly requesting access. Adoption extends beyond a single function, positioning Dimension Labs as shared analytics infrastructure for the company rather than a standalone CX tool. This adoption also reaches the executive level. Customer conversations are reviewed weekly in leadership pulse-check meetings, with insights presented by every major department. Leadership now has consistent, real-time visibility into customer friction across the business, allowing issues to be identified and prioritized within the same operating week rather than during periodic reporting cycles. “We can finally understand why customers are contacting us, not just what category their issue falls into.”, Stake.com ## Use case 01 Deposits & Withdrawals Experience Intelligence “We can move from question to insight fast enough that it actually changes what teams do that week.”, Stake.com Deposits and withdrawals are among the most critical moments in the player experience, directly impacting trust, conversion, and retention. Stake uses Dimension Labs to analyze customer conversations related to these workflows across regions and payment methods. Rather than relying on aggregate metrics or reactive escalations, Stake structures deposit and withdrawal-related conversations to quickly identify where and why issues occur. This enables Stake to: - Identify any recurring issues across regions or payment types - Compare fiat and crypto payment experiences - Detect early signs of dissatisfaction before churn appears in downstream metrics - Prioritize operational fixes based on real customer behavior By turning financial support conversations into structured intelligence, Stake gains early visibility into issues that directly affect customer conversion and retention. ## Use case 02 Customer Segmentation & VIP Intelligence “Dimension Labs lets our teams validate product and design decisions using real customer conversations.”, CX Manager, Stake.com Stake serves a wide range of customer segments, from casual players to high-value VIPs. Dimension Labs allows Stake to analyze conversations by each customer tier, rather than treating all interactions equally. Customer conversations are enriched with segmentation metadata, enabling teams to understand: - Segment-specific pain points and expectations - Questions and intent related to VIP programs and level progression - Emerging issues that disproportionately affect high-value customers This insight allows Stake to tailor experience improvements and retention strategies by customer value, ensuring that VIP experience optimization is driven by data rather than anecdote. ## Use case 03 Customer Journey & Product Experience Analysis “What used to be impossible or too manual to analyze is now available within the same week, directly inside dashboards our teams use every day.”, Stake.com Across casino gameplay and sportsbook experiences, customer friction often concentrates at specific stages of the journey. Stake uses Dimension Labs to map conversations to key journey stages, including registration, KYC, deposits, gameplay, and withdrawals. This customer journey structure enables Stake to: - See where friction concentrates across the end-to-end journey - Compare journey performance across products and regions - Track week-over-week shifts in customer issues and dissatisfaction drivers - Ground product and experience decisions in full-population data The result is a shift from anecdotal transcript reviews to a complete, journey-level view of player experience at scale. ## Conclusion “Teams don’t wait for reports anymore. They explore insights themselves and use them to set priorities.”, Stake.com Modern iGaming operators generate massive volumes of customer conversations across markets, products, and customer segments. These interactions contain critical signals related to trust, friction, and retention, but only when they are analyzed consistently and at scale. By establishing Dimension Labs as the Meaning Layer for customer language, Stake transformed raw support conversations into structured, analytics ready intelligence. As the business continues to scale globally, this foundation ensures customer experience remains measurable, comparable, and continuously improvable across all of their customer support tools and channels. ## More from customer stories How Mars Petcare Built a Meaning Layer for Customer Conversations Mars Petcare is one of the world’s largest pet nutrition and care businesses, operating as part of Mars, Incorporated, a global consumer packaged goods company with over $50B in annual revenue. How the NBA Transforms Fan Survey Responses into Structured Fan Intelligence The NBA is one of the world’s most recognized professional sports leagues, operating across 30 teams and engaging hundreds of millions of fans globally. Covered California Establishes a Structured Intelligence Layer to Scale Health Coverage Enrollment Covered California is the state’s official health insurance marketplace, providing access to affordable health coverage for millions of residents. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Concentrix: Chatbot Intelligence for Retail Journeys URL: https://www.dimensionlabs.io/customer-stories/concentrix Customer stories # Concentrix Establishes Structured Chatbot Intelligence to Optimize Retail Customer Journeys Concentrix is a leading global provider of customer experience (CX) solutions and technology, supporting some of the world’s most recognized brands. 3 min read ## About the Company Concentrix is a leading global provider of customer experience (CX) solutions and technology, supporting some of the world’s most recognized brands. As a digital CX partner, Concentrix manages and optimizes customer interactions across channels, including advanced self-service and chatbot programs. In this engagement, Concentrix partnered with Dimension Labs to improve chatbot performance for a large brick-and-mortar retail client seeking to enhance satisfaction and efficiency across its e-commerce channel. The client’s chatbot supported complex customer flows, including order status, returns and exchanges, loyalty programs, and gift registries, requiring precision, scalability, and continuous optimization. ## Before Dimension Labs While the chatbot was operational and handling high volumes of interactions, visibility into conversational performance was limited. Understanding where customers experienced friction, why escalations occurred, and which flows required refinement required extensive manual transcript review. As complexity increased across intents and user journeys, scalable analysis became increasingly difficult. Before Dimension Labs: - Chatbot transcripts existed as raw, unstructured data - Limited visibility into escalation drivers and journey drop-off points - Manual review of large transcript volumes did not scale - Difficulty isolating friction within multi-step user flows - Inconsistent measurement of NLP engine effectiveness Concentrix had access to conversational data, but lacked a structured, governed system for consistently extracting actionable insight across complex customer journeys. ## With Dimension Labs “The partnership with Dimension Labs has been instrumental in supporting our improvements in self-service performance and customer journey optimization.”, Jenny Burr, Speech Science & Analytics Concentrix implemented Dimension Labs as the structured intelligence layer for chatbot analytics, transforming raw transcripts into governed, analytics-ready signals tied directly to journey goals and performance outcomes. Every interaction is analyzed at the record level and enriched with structured dimensions capturing user intent, journey stage progression, escalation triggers, resolution outcomes, and NLP effectiveness. This created a consistent schema for analyzing conversational flows across retail use cases. With Dimension Labs, Concentrix can now: - Map user journeys to identify friction and escalation points - Measure bot health and task completion at the intent level - Isolate drop-off patterns within complex multi-step flows - Prioritize NLP improvements based on structured evidence - Embed conversational analytics into continuous improvement workflows This foundation enabled measurable impact: - Significant increase in task completion rates - Reduction in average handling time per interaction - Improved CSAT scores across e-commerce journeys - Cost savings through streamlined optimization processes Chatbot optimization shifted from periodic review to continuous, data-driven refinement. ## Use case 01 Optimizing Customer Journeys Retail customer journeys often span multiple intents and steps, particularly for order management, returns, and loyalty-related interactions. Concentrix needed precise visibility into where these journeys broke down. Dimension Labs enriches conversations with structured signals tied to: - Flow entry and exit points - Escalation or fallback triggers - Task completion status - Intent accuracy and mapping effectiveness This structured analysis enables Concentrix to: - Identify specific journey stages where friction concentrates - Quantify escalation drivers across high-volume intents - Improve resolution paths for order status and returns workflows - Optimize loyalty and gift registry flows based on real user behavior By grounding optimization in full-population conversational data, Concentrix significantly increased task completion rates and reduced handling time. ## Use case 02 NLP Performance & Continuous Improvement Framework Maintaining high-performing bots requires ongoing NLP tuning and intent refinement. Historically, reviewing training data and identifying improvement areas required time-intensive transcript sampling. Dimension Labs automatically surfaces: - Misclassified or ambiguous intents - High-frequency phrases requiring training updates - Patterns tied to unresolved or escalated interactions - Performance shifts across bot versions This structured intelligence allows Concentrix to: - Streamline NLP retraining cycles - Incorporate high-impact training phrases across bots - Maintain consistent measurement of bot accuracy - Operate a scalable continuous improvement program Conversational data becomes a governed feedback loop that systematically improves automation quality. ## Conclusion Concentrix manages complex chatbot environments across high-volume retail customer journeys. By establishing Dimension Labs as the Meaning Layer for conversational data, Concentrix transformed raw transcripts into structured, measurable intelligence. Bot performance is now analyzed at the journey level, NLP accuracy is continuously optimized, and escalation drivers are detected before they impact satisfaction metrics. This structured foundation enables scalable, cost-efficient self-service programs that deliver measurable improvements in task completion, operational efficiency, and customer experience. ## More from customer stories Intuit QuickBooks Establishes Structured Chatbot Intelligence Across 1M+ Monthly Sessions Intuit QuickBooks supports more than seven million small businesses worldwide, providing financial management tools across accounting, payroll, invoicing, and payments. Covered California Establishes a Structured Intelligence Layer to Scale Health Coverage Enrollment Covered California is the state’s official health insurance marketplace, providing access to affordable health coverage for millions of residents. How Dropbox Transformed Conversational Data into Scalable Self-Service Intelligence Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## How Dropbox Scaled Self-Service With Conversation Data URL: https://www.dimensionlabs.io/customer-stories/dropbox Customer stories # How Dropbox Transformed Conversational Data into Scalable Self-Service Intelligence Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. 3 min read ## About the Company Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. As a digital-first platform, Dropbox relies heavily on customer support interactions across live chat, email, and self-service bots to maintain a seamless user experience. As chat volume scaled rapidly and digital engagement increased, self-service automation became a strategic priority. Optimizing chatbot performance and customer journey flows required more than high-level metrics, it required deep visibility into the language and intent behind every customer interaction. ## Before Dimension Labs Dropbox managed a growing volume of customer service requests across live agents and automated bots. While bots were central to scaling support, understanding how and why conversations succeeded or failed required manual analysis and fragmented reporting. Customer journey reviews often involved manually reviewing conversation diagrams and transcripts. Insights were reactive, time-consuming to generate, and difficult to operationalize across teams. Before Dimension Labs: - Bot conversations existed primarily as raw, unstructured transcripts - Manual journey diagram reviews slowed optimization cycles - Limited visibility into conversational flows and failure points - Reactive reporting tied to outcomes rather than root causes - Difficulty scaling automation to more complex service topics Dropbox had access to vast conversational data, but lacked the structured intelligence needed to consistently optimize and expand self-service automation. ## With Dimension Labs “Dimension Labs is key for us to both report outcomes to the business and prioritize where to dedicate resources to enhancing our customer journey.”, Maureen O’Sullivan, Program Manager at Dropbox Dropbox implemented Dimension Labs as the structured intelligence layer for conversational support data, transforming raw bot transcripts into governed, analytics-ready signals. Every chatbot interaction is analyzed at the record level and enriched with structured attributes that capture intent, journey stage, resolution outcome, escalation points, and friction drivers. This created a consistent analytical schema across bot and live-agent interactions. With Dimension Labs, Dropbox can now: - Structure and analyze full-population bot conversations at scale - Identify top support intents and unresolved interaction drivers - Map conversation flows to detect drop-off and escalation patterns - Align bot performance directly with operational KPIs - Prioritize automation opportunities based on structured evidence This foundation delivered measurable impact: - 43% reduction in unhandled bot conversations - 3× increase in self-service adoption for “Account upgrade” topics - 15× increase in chat volume managed without proportional cost growth Conversational data moved from reactive transcript review to proactive journey optimization. ## Use case 01 Self-Service Optimization & Escalation Reduction Self-service bots play a central role in Dropbox’s ability to scale customer support efficiently. However, optimizing bot performance required understanding exactly where friction occurred in the conversation journey. Dimension Labs enriches each conversation with structured signals capturing: - Customer intent and request category - Journey stage progression - Resolution or escalation outcome - Common phrases associated with failure or confusion This structured intelligence enables Dropbox to: - Identify which topics most frequently lead to unresolved interactions - Detect patterns in conversational drop-off or escalation - Redesign bot flows based on full-population data rather than sampled transcripts - Align automation improvements with cost reduction goals The result was a 43% reduction in unresolved bot conversations, even as total chat volume increased 15×. Expansion into Complex Service Automation As Dropbox matured its self-service strategy, the next step was expanding automation into more complex service areas such as implementation and onboarding. Dimension Labs provided structured visibility into emerging support themes and high-frequency requests, enabling teams to confidently automate additional use cases. With enriched conversational data, Dropbox could: - Identify high-impact automation opportunities - Quantify demand for specific service topics - Launch specialized bots for targeted support - Automate onboarding workflows within one year This structured expansion led to a 3× increase in self-service adoption for account upgrades and accelerated automation across more advanced service categories. Conversational intelligence became a forward-looking input into product and support strategy, not just a performance report. ## Conclusion Dropbox generates millions of customer interactions across chat and self-service channels. By establishing Dimension Labs as the Meaning Layer for conversational data, Dropbox transformed raw bot transcripts into structured, actionable intelligence. Conversational flows are now measurable, comparable, and continuously optimized against operational goals. As digital service continues to scale, this structured foundation ensures automation decisions are grounded in full-population customer evidence, driving efficiency, cost reduction, and improved customer experience. ## More from customer stories Intuit QuickBooks Establishes Structured Chatbot Intelligence Across 1M+ Monthly Sessions Intuit QuickBooks supports more than seven million small businesses worldwide, providing financial management tools across accounting, payroll, invoicing, and payments. Ylopo Establishes Structured Conversation Intelligence to Scale Real Estate Lead Conversion Ylopo is a digital marketing and lead generation platform built specifically for real estate businesses. Travelers Establishes a Unified Meaning Layer Across Millions of Customer Interactions Travelers is a Fortune 100 insurance provider serving millions of policyholders across personal, commercial, and specialty lines of business. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## How Mars Petcare Built a Meaning Layer for Conversations URL: https://www.dimensionlabs.io/customer-stories/mars Customer stories # How Mars Petcare Built a Meaning Layer for Customer Conversations Mars Petcare is one of the world’s largest pet nutrition and care businesses, operating as part of Mars, Incorporated, a global consumer packaged goods company with over $50B in annual revenue. 4 min read ## About the Company Mars Petcare is one of the world’s largest pet nutrition and care businesses, operating as part of Mars, Incorporated, a global consumer packaged goods company with over $50B in annual revenue. Mars Petcare manages dozens of well-known brands including Pedigree, Whiskas, Royal Canin, IAMS, Cesar, Sheba, and Banfield, serving millions of pet owners globally across food, veterinary, and direct-to-consumer channels. As a modern CPG and retail-driven organization, Mars Petcare operates at a massive scale across regions, languages, and product lines. Digital customer engagement plays a critical role in how the company supports customers, gathers feedback, and improves product and experience quality across markets. ## Before Dimension Labs Across its business lines, Mars Petcare generates large volumes of customer language through Ada chatbot conversations, customer support calls, and customer surveys. These channels work well for engaging and supporting customers, but the data they produce exists as raw, unstructured text. Conversations could be manually reviewed or sampled, but they were difficult to analyze at scale. Before Dimension Labs: - Chatbot transcripts and survey responses were unstructured and siloed - Analysis relied on manual review, keyword lists, and subjective sentiment scoring - Insights varied by team and market with no consistent logic - Two years of internal BI development failed to deliver scalable language analytics Mars had access to the raw data, but not infrastructure required to extract meaning and insights. ## With Dimension Labs “Having customer language structured and reusable is foundational for how we operate.”, Digital Experience & Analytics Leader, MARS Petcare Mars Petcare implemented Dimension Labs as the Meaning Layer for customer language, transforming raw conversations from Ada chatbots, support interactions, and surveys into structured, analytics-ready data. Every conversation is analyzed at the record level and enriched with governed dimensions, creating a shared data schema for analysis across brands, regions, and time. “What used to take days of manual work per region is now available instantly.”, Customer Analytics Leader, MARS Petcare With Dimension Labs, Mars Petcare: - Structured every customer conversation into consistent signals - Unified chatbot, survey, and support analysis under one logic - Enabled cross-brand and cross-market comparability - Embedded conversation insights into weekly operating workflows - Removed manual review and ad hoc tagging This foundation delivered measurable outcomes: - 50% fewer chatbot escalations by fixing the flows that drove handoff - 18% higher engagement through targeted optimization - ~3 FTEs eliminated by automating conversation analysis - Faster insight cycles , shifting from monthly to weekly reporting Customer language became a governed, operational data asset, powering continuous improvement across support performance, product feedback, and customer experience. ## Use case 01 Chatbot Performance and Escalation Intelligence “We now have consistent visibility across chatbot, chat, and survey data in a way we never had before.”, CX & Insights Executive, MARS Petcare Mars uses Dimension Labs to analyze customer conversations generated by the Ada chatbot across multiple brands, regions, and bots. Instead of relying on high-level chatbot metrics like deflection rates or conversation volume, Mars structures every conversation into consistent, analytics-ready signals that explain what actually happened during the interaction. This includes: - Why the customer reached out - What topic or category the conversation falls into - Whether an issue occurred and how it was handled - Whether the conversation escalated to a human - Whether the interaction was successfully resolved With this structure in place, Mars can: - Compare chatbot performance across brands, regions, and markets using the same definitions - See which topics most often lead to escalation or drop-off - Identify gaps in chatbot coverage before they show up in CSAT or NPS - Prioritize improvements based on real customer behavior, not sampled transcripts This shift allowed Mars to move from monitoring chatbot usage to actively improving chatbot effectiveness, contributing to a 50% reduction in escalations and a 18% increase in engagement within six months. ## Use case 02 Loyalty, Retention, and Marketing Enablement “Dimension Labs has enabled us to move beyond anecdotal feedback and into measurable, operational insight.”, CX & Insights Executive, MARS Petcare Beyond support and feedback, Mars uses Dimension Labs to extract early indicators related to loyalty, retention, and upsell opportunities directly from customer language. Customer conversations are analyzed to surface signals such as: - Signs of dissatisfaction or risk of disengagement - Loyalty and membership-related intent - Opportunities for cross-sell or upsell - Unmet expectations expressed during support interactions Because these signals come directly from live conversations, they often appear earlier than transactional data or survey results. This allows Mars to: - Identify customers at risk before churn shows up in downstream metrics - Spot missed upsell opportunities embedded in support conversations - Align customer experience and marketing efforts around the same signals - Use conversation-level insight as an input into personalization and retention strategies Customer language becomes a forward-looking input into growth and loyalty decisions, not just a retrospective explanation. ## Use case 03 Product and Experience Feedback at Scale “It allows teams across the organization to work from the same data without rebuilding logic or relying on manual effort.”, Digital Experience & Analytics Leader, MARS Petcare Mars receives large volumes of open-ended feedback through chatbot conversations and surveys. Historically, this feedback was reviewed manually, summarized in decks, or sampled sporadically, making it difficult to spot trends early or compare insights across brands. Dimension Labs transforms this free-text feedback into structured signals that can be analyzed alongside other business data. At a high level, Mars structures feedback around: - Product and brand mentions - Common themes and topics - Experience friction and confusion points - Sentiment and emotional tone This enables Mars to: - Quantify feedback that was previously anecdotal - Track recurring product and experience issues across brands - Compare feedback trends over time, including before and after launches or rebrands - Ground product and content decisions in actual customer language at scale Rather than relying on word clouds or summaries, teams work with structured feedback data that reflects the full population of customer conversations. ## Conclusion Mars Petcare generates millions of customer conversations across brands, channels, and markets. By establishing Dimension Labs as the Meaning Layer for customer language, the company created a durable foundation for turning raw conversations into structured, analytics-ready signals. As digital engagement continues to scale, this Meaning Layer ensures customer language remains consistently understood, comparable over time, and ready to drive future improvements across products, experiences, and growth. ## More from customer stories How Stake.com Turns 1.5M+ Monthly Player Conversations Into Actionable Intelligence Stake.com operates one of the world’s largest global iGaming platforms, spanning online casino, live dealer, and sportsbook experiences across dozens of markets. How the NBA Transforms Fan Survey Responses into Structured Fan Intelligence The NBA is one of the world’s most recognized professional sports leagues, operating across 30 teams and engaging hundreds of millions of fans globally. How Dropbox Transformed Conversational Data into Scalable Self-Service Intelligence Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Travelers: 600% ROI From a Unified Conversation Layer URL: https://www.dimensionlabs.io/customer-stories/travelers Customer stories # Travelers Establishes a Unified Meaning Layer Across Millions of Customer Interactions Travelers is a Fortune 100 insurance provider serving millions of policyholders across personal, commercial, and specialty lines of business. 3 min read ## About the Company Travelers is a Fortune 100 insurance provider serving millions of policyholders across personal, commercial, and specialty lines of business. As a large-scale enterprise insurer, Travelers manages millions of customer interactions each year across voice, chat, email, SMS, and self-service channels. Delivering consistent, low-effort service across this omnichannel ecosystem is critical to both customer satisfaction and operational efficiency. As interaction volume grew, Travelers needed a way to unify fragmented conversational data into a single, governed intelligence layer capable of identifying automation gaps, customer friction, and performance inefficiencies at scale. ## Before Dimension Labs Customer journeys were distributed across multiple touchpoints, often requiring policyholders to repeat information as they moved between digital and human channels. While operational data existed across chat, voice, and email systems, there was no unified framework for analyzing those interactions cohesively. Traditional BI systems could report on structured metrics, but they could not connect unstructured conversational data into a consistent, cross-channel view of customer effort and root causes. Before Dimension Labs: - Omnichannel conversations existed in siloed systems - No unified schema across chat, voice, email, SMS, and bots - Limited visibility into cross-channel friction and repeat contacts - Rising cost-to-serve driven by escalations and manual processes - Inability to consistently measure customer effort drivers Travelers had access to millions of customer interactions, but lacked the infrastructure required to extract structured, comparable intelligence across the full policyholder journey. ## With Dimension Labs “Dimension Labs gives us that 50,000-foot view we needed to figure out what’s going on out there.”, Data Science Business Partner, Travelers Travelers implemented Dimension Labs as the Meaning Layer for omnichannel conversational data, transforming raw transcripts into governed, analytics-ready signals. Every interaction across voice, chat, email, SMS, and bots is analyzed at the record level and enriched with structured dimensions capturing intent, sentiment, customer effort, journey stage, resolution outcome, and escalation triggers. This created a consistent analytical schema across lines of business, regions, and service teams. With Dimension Labs, Travelers can now: - Unify omnichannel conversational data into a single intelligence layer - Map customer journeys across channel handoffs - Identify root causes of customer effort and repeat contacts - Compare performance across business units and regions - Maintain governed pipelines aligned with HIPAA, GDPR, and enterprise policies This foundation delivered measurable financial impact: - 600% ROI within 18 months - $3.13M in total savings - $2M in avoided BI redevelopment costs - $1.13M in CX automation and manual process reduction Customer conversations shifted from fragmented logs to structured operational intelligence. ## Use case 01 Omnichannel Journey & Customer Effort Intelligence Policyholders often move between chat, voice, and email to resolve issues. Without structured visibility across these handoffs, identifying sources of friction was difficult. Dimension Labs enriches interactions with structured journey attributes, enabling Travelers to: - Visualize cross-channel customer flows - Identify drop-off and escalation points - Detect high-friction categories driving repeat contacts - Quantify drivers behind customer effort score (CES) trends This structured journey intelligence led to: - Reduced repeat contacts - Improved customer effort scores - Faster resolution across channels By grounding improvements in full-population conversational data, Travelers reduced cost-to-serve while enhancing service consistency. ## Use case 02 Automation Optimization & Agent Productivity Reducing escalations and improving agent productivity were central operational priorities. Dimension Labs surfaces high-impact automation opportunities by identifying: - Intents frequently escalated from virtual assistants - Root causes behind failed self-service attempts - Training and process gaps contributing to agent workload - Recurring patterns across service lines This structured insight enables Travelers to: - Continuously retrain and optimize chatbot flows - Prioritize automation improvements with measurable ROI - Reduce manual triage and repetitive data gathering - Equip managers with dashboards to proactively identify coaching needs As automation improved, agent productivity increased and burnout decreased, allowing teams to focus on higher-value cases. ## Conclusion Travelers manages millions of customer interactions across a complex omnichannel environment. By establishing Dimension Labs as the Meaning Layer for conversational data, the company transformed fragmented transcripts into structured, governed operational intelligence. Customer effort drivers are now measurable across channels, automation opportunities are prioritized based on evidence, and BI rebuild costs are avoided through scalable, purpose-built analytics. This structured foundation enables Travelers to continuously reduce cost-to-serve while delivering a more seamless, consistent policyholder experience. ## More from customer stories Covered California Establishes a Structured Intelligence Layer to Scale Health Coverage Enrollment Covered California is the state’s official health insurance marketplace, providing access to affordable health coverage for millions of residents. How Dropbox Transformed Conversational Data into Scalable Self-Service Intelligence Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. Intuit QuickBooks Establishes Structured Chatbot Intelligence Across 1M+ Monthly Sessions Intuit QuickBooks supports more than seven million small businesses worldwide, providing financial management tools across accounting, payroll, invoicing, and payments. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## American Red Cross: Real-Time Insight for Disaster Response URL: https://www.dimensionlabs.io/customer-stories/arc Customer stories # The American Red Cross Establishes a Real-Time Meaning Layer for Disaster Response The American Red Cross (ARC) is one of the world’s leading humanitarian organizations, providing emergency assistance, disaster relief, and preparedness education across the United States. 3 min read ## About the Company The American Red Cross (ARC) is one of the world’s leading humanitarian organizations, providing emergency assistance, disaster relief, and preparedness education across the United States. During natural disasters and crisis events, the organization must rapidly scale support to serve vulnerable communities seeking shelter, supplies, and critical information. To meet this demand, the ARC relies on digital engagement channels, including chatbots, to provide immediate guidance and connect individuals to essential resources. During high-volume disaster periods, these conversational channels become frontline infrastructure for emergency response. ## Before Dimension Labs During disasters, chatbot interactions surge dramatically as individuals seek shelter locations, aid resources, and emergency guidance. While these bots were operationally critical, the ARC lacked structured visibility into how conversations were unfolding in real time. Chat transcripts required manual review, making it difficult to quickly detect emerging issues, identify failed intents, or retrain natural language models at scale. Before Dimension Labs: - Chatbot conversations existed as raw, unstructured transcripts - Manual transcript review could not scale during disaster surges - Limited real-time visibility into user behavior and emerging needs - High fallback and not-handled intent, particularly in Spanish-language interactions - No scalable system to generate structured training data for NLU improvement The ARC had access to conversational data, but lacked the infrastructure required to consistently extract insight and improve chatbot performance during critical response windows. ## With Dimension Labs “When disaster strikes, we have to quickly interpret conversations at scale and adjust to new requests and topics. Dimension Labs gives us the insights to provide the support needed to those who need it most.”, Hailey Burgess, Product Manager The American Red Cross implemented Dimension Labs as the structured intelligence layer for chatbot interactions, transforming raw transcripts into governed, analytics-ready signals. Every chatbot interaction is analyzed at the record level and enriched with structured dimensions capturing user intent, topic clusters, resolution outcomes, language, and journey stage. This creates a shared schema that enables consistent monitoring during both routine operations and high-volume emergency events. With Dimension Labs, ARC can now: - Quantify user behavior and emerging topics during disaster surges - Monitor fallback and not-handled intent in real time - Extract structured training data for NLU retraining - Proactively oversee chatbot performance across internally managed and contractor-managed channels - Shift reporting from transcript summaries to journey-level intelligence Chatbot conversations are no longer reactive support logs, they are structured operational signals. ## Use case 01 Real-Time Disaster Response Intelligence During events such as hurricanes and large-scale emergencies, chatbot volume increases rapidly. The ARC must quickly identify what individuals are asking and where support gaps exist. Dimension Labs enriches conversations with structured topic modeling and behavioral clustering to surface: - Requests related to shelter location and availability - Questions about essential supplies and relief resources - Emerging themes unique to specific disaster events - Escalation or confusion patterns during peak demand This structured intelligence enables ARC to: - Detect new or shifting user needs in real time - Adjust chatbot flows to better guide individuals to resources - Improve disaster response goal attainment - Ensure individuals seeking shelter or assistance can successfully find it The result is faster identification of friction and stronger alignment between digital channels and on-the-ground response efforts. ## Use case 02 NLU Optimization & Multilingual Performance Improving natural language understanding (NLU) models is essential for chatbot effectiveness, especially across diverse populations. Dimension Labs automatically identifies unresolved intents, fallback patterns, and novel training examples directly from live conversations. These structured signals enable: - Scalable generation of retraining datasets - Faster iteration cycles for NLU improvement - Targeted optimization of Spanish-language interactions - Reduced not-handled intent across high-priority flows By lowering fallback rates, particularly in Spanish-language conversations, the ARC improved access to critical information for diverse communities during emergencies. Chatbot retraining shifted from manual transcript review to structured, continuous improvement. ## Conclusion The American Red Cross generates high volumes of chatbot interactions during moments when speed and accuracy matter most. By establishing Dimension Labs as the Meaning Layer for conversational data, the ARC transformed raw transcripts into structured, operational intelligence. Chatbot performance is now measurable in real time, comparable across languages, and continuously optimized to support disaster response objectives. As emergency response demands continue to evolve, this structured foundation ensures digital engagement channels remain reliable, scalable, and aligned with the ARC’s mission to serve communities in need. ## More from customer stories Covered California Establishes a Structured Intelligence Layer to Scale Health Coverage Enrollment Covered California is the state’s official health insurance marketplace, providing access to affordable health coverage for millions of residents. How Dropbox Transformed Conversational Data into Scalable Self-Service Intelligence Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. Concentrix Establishes Structured Chatbot Intelligence to Optimize Retail Customer Journeys Concentrix is a leading global provider of customer experience (CX) solutions and technology, supporting some of the world’s most recognized brands. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Covered California: Scaling Health Coverage Enrollment URL: https://www.dimensionlabs.io/customer-stories/covered-california Customer stories # Covered California Establishes a Structured Intelligence Layer to Scale Health Coverage Enrollment Covered California is the state’s official health insurance marketplace, providing access to affordable health coverage for millions of residents. 3 min read ## About the Company Covered California is the state’s official health insurance marketplace, providing access to affordable health coverage for millions of residents. The organization supports new and existing policyholders across complex eligibility, enrollment, and subsidy qualification processes. As a public service entity, Covered California must deliver accurate, timely guidance across English and Spanish channels, especially during open enrollment and high-demand periods. Automating common service requests through chatbots became essential to reducing strain on live agents while maintaining accessibility and compliance. ## Before Dimension Labs Covered California managed large volumes of customer inquiries across chatbot, live agent, and email channels. While automation was a priority, the eligibility and application processes were complex, requiring multi-step verification and qualification workflows. Chatbot interactions were difficult to analyze at scale. Friction points in the enrollment journey were often identified only after escalations occurred, and real-time visibility into drop-offs was limited. Before Dimension Labs: - Chatbot transcripts existed as unstructured data without consistent journey mapping - Friction and drop-off points were difficult to isolate - High escalation volume for complex eligibility questions - Limited real-time insight into consumer behavior across English and Spanish flows - Manual effort required to evaluate and improve new self-service use cases Covered California had access to tens of thousands of chatbot conversations, but lacked the structured framework needed to systematically optimize the enrollment journey. ## With Dimension Labs “You’re going in blind without Dimension Labs.”, CX Manager Covered California implemented Dimension Labs as the Meaning Layer for chatbot interactions, transforming raw transcripts into structured, analytics-ready journey intelligence. Every chatbot session is analyzed at the record level and enriched with structured dimensions capturing journey stage, intent, escalation triggers, containment outcome, and drop-off location. This created a consistent schema for measuring enrollment flow performance and identifying friction across both English and Spanish interactions. With Dimension Labs, Covered California can now: - Map full consumer journeys across chatbot flows - Identify precise drop-off and escalation drivers - Monitor containment rates for key intents - Launch and measure new self-service use cases with structured evidence - Implement daily journey improvements based on real-time insights This foundation delivered measurable operational improvements: - New self-service use case launched in 2 weeks - 25% reduction in resources required for implementation - Nearly 100% containment rate for key intents - 5× increase in chatbot self-service adoption Chatbot interactions shifted from reactive transcript review to structured enrollment intelligence. ## Use case 01 Enrollment Journey & Drop-Off Optimization Eligibility and application submission require multi-step verification processes that can create friction for consumers. Dimension Labs enriches chatbot interactions with structured journey attributes tied to: - Entry and exit points within the enrollment flow - Points of confusion during eligibility qualification - Escalation triggers to live agents - Language-specific performance differences This structured analysis enables Covered California to: - Detect where consumers abandon applications - Refine conversation design to reduce drop-offs - Improve clarity in subsidy qualification steps - Optimize English and Spanish flows independently By grounding improvements in full-population conversational data, Covered California reduced friction and increased self-service completion rates. ## Use case 02 Containment & Scalable Automation Reducing escalation to live agents is critical for maintaining service levels during peak enrollment periods. Dimension Labs identifies high-frequency escalation drivers and surfaces: - Unanswered or ambiguous questions - Intents requiring additional training or redesign - Opportunities to expand automation coverage - Gaps in multilingual support This structured intelligence allows Covered California to: - Improve containment rates for priority intents - Rapidly retrain and refine chatbot responses - Expand automation coverage with measurable confidence - Operate a continuous improvement cycle driven by daily performance data As containment improved, agent workload decreased and citizens were guided more efficiently toward successful enrollment. ## Conclusion Covered California manages tens of thousands of enrollment-related conversations across complex eligibility workflows. By establishing Dimension Labs as the Meaning Layer for chatbot data, the organization transformed fragmented transcripts into structured, journey-level intelligence. Enrollment flows are now measurable, drop-offs are proactively addressed, and containment performance is optimized across languages. This structured foundation enables Covered California to scale automation responsibly while improving access to health coverage for the communities it serves. ## More from customer stories Travelers Establishes a Unified Meaning Layer Across Millions of Customer Interactions Travelers is a Fortune 100 insurance provider serving millions of policyholders across personal, commercial, and specialty lines of business. How Dropbox Transformed Conversational Data into Scalable Self-Service Intelligence Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. Concentrix Establishes Structured Chatbot Intelligence to Optimize Retail Customer Journeys Concentrix is a leading global provider of customer experience (CX) solutions and technology, supporting some of the world’s most recognized brands. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Intuit QuickBooks: Chatbot Intelligence at 1M+ Sessions URL: https://www.dimensionlabs.io/customer-stories/intuit Customer stories # Intuit QuickBooks Establishes Structured Chatbot Intelligence Across 1M+ Monthly Sessions Intuit QuickBooks supports more than seven million small businesses worldwide, providing financial management tools across accounting, payroll, invoicing, and payments. 3 min read ## About the Company Intuit QuickBooks supports more than seven million small businesses worldwide, providing financial management tools across accounting, payroll, invoicing, and payments. As part of its digital support strategy, QuickBooks relies heavily on the QuickBooks Assistant chatbot to guide users through complex workflows and product questions. Handling over one million chatbot sessions per month across 50+ bots, the Assistant plays a central role in customer self-service. As usage scaled, ensuring accuracy, minimizing escalation, and improving intent performance became essential to maintaining operational efficiency and customer satisfaction. ## Before Dimension Labs The QuickBooks Assistant generated high volumes of conversational data, but interactions existed primarily as raw transcripts without a consistent analytical framework. Mishandled and fallback intents led to inaccurate responses and unnecessary human escalations. While aggregate metrics indicated performance issues, the team lacked structured visibility into the specific drivers behind those failures. Before Dimension Labs: - Chatbot transcripts existed as unstructured data without consistent schema - High fallback and not-handled intent rates - Limited visibility into root causes of escalation - Difficulty mapping conversational flows across 50+ bots - Inability to systematically prioritize NLP and journey improvements QuickBooks had access to vast conversational data, but lacked the infrastructure required to consistently extract root causes and optimize chatbot performance at scale. ## With Dimension Labs QuickBooks implemented Dimension Labs as the structured intelligence layer for the QuickBooks Assistant, transforming raw chatbot transcripts into governed, analytics-ready signals. Every interaction is analyzed at the record level and enriched with structured dimensions capturing intent classification, fallback drivers, journey progression, escalation triggers, and resolution outcomes. This created a shared schema across bots and product areas, enabling consistent measurement and optimization. With Dimension Labs, QuickBooks can now: - Unify chatbot transcripts across 50+ bots into a centralized analytical layer - Map conversational flows to detect friction and drop-off patterns - Identify root causes of mishandled and fallback intents - Prioritize NLP improvements based on structured evidence - Extract targeted insights using custom prompts and AI enrichment This foundation delivered measurable impact: - 35.3% reduction in fallback (not handled) intent rate - 57% reduction in human escalations - Improved chatbot efficiency even as usage volume continued to scale Conversational data shifted from reactive transcript review to structured performance intelligence. ## Use case 01 Fallback & Intent Optimization at Scale Managing fallback and mishandled intents across 50+ bots requires precise visibility into conversational failures. Dimension Labs enriches each session with structured attributes tied to: - Fallback triggers and unresolved intents - Customer confusion points within conversation flows - Journey stages where escalation is most likely - Recurring patterns across product categories This structured analysis enables QuickBooks to: - Isolate root causes of high fallback rates - Improve intent mapping and training data coverage - Redesign conversation flows based on full-population data - Reduce unnecessary handoffs to live agents The result was a 35.3% reduction in fallback intent and significantly improved chatbot accuracy. ## Use case 02 Escalation Reduction & Self-Service Efficiency Reducing human escalation is critical to operational efficiency in high-volume environments. Dimension Labs provides journey-level visibility into escalation drivers, allowing QuickBooks to: - Identify specific interaction patterns that trigger live-agent handoff - Quantify which product flows create the most friction - Optimize conversation paths to resolve issues within self-service - Align chatbot improvements directly with support cost metrics By grounding optimization decisions in structured conversational intelligence, QuickBooks reduced human escalations by 57%, improving both customer experience and support efficiency. The success of this structured approach led to expansion of the Assistant into QuickBooks Online, extending scalable automation across additional product surfaces. ## Conclusion The QuickBooks Assistant handles over one million sessions per month across a complex multi-bot environment. By establishing Dimension Labs as the Meaning Layer for chatbot interactions, QuickBooks transformed raw transcripts into structured, measurable intelligence. Fallback drivers, escalation patterns, and conversational friction are now consistently identified and prioritized. As chatbot usage continues to grow, this structured foundation ensures performance improvements are scalable, governed, and directly aligned with customer experience and operational efficiency goals. ## More from customer stories How Dropbox Transformed Conversational Data into Scalable Self-Service Intelligence Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. Concentrix Establishes Structured Chatbot Intelligence to Optimize Retail Customer Journeys Concentrix is a leading global provider of customer experience (CX) solutions and technology, supporting some of the world’s most recognized brands. Travelers Establishes a Unified Meaning Layer Across Millions of Customer Interactions Travelers is a Fortune 100 insurance provider serving millions of policyholders across personal, commercial, and specialty lines of business. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## How the Seattle Mariners Analyze Fan Feedback URL: https://www.dimensionlabs.io/customer-stories/seattle-mariners Customer stories # How the Seattle Mariners Transformed Fan Feedback into Structured Intelligence Each season, the Seattle Mariners host 81 regular season home games, welcoming 31,000+ fans per night and collecting massive amounts of fan feedback through post-game satisfaction surveys. 5 min read ## About the Company Each season, the Seattle Mariners host 81 regular season home games, welcoming 31,000+ fans per night and collecting massive amounts of fan feedback through post-game satisfaction surveys. Over the course of a season, that results in hundreds of thousands of structured ratings and open-ended responses describing every aspect of the ballpark experience, from concessions and crowd flow to in-game entertainment and overall satisfaction. The volume of feedback was never the issue. The challenge was translating that scale of fan language into clear, actionable intelligence that could explain performance shifts from homestand to homestand. Recognizing that the real insight lived inside open-ended responses, the Mariners partnered with Dimension Labs to transform unstructured fan comments into structured, queryable data. With the platform in place, they made a strategic shift in their survey program, leaning more heavily into open-ended feedback and capturing richer qualitative input, confident they now had the infrastructure to analyze it consistently at scale. What began as a reporting challenge evolved into a broader fan intelligence strategy, redefining how the organization collects, structures, and acts on feedback across an entire season. ## Before Dimension Labs “We have all these scores and numerics… we really don’t understand why they go up and down.”, Chris Kennedy, SVP of Business Strategy & Analytics Prior to Dimension Labs, the Mariners relied on a combination of manual review and basic modeling to analyze open-text survey responses. The team could read comments and observe when satisfaction scores moved from game to game or homestand to homestand. But understanding why those shifts occurred was far less clear. Key limitations included: - Manual labeling that required significant analyst time across the season - Inconsistent categorization of themes - No scalable way to quantify which issues were widespread versus isolated - Difficulty distinguishing baseline complaints from true performance drivers - Internal debate driven by anecdotes rather than evidence Even transitioning from analyst labeling to model-assisted labeling still lacked the nuance needed to analyze complex comments that included both praise and friction in the same response. The insight existed, but it was expensive, inconsistent, and difficult to scale across all 81 home games. ## With Dimension Labs The Mariners transformed open-ended fan feedback from a manual reporting exercise into a structured intelligence system operating across all 81 home games. Where analysis was once manual and inconsistent, the team now automatically processes hundreds of thousands of open-text responses using a consistent, multi-dimensional framework. Each comment is structured across category, issue and success drivers, specific experience aspects, and predicted satisfaction impact, turning fan language into measurable signals tied directly to performance. The shift directly addressed the limitations they faced before: - 100% of open-text responses are analyzed consistently at scale across the full season. - Categories, issue drivers, and experience aspects are structured using predefined and dynamically generated labels. - Statistically significant movement between games and homestands can be isolated with confidence. - High-volume complaints are separated from drivers that materially impact satisfaction. - Fan language is directly connected to movements in overall satisfaction and entertainment scores. Implementing Dimension Labs also enabled a strategic evolution in the Mariners’ survey program. Confident that open-ended feedback could now be structured and quantified, the organization leaned further into qualitative questions to capture richer insight. Open-text responses are no longer supplemental to structured ratings, they are the central context to understanding fan feedback. What was once reactive reporting has become a scalable intelligence layer that informs operational and strategic decisions throughout the season. ## Use case 01 Explaining and Validating KPI Movement “Dimension Labs unlocks the ability to maximize the potential usage of a survey.”, Kobe Sarausad, Analyst of Revenue Insights The Mariners track structured KPIs such as overall satisfaction and in-game entertainment score. Previously, score movement was visible but not clearly attributable. Now, every open-ended comment is analyzed across multiple dimensions, including: - Category (e.g., concessions, venue satisfaction, game presentation) - Issue reason and success reason - Specific aspects such as food quality, drink variety, noise levels, and exit flow - Predicted satisfaction impact This enables the team to connect unstructured fan language directly to KPI changes. With Dimension Labs, they can now: - Quantify which categories correlate with score movement - Identify the top drivers per homestand - Filter out minor fluctuations using statistical significance testing - Validate predicted satisfaction against actual ratings Across a full season of 81 home games, this means: - 100% of open-text responses analyzed consistently - Clear attribution of score movement - Reduced internal speculation - Faster post-game reporting Rather than debating anecdotes, the team can state with confidence which 2–3 experience variables influenced performance shifts. ## Use case 02 Separating Noise from True Impact Not all high-volume feedback drives measurable outcomes. As Chris noted, “They’re the loudest people, but it’s not the most impactful thing.” Dimension Labs enables the Mariners to distinguish between: - Recurring baseline complaints - Minor statistical variation - True anomalies that cross meaningful thresholds Through significance testing and trend analysis, the team avoids reacting to every 3–5% fluctuation. Instead, they focus on shifts that materially influence satisfaction and experience metrics. Across an 81-game season, this precision prevents operational churn and ensures resources are directed toward issues that genuinely move fan perception. The result is smarter prioritization, better resource allocation, and clearer ownership. ## Use case 03 Aligning Departments Around Structured Insight “Ultimately, it allows us to quantify the qualitative and give context and reasoning to KPI movements.”, Seattle Mariners Fan experience spans multiple internal teams: - Ballpark operations - Concessions and food & beverage - Marketing and communications - Game presentation - Executive leadership Each group requires concise, outcome-focused insight rather than raw comment feeds. Dimension Labs enables the Mariners to deliver structured summaries aligned to departmental KPIs. For example: - Concessions leaders can quantify how food quality or drink variety impacts satisfaction. - Marketing teams can isolate drivers influencing entertainment scores. - Operations teams can identify crowd flow or exit friction affecting experience. Because the platform analyzes open-ended responses across predefined aspects, stakeholders receive targeted intelligence without requiring additional survey questions. This has strengthened cross-functional alignment and reduced internal debate over what matters most. ## Conclusion For the Seattle Mariners, fan feedback is no longer a reporting artifact but an operating system for experience strategy. By restructuring their survey program to capture richer open-ended insight and deploying Dimension Labs to structure that language at scale, the organization transformed hundreds of thousands of fan comments into a continuous signal layer. What once surfaced as disconnected scores and anecdotes is now a measurable understanding of what drives satisfaction, fan engagement, and return intent. At an executive level, this shift changes the conversation from “How did we perform?” to “What is shaping the fan experience, and where should we invest?” The Mariners have moved from reactive reporting to proactive experience management, aligning operations, marketing, and leadership around evidence rather than opinion. In a league where fan loyalty and in-venue differentiation matter, structured intelligence from unstructured feedback becomes not just a tool, but a strategic data advantage. ## More from customer stories How the NBA Transforms Fan Survey Responses into Structured Fan Intelligence The NBA is one of the world’s most recognized professional sports leagues, operating across 30 teams and engaging hundreds of millions of fans globally. How Mars Petcare Built a Meaning Layer for Customer Conversations Mars Petcare is one of the world’s largest pet nutrition and care businesses, operating as part of Mars, Incorporated, a global consumer packaged goods company with over $50B in annual revenue. How Stake.com Turns 1.5M+ Monthly Player Conversations Into Actionable Intelligence Stake.com operates one of the world’s largest global iGaming platforms, spanning online casino, live dealer, and sportsbook experiences across dozens of markets. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Ylopo: Conversation Intelligence for Real Estate Leads URL: https://www.dimensionlabs.io/customer-stories/ylopo Customer stories # Ylopo Establishes Structured Conversation Intelligence to Scale Real Estate Lead Conversion Ylopo is a digital marketing and lead generation platform built specifically for real estate businesses. 3 min read ## About the Company Ylopo is a digital marketing and lead generation platform built specifically for real estate businesses. The company enables realtors and brokerages to capture, nurture, and convert inbound leads through automated SMS engagement and intelligent marketing workflows. At the core of Ylopo’s platform is an SMS bot designed to qualify leads, schedule calls, and guide prospects through early stages of the buying journey. As adoption expanded across realtors, optimizing intent accuracy, improving conversion rates, and scaling bot performance became central to Ylopo’s growth strategy. ## Before Dimension Labs Ylopo’s SMS bot generated significant volumes of conversational data, but extracting consistent, scalable insight from those interactions proved difficult. Lead generation rates had plateaued, and understanding why certain conversations converted while others failed required manual analysis and fragmented reporting. Scaling new intents across realtor clients required heavy involvement from the data science team, slowing expansion. Before Dimension Labs: - SMS conversations existed as raw, unstructured transcripts - Lead generation rate stalled at approximately 9% - Limited visibility into intent accuracy and performance - Manual grading and data science workflows created bottlenecks - Difficulty scaling bot implementation across realtor clients Ylopo had access to high-volume conversational data, but lacked a structured system to consistently measure intent performance, optimize conversion drivers, and accelerate adoption. ## With Dimension Labs Ylopo implemented Dimension Labs as the structured intelligence layer for SMS bot interactions, transforming raw lead conversations into governed, analytics-ready signals tied directly to conversion outcomes. Every conversation is analyzed at the record level and enriched with structured attributes capturing intent classification, engagement signals, conversion stage progression, and scheduled call outcomes. This created a consistent schema for analyzing lead quality and performance across realtor accounts. With Dimension Labs, Ylopo can now: - Monitor and improve intent accuracy in real time - Identify high-performing and underperforming conversation flows - Track lead progression through qualification and scheduling stages - Automate previously manual grading and evaluation workflows - Scale bot deployment across realtor clients with structured evidence This foundation delivered measurable business impact: - Realtor implementation increased from 5% to 50% within six months - Data science team productivity increased 3× - Over $250,000 saved in resource costs through workflow automation - More than 20% improvement in scheduled call conversion rates Conversational data shifted from manual review to structured growth intelligence. ## Use case 01 Intent Optimization & Conversion Intelligence Accurate intent classification is critical in real estate lead nurturing, where timing and relevance directly impact conversion. Dimension Labs enriches each SMS interaction with structured intent and outcome signals, allowing Ylopo to: - Identify misclassified or ambiguous intents - Monitor engagement quality across new intent rollouts - Quantify which conversational patterns lead to scheduled calls - Detect friction points that reduce lead progression This structured visibility enabled Ylopo to systematically improve intent accuracy and increase conversion rates for scheduled calls by over 20%, contributing to stronger lead generation performance. ## Use case 02 Scalable Automation & Data Science Efficiency Prior to implementing Dimension Labs, scaling intent models required manual grading and intensive data science oversight. As realtor adoption increased, these workflows became a constraint. Dimension Labs automated transcript evaluation and performance tracking by: - Extracting high-impact training phrases - Identifying conversation patterns requiring retraining - Quantifying hot lead engagement signals - Standardizing performance metrics across realtor accounts This automation reduced reliance on manual graders and tripled the productivity of Ylopo’s data science team, while saving more than $250,000 in operational costs. Bot optimization became a governed, repeatable system rather than an ad hoc process. ## Conclusion Ylopo generates high volumes of SMS lead conversations across thousands of realtor interactions. By establishing Dimension Labs as the Meaning Layer for conversational data, Ylopo transformed raw transcripts into structured, conversion-focused intelligence. Lead intent accuracy is now measurable, conversion drivers are identifiable at scale, and bot performance is continuously optimized through governed workflows. This structured foundation enabled rapid realtor adoption, increased operational efficiency, and accelerated revenue growth through data-driven lead conversion. ## More from customer stories How Dropbox Transformed Conversational Data into Scalable Self-Service Intelligence Dropbox is a global leader in cloud-based file storage and collaboration, serving millions of individuals and businesses worldwide. Concentrix Establishes Structured Chatbot Intelligence to Optimize Retail Customer Journeys Concentrix is a leading global provider of customer experience (CX) solutions and technology, supporting some of the world’s most recognized brands. Travelers Establishes a Unified Meaning Layer Across Millions of Customer Interactions Travelers is a Fortune 100 insurance provider serving millions of policyholders across personal, commercial, and specialty lines of business. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Artifacts Gallery: Reports Built by the Agent | Dimension Labs URL: https://www.dimensionlabs.io/artifacts Artifacts Gallery # Work and rework your data. Analytical freedom to turn one data layer into unlimited reports, dashboards, monitors and apps. Every artifact here was produced by the agent, and every one is built on Dimensions. All Automotive Consumer products Financial services Fitness Hospitality Insurance Restaurants Retail Software Sports Panera Bread Report Store Performance Diagnostic 27 dimensions · Reviews Marriott Report Multi-Location Review Analysis 23 dimensions · Reviews Marriott Monitor Guest Experience Monitor 23 dimensions · Reviews Carvana Monitor Customer Voice Monitor 25 dimensions · Reviews (5 sites) Gamba Osaka Report Fan Behavior Intelligence (Japanese) 30 dimensions · Fan surveys, sales activity, attendance records Carvana Report Multi-Source Review Analysis 25 dimensions · Reviews (5 sources) Zendesk Report Churn Analysis 33 dimensions · Support tickets, chat, onboarding surveys +5 NBA Report Fan Experience Intelligence 21 dimensions · Survey responses Home Depot Report Multi-Location Review Analysis 19 dimensions · Reviews Gamba Osaka Report Fan Behavior Intelligence 30 dimensions · Fan surveys, sales activity, attendance records AutoNation Report Customer Review Analysis 22 dimensions · Reviews American Dream Mall Report Visitor Analysis 20 dimensions · Google Maps reviews Chick-fil-A Report Customer Review Analysis 24 dimensions · Google Maps reviews Duolingo App Interactive Insights App 25 dimensions · Reviews Duolingo Report Mobile Review Analysis 25 dimensions · Reviews Eastern Bank Report Board Diagnostic 29 dimensions · Reviews and complaints (8 public sources) Owlet Report Product Voice Intelligence 34 dimensions · Product reviews, SEC filings Austin Firebirds Monitor Fan Intelligence Monitor 27 dimensions · Fan surveys, social, reviews, support (sample data) Austin Firebirds Report Fan Voice and Behavior Intelligence Report 27 dimensions · Fan surveys, social, reviews, support, attendance Planet Fitness Monitor Member Churn Causal Monitor 28 dimensions · Google Maps reviews Planet Fitness Report Causal Analysis 28 dimensions · Google Maps reviews, public filings Rhino Report Causal Brief 26 dimensions · Reviews (Trustpilot, BBB) Dimension Labs Report What Your CX Tools Can’t Tell You 23 dimensions · Google Maps reviews Dimension Labs Report What Your CX Tools Can’t Tell You (Spanish) 23 dimensions · Google Maps reviews ## Every artifact, built with Dimensions. Dimensions are typed fields the agent writes onto every conversation. Once text has them, it behaves like any structured data, which is what makes reports, dashboards and monitors possible on raw conversations. Learn about Dimensions Dimension Labs / Start with one file From $299 a month ## Make one from your own data. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Duolingo Customer Analysis: 502,269 Reviews | Dimension Labs URL: https://www.dimensionlabs.io/artifacts/duolingo-customer-analysis Artifacts Gallery # Duolingo Customer Analysis 6 min read FULL In week one of May 2025, a structured analysis of Duolingo customer reviews identified exactly the friction that Duolingo's CEO publicly acknowledged on May 4, 2026. In between those two dates, the stock fell 81% and roughly $20 billion in market capitalization was erased. The customer voice had a 12-month lead time over the equity market's repricing. This is what that analysis found, and what it means for every executive running a subscription business that has shipped an AI announcement in the last year. Key takeaway: Customer voice is a leading indicator of financial performance with a 9–12 month lead time over quarterly SEC filings. The Duolingo case makes this concrete: the friction that destroyed $20B in market cap was visible in review data seven days after the event that caused it. ## What Happened to Duolingo Duolingo made two product decisions in 2025 that their customers rejected loudly, and their financials confirmed quietly, one quarter at a time. Event 1: The AI-first memo (April 28, 2025) Duolingo publicly announced it was going "AI-first," which customers read as: humans are out, quality is going down. The customer reaction was immediate and measurable. Signal Pre-pivot rate Post-pivot rate Significance AI mentioned in reviews 0.72% 4.53% p < 0.001 Trust erosion 0.27% 3.71% p < 0.001 Translator layoff reaction 0.06% 2.30% p < 0.001 Course quality declined 2.95% 5.50% p < 0.001 That spike wasn't a one-week blowup that normalized. Eleven months later, AI mentions in reviews were still running 2.18× above the pre-pivot baseline. The announcement landed as quality-control removal, and customers never fully forgave it. Event 2: The Energy Points rollout (October 20, 2025) Duolingo replaced its hearts mechanic with an energy system on the free tier. Customers interpreted it as a disguised paywall that interrupted learning mid-session. In the rollout week, 47.8% of Android reviews were 1 or 2 stars, up from 9.4% the prior week. The dominant language in reviews was consistent: "can't finish a lesson," "running out of energy," "bring back hearts." That's not a feature complaint. That's a trust complaint. ## The Financial Lag Here's what makes this analytically striking. The customer voice signaled the problem in week one. The equity market needed four full quarters to price it in. Quarter DAU Growth YoY Bookings Growth YoY Customer Voice Context Q1 2025 +49% +38% Pre-pivot baseline Q2 2025 +40% +41% AI memo lands Apr 28; surface metrics still strong Q3 2025 +36% High DAU growth decelerates; AI residual persists Q4 2025 +30% +24% Energy launches Oct 20; bookings crater 17 points Q1 2026 +21% ~+10% Management acknowledges "extra friction in monetization" The Q4 2025 bookings cliff, from +41% to +24% in a single quarter, is the single largest deceleration in Duolingo's public history. The customer voice had identified the underlying friction ten weeks earlier. By May 2026, Duolingo's stock had fallen from $544.93 to $105.15. Management announced they were investing $50M+ in foregone bookings to roll back the friction. The customer data had been telling them this for a year. ## Three Things This Proves 1. Customer voice leads financial metrics by 9–12 months Quarterly filings are a lagging indicator. By the time a bookings deceleration shows up in a 10-Q, the underlying cause has been visible in customer language for multiple quarters. The gap between the two is where causal intelligence lives. 2. AI announcements carry a specific reputational risk When a broad AI-first announcement lands, customers don't read it as "new features." They read it as "the humans are gone." The reviews make this explicit: "If I wanted to learn with an AI, I'd subscribe to ChatGPT." That substitution framing, general-purpose AI as the alternative, is the most expensive outcome. You've trained your own customers to leave your category. 3. Engagement mechanics that gate outcomes are a retention tax There's a difference between a mechanic that limits access to a feature and a mechanic that interrupts the thing the customer came to do. Energy depleted mid-lesson isn't gamification, it's coercion. Once customers frame a mechanic as "money hungry" or "disguised paywall," every subsequent monetization surface inherits that framing. Trust eroded ahead of price language in the data by one to two quarters. ## The Portable Diagnostic This isn't a Duolingo story. It's a framework that applies to any subscription business that has shipped an AI announcement or changed an engagement mechanic in the last 12 months. Three conditions made Duolingo uniquely exposed: - A broad AI announcement framed as "AI-first" rather than "we added AI to one specific workflow", broad framings invite customers to compare you to general-purpose assistants - A mechanic that gates outcomes , energy depleting mid-lesson, not mid-feature - A parasocial brand relationship , customers who talk about your product like a person feel betrayed rather than disappointed If all three conditions apply to your business, the diagnostic to run is straightforward: - Pull your customer voice, reviews, tickets, exit surveys, for a 90-day window before and after your last major product announcement - Design 15–20 structured dimensions covering the hypothesis: AI quality, trust, engagement mechanics, competitive substitution - Run pre/post statistical testing across the event boundary, look for the joint cliff where adverse sentiment and mechanism mentions (e.g., "AI," "energy," "paywall") move together in the same week - Measure the residual at 90 days, if AI or friction mentions are still running 2× above baseline, the announcement landed as quality removal - Overlay against your next two quarterly filings, if the pattern matches, you have a leading indicator system The cost of running this analysis is rounding error compared to a single quarter of bookings deceleration. ## What Causal Intelligence Makes Possible Your dashboards show you the Q4 bookings cliff. They cannot tell you that the underlying cause was visible in customer language in May, six months earlier. The distance between "our bookings decelerated" and "here's the specific friction event that caused it, with statistical evidence, dated to the week it happened", that distance is the most expensive gap in enterprise analytics. Dimension Labs closes it. We convert unstructured customer conversations into structured, queryable data, and join it with your CRM, billing, and product metrics. The result is intelligence that answers why , not just what . The Duolingo case is a closed loop. Every finding in the customer voice was confirmed by SEC filings with a 9–12 month lag. The framework is portable to any consumer subscription business running AI features today. ## Frequently Asked Questions ### What do Duolingo reviews reveal about the impact of its AI pivot? A structured analysis of 502,269 Duolingo reviews found that four of five AI-quality signals shifted significantly (p < 0.001) within seven days of the April 28, 2025 AI-first announcement. AI mentions jumped from 0.72% to 4.53% of reviews, and trust erosion spiked from 0.27% to 3.71%. Eleven months later, AI mentions were still running 2.18× above the pre-pivot baseline, indicating a permanent reputation shift, not a transient backlash. ### How does customer voice data predict financial performance? Customer language captures friction and trust erosion weeks to months before those dynamics appear in quarterly revenue or DAU metrics. In Duolingo's case, the customer voice identified the bookings-deceleration signal in week one of May 2025; the financial confirmation arrived through SEC filings across four subsequent quarters. That 9–12 month lead time is the actionable window for executives who want to course-correct before results deteriorate. ### What is a "retention tax" in subscription products? A retention tax is an engagement mechanic that interrupts the core outcome a customer paid for, rather than gating access to an additional feature. Duolingo's Energy system depleted mid-lesson, customers couldn't complete a practice session without watching ads or paying. Reviews characterized it as a "disguised paywall." The distinction matters because mechanics that gate outcomes produce trust language ("money hungry," "coercive") while mechanics that gate features produce value-comparison language, and trust language is significantly harder to recover from. ### Does this analysis apply to B2B SaaS or only B2C subscription? The underlying framework, joining unstructured customer language with structured business metrics and testing for causal relationships across event boundaries, applies to any business with customer conversations and measurable outcomes. The Duolingo case is B2C, but the same methodology has been applied to enterprise SaaS churn analysis, insurance policyholder interactions, and hardware product feedback. The specific three-condition risk profile (broad AI announcement + outcome-gating mechanic + parasocial brand) is most acute in consumer subscription, but the diagnostic approach is industry-agnostic. To see what your customer conversations are telling you that your dashboards can't, book a demo with Dimension Labs. Sources: - Dimension Labs, "The Cable Episode 01: How 502,269 Reviews Predicted a $20B Market Cap Destruction" (May 2026). dimensionlabs.ai - Duolingo, Inc., Form 10-K for Fiscal Year 2025. Filed February 2026. SEC EDGAR. - Duolingo, Inc., Q1 2026 Shareholder Letter. Filed May 4, 2026. ir.duolingo.com Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## The Dimensionality of Meaning: Research Preview URL: https://www.dimensionlabs.io/research/the-dimensionality-of-meaning Research # Preview: The Dimensionality of Meaning Our early validation work on text dimensionality provides key support for an emerging class of AI-native research methods. Jul 1, 2026 1 min read LLM Experience Ratings Reproducibility Scale Compression Intercoder Reliability Open-Ended Responses Survey Validation Predictive Scoring Text Analysis Read the paper ## Abstract Most enterprise text analytics rests on a simplifying assumption: each customer conversation can be reduced to a single topic, sentiment, or routing label. This paper challenges that assumption by introducing text dimensionality, a framework for measuring the multiple distinct meanings that can coexist inside the same passage of language. Using 1,916 Stake.com support conversations from a single day of live traffic, Dimension Labs read each conversation through six business lenses: payments, support, marketing, compliance, product, and retention. The study found that 92% of conversations carried information relevant to more than one function, with the average conversation activating 2.74 lenses. The clean single-issue ticket accounted for fewer than 5% of the data. The findings show that these meanings are not noise or redundant labels, but part of a stable business structure. Support appeared as the central hub, co-occurring with payments, compliance, marketing, product, and retention issues across the majority of multi-meaning conversations. Most importantly, the framework exposed revenue-relevant causal paths that a flattened reading would miss, including a churn pattern that began with deposit failures, intensified through unresolved support interactions, and ended in explicit departure language. The result is a validated foundation for AI-native research methods: large language models can now extract multiple measurable dimensions from the same unstructured data, join them to operational and revenue data, and turn text that companies already own into a structured map of what drives customer experience, retention, and business performance. ## More from research Jul 1, 2026 What sentiment analysis can’t see Predicted satisfaction beats sentiment as a measurement of whether customers were helped, and what went wrong, across 70,000 support conversations. Jul 14, 2026 Dimensionality in Satisfaction Ratings LLMs decompose support conversations into satisfaction drivers, revealing lower customer satisfaction than surveys suggest. Apr 15, 2026 LLM Predictive Scoring and Validation Part 1: LLM-annotation recovers self-reported experience ratings from raw unstructured textual data across 10,000 MLB fan surveys. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## The Signal Is the Ceiling: Limits of LLM-Predicted Ratings URL: https://www.dimensionlabs.io/research/the-signal-is-the-ceiling Research # The signal is the ceiling Part 2: Reliability of AI annotation exceeds human benchmarks, however, the measurement limits of LLM-predicted ratings have an upper limit. Apr 22, 2026 1 min read LLM Experience Ratings Reproducibility Scale Compression Intercoder Reliability Open-Ended Responses Survey Validation Predictive Scoring Text Analysis Read on arXiv Download PDF ## Abstract An earlier paper (Hong, Potteiger, and Zapata 2026) established that an unoptimized GPT 4.1 prompt predicts fan-reported experience ratings within one point 67% of the time from open-ended survey text. This paper tests the relative impact of prompt design and model selection on that performance. We compared four configurations on approximately 10,000 post-game surveys from five MLB teams: the original baseline prompt and a moderately customized version, crossed with three GPT models (4.1, 4.1-mini, 5.2). Prompt customization added roughly two percentage points of within-±1 agreement on GPT 4.1 (from 67% to 69%). Both model swaps from that best configuration degraded performance: GPT 5.2 returned to the baseline, and GPT 4.1-mini fell six percentage points below it. Both levers combined were dwarfed by the input itself: across capable configurations, accuracy varied more than an order of magnitude more by the linguistic character of the text than by the choice of prompt or model. The ceiling has two parts. One is a bias in how the model reads text, which prompt design can correct. The other is a difference between what fans write about and what they actually decide, which no engineering can close because the missing information is not in the text. Prompt customization moved the first part; model selection moved neither reliably. The result is not that "prompt engineering helps a little" but that prompt engineering helps in a specific and predictable way, on the part of the ceiling it can reach. ## More from research Apr 15, 2026 LLM Predictive Scoring and Validation Part 1: LLM-annotation recovers self-reported experience ratings from raw unstructured textual data across 10,000 MLB fan surveys. Jul 1, 2026 Preview: The Dimensionality of Meaning Our early validation work on text dimensionality provides key support for an emerging class of AI-native research methods. Jul 1, 2026 What sentiment analysis can’t see Predicted satisfaction beats sentiment as a measurement of whether customers were helped, and what went wrong, across 70,000 support conversations. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## What Sentiment Analysis Can’t See | Dimension Labs URL: https://www.dimensionlabs.io/research/what-sentiment-cant-see Research # What sentiment analysis can’t see Predicted satisfaction beats sentiment as a measurement of whether customers were helped, and what went wrong, across 70,000 support conversations. Jul 1, 2026 1 min read LLM Experience Ratings Reproducibility Scale Compression Intercoder Reliability Open-Ended Responses Survey Validation Predictive Scoring Text Analysis Read on arXiv Download PDF ## Abstract Most companies read their customer support data at scale using sentiment analysis, which measures how customers sound rather than whether they were satisfied with the result. We tested a richer alternative on 70,450 support conversations from a leading online fundraising platform: alongside tone, we used GPT-5.4 to estimate each customer's satisfaction and to flag whether they reported a concrete problem, then validated all three readings against the 1-to-5 ratings customers left on the conversations they rated. The satisfaction estimate tracked those ratings far better than sentiment did, correlating at 0.47 against 0.36 and flagging unhappy customers with far fewer false alarms. The structured read also sees what sentiment cannot: tone and satisfaction disagree in 44% of conversations, a single "Neutral" label hides everything from quietly satisfied customers to ones who quietly gave up, and the largest group of all is "tolerated friction," customers who are satisfied but still reporting a fixable problem, a standing issue that no sentiment-based dashboard can surface. The broader finding is that LLM-based annotation can capture far more than the tonality of a customer's language, offering strong potential for new business metrics grounded instead in the customer's state (whether they were satisfied) and the cause of their problem extracted directly from the raw textual data of interactions and feedback. ## More from research Jul 14, 2026 Dimensionality in Satisfaction Ratings LLMs decompose support conversations into satisfaction drivers, revealing lower customer satisfaction than surveys suggest. Jul 1, 2026 Preview: The Dimensionality of Meaning Our early validation work on text dimensionality provides key support for an emerging class of AI-native research methods. Apr 15, 2026 LLM Predictive Scoring and Validation Part 1: LLM-annotation recovers self-reported experience ratings from raw unstructured textual data across 10,000 MLB fan surveys. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Dimensionality in Satisfaction Ratings | Dimension Labs URL: https://www.dimensionlabs.io/research/dimensionality-in-satisfaction-ratings Research # Dimensionality in Satisfaction Ratings LLMs decompose support conversations into satisfaction drivers, revealing lower customer satisfaction than surveys suggest. Jul 14, 2026 1 min read LLM Experience Ratings Reproducibility Scale Compression Intercoder Reliability Open-Ended Responses Survey Validation Predictive Scoring Text Analysis Read on arXiv Download PDF ## Abstract We used a large language model (GPT-4.1) to annotate the text of about 9,000 support conversations at a global consumer-goods firm, decomposing customer-care satisfaction into component axes (overall, agent, outcome, product, and customer effort), and validated the LLM annotations against the satisfaction ratings customers gave themselves. Four of five axes track self-reported satisfaction closely (overall, agent, and outcome near an unadjusted 0.65; effort -0.54), while product satisfaction is weak against the available proxy. The unadjusted correlation also understates the alignment: the disagreements concentrate in a small, readable tail of divergent sessions rather than in general drift, and the overall correlation rises to 0.811 when only the severe divergences are excluded and to 0.914 when the full divergent tail is excluded. The axes are also highly collinear, and adding them to the overall score does not improve prediction of the customer's rating, the decomposition's value is not incremental prediction but attribution and coverage. And, with greater coverage the picture of the data changes. Read on every contact rather than the few that return a survey, satisfaction is markedly lower than the survey reports (a full-census 2.91 against the surveyed 3.62 on a five-point scale). The promise of decomposed satisfaction as a methodology is the ability to identify more nuanced drivers of customer experience in conversational data. ## More from research Jul 1, 2026 What sentiment analysis can’t see Predicted satisfaction beats sentiment as a measurement of whether customers were helped, and what went wrong, across 70,000 support conversations. Apr 15, 2026 LLM Predictive Scoring and Validation Part 1: LLM-annotation recovers self-reported experience ratings from raw unstructured textual data across 10,000 MLB fan surveys. Jul 1, 2026 Preview: The Dimensionality of Meaning Our early validation work on text dimensionality provides key support for an emerging class of AI-native research methods. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## LLM Predictive Scoring: Inferring Ratings From Text URL: https://www.dimensionlabs.io/research/llm-predictive-scoring-and-validation-inferring-experience-ratings-from-unstructured-text Research # LLM Predictive Scoring and Validation Part 1: LLM-annotation recovers self-reported experience ratings from raw unstructured textual data across 10,000 MLB fan surveys. Apr 15, 2026 1 min read LLM Experience Ratings Reproducibility Scale Compression Intercoder Reliability Open-Ended Responses Survey Validation Predictive Scoring Text Analysis Read on arXiv Download PDF ## Abstract We tasked GPT-4.1 to read what baseball fans wrote about their game-day experience and predict the overall experience rating each fan gave on a 0-10 survey scale. The model received only the text of a single open-ended response. These AI predictions were compared with the actual experience ratings captured by the survey instrument across approximately 10,000 fan responses from five Major League Baseball teams. In total two-thirds of predicted ratings fell within one point of self-reported fan ratings (67% within +/-1, 36% exact match), and the predicted measurement was near-deterministic across three independent scoring runs (87% exact agreement, 99.9% within +/-1). Predicted ratings aligned most strongly with the overall experience rating (r = 0.82) rather than with any specific aspect of the game-day experience such as parking, concessions, staff, etc. However, predictions were systematically lower than self-reported ratings by approximately one point, and this gap was not driven by any single aspect. Rather, our analysis shows that self-reported ratings capture the fan's verdict, an overall evaluative judgment that integrates the entire experience. While predicted ratings quantify the impact of salient moments characterized as memorable, emotionally intense, unusual, or actionable. Each measure contains information the other misses. These baseline results establish that a simple, unoptimized prompt can directionally predict how fans rate their experience from the text a fan wrote and that a gap between the two numbers can be interpreted as a construct difference worth preserving rather than an error to eliminate. ## More from research Apr 22, 2026 The signal is the ceiling Part 2: Reliability of AI annotation exceeds human benchmarks, however, the measurement limits of LLM-predicted ratings have an upper limit. Jul 14, 2026 Dimensionality in Satisfaction Ratings LLMs decompose support conversations into satisfaction drivers, revealing lower customer satisfaction than surveys suggest. Jul 1, 2026 What sentiment analysis can’t see Predicted satisfaction beats sentiment as a measurement of whether customers were helped, and what went wrong, across 70,000 support conversations. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Dimension Labs vs Enterpret, Chattermill, Qualtrics and More URL: https://www.dimensionlabs.io/compare Compare # Other tools need a demo and a $30,000 contract. This one needs a file. Most feedback analytics tools are sold through a sales process and an annual contract, and rank issues by how often they come up. Dimension Labs starts on a monthly plan and ranks issues by what they actually do to the number. Start now › See pricing › Dimension Labs vs Enterpret Enterpret is a customer intelligence platform that unifies feedback and ties it to churn, NPS, CSAT and expansion. Pricing: Custom pricing based on volume and integrations Dimension Labs vs Chattermill Chattermill is an AI voice-of-customer platform that maps aspect-level sentiment to NPS, CSAT and CES. Pricing: Not published Dimension Labs vs Thematic Thematic is a feedback analytics platform built around research-grade theme coding. Pricing: Annual contracts; earlier research found a floor around $25,000 a year Dimension Labs vs Unwrap Unwrap is a customer feedback platform that surfaces trends across channels without manual tagging. Pricing: From $24,000 a year Dimension Labs vs Qualtrics Qualtrics is an experience management suite: surveys, panels, dashboards and add-ons such as Text iQ for open text. Pricing: Enterprise contracts; median around $30,000 a year per Vendr data, with add-ons extra Dimension Labs vs Medallia Medallia is an enterprise customer experience suite for collecting and managing feedback across locations and channels. Pricing: Enterprise contracts, typically high five figures and up Competitor details are taken from each company's own website, checked in October 2026. Pricing changes; check with each vendor. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Dimension Labs vs Chattermill: An Honest Comparison URL: https://www.dimensionlabs.io/compare/chattermill Compare # Dimension Labs vs Chattermill Chattermill is an AI voice-of-customer platform that maps aspect-level sentiment to NPS, CSAT and CES. Dimension Labs is an analytics agent that reads every customer conversation, joins it to what customers did, and proves which issues actually move your numbers. Start now See pricing Chattermill Dimension Labs How you start Book a demo Start now on a monthly plan. No demo, no sales call Pricing Not published Published: from $299 a month, Prove $2,999, enterprise from $150,000 a year How issues are ranked By the vendor’s own analysis of themes and sentiment By measured effect on your metric, with effect sizes and confidence intervals Joins to business data See the vendor’s documentation Yes: CRM, orders, usage and renewals, plus Snowflake, Databricks and BigQuery Every claim traced to source See the vendor’s documentation Yes, every finding links to the transcripts behind it What you get The vendor’s dashboards and reports Shareable reports, dashboards, decks and apps the agent builds on request Chattermill details from Chattermill's website, checked October 2026. Pricing changes; confirm with the vendor. ## Pick Chattermill if You want a CX dashboard program for a large consumer brand and have the time for an enterprise buying process. ### What Chattermill does well - Aspect-based sentiment across reviews, surveys and support - Consumer brands with high feedback volume - Dashboards for CX teams ## Pick Dimension Labs if You want the answer to why a number moved, not another dashboard, and you want to start on a monthly plan without a sales call. Start now ## Common questions Is Dimension Labs an alternative to Chattermill? For teams that already have customer conversations and need to know why a metric moved, yes. Chattermill is an AI voice-of-customer platform that maps aspect-level sentiment to NPS, CSAT and CES. Dimension Labs reads every conversation, joins it to outcomes like churn and revenue, and ranks drivers by their measured effect. Can I use Dimension Labs alongside Chattermill? Yes. Many teams keep their existing tool for collection and send the conversations or responses to Dimension Labs for the analysis. How fast can I start? Today. Plans start at $299 a month with no demo, no sales call and no contract on monthly plans. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Dimension Labs vs Enterpret: An Honest Comparison URL: https://www.dimensionlabs.io/compare/enterpret Compare # Dimension Labs vs Enterpret Enterpret is a customer intelligence platform that unifies feedback and ties it to churn, NPS, CSAT and expansion. Dimension Labs is an analytics agent that reads every customer conversation, joins it to what customers did, and proves which issues actually move your numbers. Start now See pricing Enterpret Dimension Labs How you start Book a demo or request a trial on your data Start now on a monthly plan. No demo, no sales call Pricing Custom pricing based on volume and integrations Published: from $299 a month, Prove $2,999, enterprise from $150,000 a year How issues are ranked By the vendor’s own analysis of themes and sentiment By measured effect on your metric, with effect sizes and confidence intervals Joins to business data See the vendor’s documentation Yes: CRM, orders, usage and renewals, plus Snowflake, Databricks and BigQuery Every claim traced to source See the vendor’s documentation Yes, every finding links to the transcripts behind it What you get The vendor’s dashboards and reports Shareable reports, dashboards, decks and apps the agent builds on request Enterpret details from Enterpret's website, checked October 2026. Pricing changes; confirm with the vendor. ## Pick Enterpret if You’re a product-led software company that wants a managed feedback taxonomy for your product org and is fine with a sales-led evaluation. ### What Enterpret does well - Unifying feedback from many channels into one taxonomy - Product teams at software companies - Connecting feedback to individual customer accounts ## Pick Dimension Labs if You want to start today on your own file, see prices up front, and get findings ranked by their measured effect on the metric, with confidence intervals. Start now ## Common questions Is Dimension Labs an alternative to Enterpret? For teams that already have customer conversations and need to know why a metric moved, yes. Enterpret is a customer intelligence platform that unifies feedback and ties it to churn, NPS, CSAT and expansion. Dimension Labs reads every conversation, joins it to outcomes like churn and revenue, and ranks drivers by their measured effect. Can I use Dimension Labs alongside Enterpret? Yes. Many teams keep their existing tool for collection and send the conversations or responses to Dimension Labs for the analysis. How fast can I start? Today. Plans start at $299 a month with no demo, no sales call and no contract on monthly plans. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Dimension Labs vs Medallia: An Honest Comparison URL: https://www.dimensionlabs.io/compare/medallia Compare # Dimension Labs vs Medallia Medallia is an enterprise customer experience suite for collecting and managing feedback across locations and channels. Dimension Labs is an analytics agent that reads every customer conversation, joins it to what customers did, and proves which issues actually move your numbers. Start now See pricing Medallia Dimension Labs How you start Sales-led Start now on a monthly plan. No demo, no sales call Pricing Enterprise contracts, typically high five figures and up Published: from $299 a month, Prove $2,999, enterprise from $150,000 a year How issues are ranked By the vendor’s own analysis of themes and sentiment By measured effect on your metric, with effect sizes and confidence intervals Joins to business data See the vendor’s documentation Yes: CRM, orders, usage and renewals, plus Snowflake, Databricks and BigQuery Every claim traced to source See the vendor’s documentation Yes, every finding links to the transcripts behind it What you get The vendor’s dashboards and reports Shareable reports, dashboards, decks and apps the agent builds on request Medallia details from Medallia's website, checked October 2026. Pricing changes; confirm with the vendor. ## Pick Medallia if You need an enterprise feedback collection and case-management program. ### What Medallia does well - Large multi-location feedback programs - Closed-loop case management - Role-based dashboards across an organization ## Pick Dimension Labs if You already have the verbatims and need to know which issues move the score at each location, and which loud complaints don’t. Keep Medallia for collection and send the data to Dimension Labs. Start now ## Common questions Is Dimension Labs an alternative to Medallia? For teams that already have customer conversations and need to know why a metric moved, yes. Medallia is an enterprise customer experience suite for collecting and managing feedback across locations and channels. Dimension Labs reads every conversation, joins it to outcomes like churn and revenue, and ranks drivers by their measured effect. Can I use Dimension Labs alongside Medallia? Yes. Many teams keep their existing tool for collection and send the conversations or responses to Dimension Labs for the analysis. How fast can I start? Today. Plans start at $299 a month with no demo, no sales call and no contract on monthly plans. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Dimension Labs vs Qualtrics: An Honest Comparison URL: https://www.dimensionlabs.io/compare/qualtrics Compare # Dimension Labs vs Qualtrics Qualtrics is an experience management suite: surveys, panels, dashboards and add-ons such as Text iQ for open text. Dimension Labs is an analytics agent that reads every customer conversation, joins it to what customers did, and proves which issues actually move your numbers. Start now See pricing Qualtrics Dimension Labs How you start Sales-led, with a free survey tier Start now on a monthly plan. No demo, no sales call Pricing Enterprise contracts; median around $30,000 a year per Vendr data, with add-ons extra Published: from $299 a month, Prove $2,999, enterprise from $150,000 a year How issues are ranked By the vendor’s own analysis of themes and sentiment By measured effect on your metric, with effect sizes and confidence intervals Joins to business data See the vendor’s documentation Yes: CRM, orders, usage and renewals, plus Snowflake, Databricks and BigQuery Every claim traced to source See the vendor’s documentation Yes, every finding links to the transcripts behind it What you get The vendor’s dashboards and reports Shareable reports, dashboards, decks and apps the agent builds on request Qualtrics details from Qualtrics's website, checked October 2026. Pricing changes; confirm with the vendor. ## Pick Qualtrics if You need a survey platform to collect feedback across the enterprise. ### What Qualtrics does well - Running survey programs at enterprise scale - Panel and research operations - Broad suite coverage across experience programs ## Pick Dimension Labs if You already collect the feedback and need to know which parts of it actually move NPS, churn or revenue. Keep Qualtrics for collection and send the responses to Dimension Labs. Start now ## Common questions Is Dimension Labs an alternative to Qualtrics? For teams that already have customer conversations and need to know why a metric moved, yes. Qualtrics is an experience management suite: surveys, panels, dashboards and add-ons such as Text iQ for open text. Dimension Labs reads every conversation, joins it to outcomes like churn and revenue, and ranks drivers by their measured effect. Can I use Dimension Labs alongside Qualtrics? Yes. Many teams keep their existing tool for collection and send the conversations or responses to Dimension Labs for the analysis. How fast can I start? Today. Plans start at $299 a month with no demo, no sales call and no contract on monthly plans. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Dimension Labs vs Thematic: An Honest Comparison URL: https://www.dimensionlabs.io/compare/thematic Compare # Dimension Labs vs Thematic Thematic is a feedback analytics platform built around research-grade theme coding. Dimension Labs is an analytics agent that reads every customer conversation, joins it to what customers did, and proves which issues actually move your numbers. Start now See pricing Thematic Dimension Labs How you start Book a demo Start now on a monthly plan. No demo, no sales call Pricing Annual contracts; earlier research found a floor around $25,000 a year Published: from $299 a month, Prove $2,999, enterprise from $150,000 a year How issues are ranked By the vendor’s own analysis of themes and sentiment By measured effect on your metric, with effect sizes and confidence intervals Joins to business data See the vendor’s documentation Yes: CRM, orders, usage and renewals, plus Snowflake, Databricks and BigQuery Every claim traced to source See the vendor’s documentation Yes, every finding links to the transcripts behind it What you get The vendor’s dashboards and reports Shareable reports, dashboards, decks and apps the agent builds on request Thematic details from Thematic's website, checked October 2026. Pricing changes; confirm with the vendor. ## Pick Thematic if Your main job is coding survey verbatims into themes for a research team. ### What Thematic does well - Theme discovery in open-ended survey responses - Insights and research teams - Guides and education on feedback analysis ## Pick Dimension Labs if You need themes joined to outcomes like churn, revenue or ratings, ranked by causal effect rather than by how often they’re mentioned. Start now ## Common questions Is Dimension Labs an alternative to Thematic? For teams that already have customer conversations and need to know why a metric moved, yes. Thematic is a feedback analytics platform built around research-grade theme coding. Dimension Labs reads every conversation, joins it to outcomes like churn and revenue, and ranks drivers by their measured effect. Can I use Dimension Labs alongside Thematic? Yes. Many teams keep their existing tool for collection and send the conversations or responses to Dimension Labs for the analysis. How fast can I start? Today. Plans start at $299 a month with no demo, no sales call and no contract on monthly plans. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Dimension Labs vs Unwrap: An Honest Comparison URL: https://www.dimensionlabs.io/compare/unwrap Compare # Dimension Labs vs Unwrap Unwrap is a customer feedback platform that surfaces trends across channels without manual tagging. Dimension Labs is an analytics agent that reads every customer conversation, joins it to what customers did, and proves which issues actually move your numbers. Start now See pricing Unwrap Dimension Labs How you start Book a demo; 30-day trial Start now on a monthly plan. No demo, no sales call Pricing From $24,000 a year Published: from $299 a month, Prove $2,999, enterprise from $150,000 a year How issues are ranked By the vendor’s own analysis of themes and sentiment By measured effect on your metric, with effect sizes and confidence intervals Joins to business data See the vendor’s documentation Yes: CRM, orders, usage and renewals, plus Snowflake, Databricks and BigQuery Every claim traced to source See the vendor’s documentation Yes, every finding links to the transcripts behind it What you get The vendor’s dashboards and reports Shareable reports, dashboards, decks and apps the agent builds on request Unwrap details from Unwrap's website, checked October 2026. Pricing changes; confirm with the vendor. ## Pick Unwrap if You want trend alerting on feedback and an annual contract fits your budget. ### What Unwrap does well - Zero-shot theme detection - Alerting on emerging issues - Product and CX teams at growing companies ## Pick Dimension Labs if You want to start from $299 a month and prove which issues move your metrics before you scale up. Start now ## Common questions Is Dimension Labs an alternative to Unwrap? For teams that already have customer conversations and need to know why a metric moved, yes. Unwrap is a customer feedback platform that surfaces trends across channels without manual tagging. Dimension Labs reads every conversation, joins it to outcomes like churn and revenue, and ranks drivers by their measured effect. Can I use Dimension Labs alongside Unwrap? Yes. Many teams keep their existing tool for collection and send the conversations or responses to Dimension Labs for the analysis. How fast can I start? Today. Plans start at $299 a month with no demo, no sales call and no contract on monthly plans. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Predictive Scoring Lessons: Step-by-Step Guide | Dimension Labs URL: https://www.dimensionlabs.io/learn/all-lessons Lessons # Predictive scoring, step by step. A free course from Jason Potteiger on predicting satisfaction and other scores from what customers wrote, and validating the result. - Lesson 1 Introduction to predictive scoring Step-by-step guides on preparing your data, asking causal questions, reading driver analysis and turning findings into decisions leadership will act on. - Lesson 2 The case for predictive scoring Step-by-step guides on preparing your data, asking causal questions, reading driver analysis and turning findings into decisions leadership will act on. - Lesson 3 How predictive scoring works Step-by-step guides on preparing your data, asking causal questions, reading driver analysis and turning findings into decisions leadership will act on. - Lesson 4 Predictive scoring methods Step-by-step guides on preparing your data, asking causal questions, reading driver analysis and turning findings into decisions leadership will act on. - Lesson 5 Building prompt functions Step-by-step guides on preparing your data, asking causal questions, reading driver analysis and turning findings into decisions leadership will act on. - Lesson 6 What predictive scoring makes possible Step-by-step guides on preparing your data, asking causal questions, reading driver analysis and turning findings into decisions leadership will act on. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Why Gym Members Say They Will Cancel: Evidence From 9,186 Reviews URL: https://www.dimensionlabs.io/blog/why-gym-members-say-they-will-cancel Blog # Why Gym Members Say They Will Cancel: Evidence From 9,186 Reviews In 9,186 public reviews of a gym chain, staff problems had the biggest effect on bad ratings, but billing complaints were the ones most likely to say the member was cancelling or warning others away. Andrew Hong , CEO at Dimension Labs Oct 21, 2026 5 min read A gym chain that wants to cut cancellations usually starts with its exit survey or its star rating. The first tells you what members tick on the way out. The second moves too slowly and, as the data below shows, can be pushed around by a review campaign. Public reviews sit in between. Members write them in their own words, often in the week something went wrong. We read 9,186 public Google reviews of 33 Planet Fitness clubs in metro Chicago, written between January 2025 and July 2026, and asked two separate questions. What makes a member angry enough to leave a one- or two-star review? And what makes a member say they are done with the gym? ## Bad reviews and talk of leaving come from different problems Front desk and staff problems have the largest measured effect on bad reviews. A review that describes one is 36.3 points more likely to be one or two stars. Billing or cancellation trouble, cleanliness and safety each add about 25 points. The staff estimate has a wide range (26 to 52 points), so its lead is not certain. Talk of leaving follows a different pattern. The report counted a review as a leaving signal when it says the member is cancelling, will not come back, or is warning others away. Half of the reviews that raise a billing or cancellation problem do. Across all reviews the rate is 1.4%, so billing runs at about 36 times the average. Reviews about staff carry a leaving signal 13% of the time. These rates describe what reviews say; they are not causal estimates. Share of members raising each issue who say they will leave or warn others. From the Chicago gym analysis . A chain that only works on what moves the star rating would spend its effort on the front desk and could miss the members who were billed after cancelling. The two problems need different owners: staff training for one, the billing system and its rules for the other. ## What the billing reviews describe About one in six billing complaints describes a charge after cancelling or a fee the member did not expect. The report reads this as a trust problem with the recurring charge, and argues that making cancellation harder would not fix it. That is the report's interpretation of the one-in-six share, not something the reviews prove for every member who leaves. ## Urban clubs carry most of the problems The 33 clubs fall into three types. The ten urban clubs average 3.46 stars over the period and the sixteen outer-suburban clubs 4.84, at the same price. A third of urban reviews are one or two stars. Broken equipment is named in 8.5% of urban reviews and 0.4% of outer-suburban ones. Some of that gap comes from the reviews rather than the gyms. Between February and October 2025 about 4,903 five-star reviews arrived above the normal rate while the number of unhappy reviews barely moved, which points to a review-generation push rather than better service. Outer-suburban clubs received 84% of all reviews posted in those months. Taking the surge months out narrows the gap: outer-suburban clubs go from 3.1% bad reviews to 7.8%. Urban clubs stay at 33.8%, still about four times higher. Share of reviews that are one or two stars, by club type Planet Fitness, 33 Chicago-metro clubs, January 2025 to July 2026. Same brand and price at every club. Outer-suburban clubs got 84% of the reviews posted during the surge, which was mostly five-star, so their overall numbers look better than their recent ones. Outside it, urban clubs still get about four times the share of bad reviews. Dimension Labs analysis of 9,186 public Google reviews, run 2026-10-21. The rating Google shows has its own lag. It averages every review a club has ever had (36,279 across these clubs, of which the last eighteen months are about a quarter). At the weakest urban club it shows 4.1 stars, while reviews from the last eighteen months average 2.8. ## How a gym chain can find what drives its own cancellations - Treat the star rating as a lagging, gameable number. Use recent reviews only, per club, and check whether a burst of five-star reviews lines up with a review campaign before reading anything into the average. - Tag every review with every problem it names. Billing, staff, cleanliness, safety, equipment and crowding can all appear in one review. A single topic per review loses the second problem. - Measure two outcomes. A one- or two-star review tells you what makes members angry. A stated intent to cancel or warn others tells you what makes them leave. The two lists ranked differently here, and they may in your data too. - Compare clubs of the same type. Urban and suburban clubs have different members, traffic and wear, so a single league table mostly sorts clubs by location. - Join reviews to what you already hold. Reviews only show stated intent. To confirm it, use your own data: cancellation records and reason codes, billing disputes and chargebacks, front-desk tickets and exit surveys, by club and month. If billing complaints in reviews predict cancellations in your records, you have a cause worth fixing first. The method behind each step is in our posts on which complaints cause bad reviews , causal inference and building a churn risk report . The full gym analysis, with every exhibit, is in the Chicago gym report . ## FAQ ### Can reviews tell you why members cancel? They tell you what members say about leaving. That is a lead to check, not proof. In this data, reviews naming a billing or cancellation problem said the member was leaving about half the time. Confirm it against your own cancellation records before you act. ### Is the front desk or billing the bigger problem for a gym? In this analysis, front desk and staff problems had the largest effect on bad reviews, while billing complaints were the most likely to mention leaving. A chain needs both fixed, by different teams. ### Why not just use the star rating? A Google rating is an all-time average, so it moves slowly, and a review campaign can lift it without any change in the gym. Here, one club showed 4.1 stars while its recent reviews averaged 2.8. Dimension Labs runs this on your own reviews, surveys and tickets, club by club, and joins the results to the numbers you track. See how it works for fitness and recreation , or start with one file . ## More from blog Oct 19, 2026 Which Complaints Cause Bad Reviews? Hotel, Gym and Café Data In public reviews of a hotel group, a gym chain and a café chain, the most-mentioned complaint was not what drove the one- and two-star reviews. The numbers, and how to check your own. Feb 13, 2025 Semantic Clustering Demystified Semantic clustering groups text by meaning instead of exact words. Here it runs on 8,504 hotel reviews, including the part where it points at the wrong problem. Aug 11, 2026 How to Build a Churn Risk Report: The Methodology, the Joins, and the Dimensions Your CRM can tell you exactly which accounts churned last quarter, but it cannot tell you which ones are about to. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Which Complaints Cause Bad Reviews? Hotel, Gym and Café Data URL: https://www.dimensionlabs.io/blog/which-complaints-cause-bad-reviews Blog # Which Complaints Cause Bad Reviews? Hotel, Gym and Café Data In public reviews of a hotel group, a gym chain and a café chain, the most-mentioned complaint was not what drove the one- and two-star reviews. The numbers, and how to check your own. Andrew Hong , CEO at Dimension Labs Oct 19, 2026 4 min read The usual way to read reviews is to count: which complaint comes up most, then fix that first. This year we ran three sets of public reviews through a causal model (a hotel group, a gym chain and a restaurant chain), and in each one the most-mentioned topic was not the biggest driver of bad reviews. The full reports are linked at the end. ## Hotels: front desk complaints are common and barely move ratings 8,504 rated public Google reviews of 45 Marriott-family hotels in Orlando, January 2025 to July 2026. Front desk and check-in is the most-mentioned topic, in 2,317 reviews (27.2% of comments). Its measured effect on the chance of a one- or two-star review is about two points, and the confidence interval reaches zero. Issue named in the review Reviews Effect on 1 to 2 star risk Front desk or check-in 2,317 +2 points (not significant) A dirty room 849 +34 points Rude or unprofessional staff 307 +44 points A named, praised staff member 226 −17 points Left: issues by share of comments. Right: the same issues by measured effect on a one- or two-star review, 8,504 rated reviews. From the Orlando hotel brief . Sorted by count, the lobby is the first thing you would put money into. Sorted by effect, it is staff conduct and housekeeping. A likely reason the desk looks important: angry guests tend to describe the whole stay, and most stays pass the desk. ## Gyms: equipment gets the most complaints, staff problems do more damage 9,186 public Google reviews of 33 Planet Fitness clubs in metro Chicago, January 2025 to July 2026. Broken equipment is the complaint members raise most often (122 reviews). It raises the chance of a one- or two-star review by 4.7 points. Front desk and staff problems raise it by 36.3 points, roughly eight times as much on the central estimates (both ranges are wide). Billing or cancellation trouble and cleanliness each add about 25 points. The same six failures ranked by how often members raise them and by causal effect on a one- or two-star review. From the Chicago gym analysis . Failures also stack up within a review. Reviews that named no failure were one or two stars 4.4% of the time, against 66.1% for reviews naming one failure and 80.7% for the 57 that named two. What makes members say they are leaving is a different list, which we looked at separately . ## Restaurants: food comes up most, wrong orders hurt more 1,995 public Google reviews of 24 Panera cafés in the Chicago metro, January 2025 to April 2026. Food quality is the most-mentioned topic, in 343 reviews, and those reviews average 3.81 stars. The report found it draws about as much praise as complaint. A wrong or missing order comes up a third as often, in 116 reviews, and averages 1.92 stars. The six main topics ranked by how often customers raise them and by the average rating when they appear. From the Chicago café diagnostic . On cause, measured across the 1,157 reviews with written text, a rude or absent employee is the largest driver of a bad review, about 33 extra one- or two-star reviews per 100 reviewed visits. A wrong or missing order adds about 20. Ordering through the app or a kiosk shows no real effect, and the report found food and price barely matter. ## Why the counts mislead Counting tells you how common an experience is, not how bad it is when it happens. The parts of a visit everyone touches, like check-in or the food, come up in good reviews as well as bad ones, so their count runs high while their effect stays small. A rude employee or a wrong charge is rarer, and when one appears the review is usually angry. There is also a measurement trap. A topic can sit next to bad ratings without causing them, because it rides along in long, angry reviews. Simple driver analysis reads that as importance. In the hotel data, standard driver analysis put the front desk second on the fix list; the causal model put it near zero. ## How to check your own reviews - Tag every review with every issue it names. One review can complain about a charge and a dirty room at once. A single topic per review hides the second problem. Our semantic clustering guide shows how much a one-bucket approach misses. - Compare like with like. Hold the location, the tier or format, the review length and the time period fixed, so a busy flagship or an angry essay does not do the work for you. - Test each effect before you act on it. Swap in a fake issue (a placebo) and check the effect goes to zero. Rerun on random subsets of the data and check it holds. Each analysis above ran these checks, and each report shows the results. - Then size it. Effect per review times how often it happens tells you, roughly, where the stars are going. A rare, severe failure and a common, mild one can cost about the same. Public reviews are a good place to start, because you already have them and so do your competitors. Each report linked here was built from reviews anyone can read: the Orlando hotel brief , the Chicago gym analysis and the Chicago café diagnostic . More are in the Artifacts Gallery . Dimension Labs runs this analysis on your own reviews, surveys or tickets, location by location. Start with one file . ## More from blog Oct 21, 2026 Why Gym Members Say They Will Cancel: Evidence From 9,186 Reviews In 9,186 public reviews of a gym chain, staff problems had the biggest effect on bad ratings, but billing complaints were the ones most likely to say the member was cancelling or warning others away. Feb 13, 2025 Semantic Clustering Demystified Semantic clustering groups text by meaning instead of exact words. Here it runs on 8,504 hotel reviews, including the part where it points at the wrong problem. Aug 19, 2026 Causal Inference: How to Move From Correlation to Cause Causal inference is having a moment in the data industry, and it is not because the math is new. Rubin’s potential outcomes framework dates to the 1970s. Judea Pearl’s do-calculus is 30 years old. Difference-in-differences goes back to a ch Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Semantic Clustering Demystified: A Practical Guide URL: https://www.dimensionlabs.io/blog/semantic-clustering-demystified Blog # Semantic Clustering Demystified Semantic clustering groups text by meaning instead of exact words. Here it runs on 8,504 hotel reviews, including the part where it points at the wrong problem. Andrew Hong , CEO at Dimension Labs Feb 13, 2025 Updated Oct 19, 2026 6 min read Semantic clustering groups pieces of text by what they mean instead of the exact words they use. Feed it thousands of reviews or tickets and it hands back a few piles of similar comments, each with a label. It's a quick way into a large pile of customer text. People also tend to read more into the output than it can support, as the hotel example below shows. ## What is semantic clustering? Semantic clustering turns each piece of text into a list of numbers (a vector) that captures its meaning, then groups the vectors that sit close together. Two reviews that say "the room was filthy" and "housekeeping never cleaned" share almost no words, but a good representation puts them near each other, so they land in the same cluster. Keyword grouping cannot do that, because it needs the same word to appear in both. ## How it works - Represent the text. The simple option is TF-IDF, which weights words by how distinctive they are. The stronger option is a sentence-embedding model (the sentence-transformers family, or an embedding API), which places paraphrases close together even when no word overlaps. - Reduce, if needed. Embeddings have hundreds of dimensions. Methods like UMAP or PCA compress them so the clustering step is faster and less noisy. This step is optional for a few thousand texts. - Group. K-means asks you to pick the number of clusters up front. Hierarchical clustering builds a tree you can cut at any level. HDBSCAN finds clusters of uneven size on its own and leaves outliers unassigned, which suits messy feedback. - Name the clusters. Pull each cluster's most distinctive terms, or have a language model read a sample and write a label. Then read a sample of the comments yourself, because automatic labels are often off. ## A worked example: 8,504 hotel reviews Sample data: public Google reviews of 45 Marriott-family hotels in the Orlando metro, January 2025 to July 2026. We used the 8,504 reviews that carry a star rating. We ran the plain version on purpose: TF-IDF, k-means, eight clusters. TF-IDF is keyword-based, so this is the lexical baseline many teams try first. Sentence embeddings would group more paraphrases together; they would not fix the limits further down. The algorithm grouped the reviews mostly by tone. Six of the eight clusters were built on words like "amazing", "great location", "nice clean" and "excellent service". Together they held 59.5% of reviews, and only 2.4% of those reviews were one or two stars. The seventh, smaller cluster centered on the phrase "customer service". Almost every complaint went into the remaining cluster. It held 3,199 reviews (37.6% of the set) and 1,789 of the 1,967 one- and two-star reviews, 91%. Its top terms were room, hotel, parking, night, desk, which amounts to one pile for every kind of bad stay. Eight clusters, sorted mostly by tone 8,504 rated public Google reviews of 45 Marriott-family hotels in Orlando. Each cluster is named by its top terms. Sample data: public reviews, January 2025 to July 2026. TF-IDF and k-means (k=8), run by Dimension Labs on 2026-10-19. ### Clustering only the bad reviews The obvious fix is to cluster the 1,967 negative reviews on their own. That produced piles you can work with: parking charges and fees, smells and mold in the bathroom, credit card and refund disputes, the airport shuttle. Clustering only the 1,967 bad reviews One- and two-star reviews from the same hotels, clustered again. Each cluster is named by its top terms. The biggest pile is front-desk stories. On the same reviews, a causal model puts the effect of a front-desk mention at about +2 points (not significant), against +34 for a dirty room and +44 for rude staff. The biggest of them, 444 reviews or 22.6% of the negatives, was built on the words desk, said, told, check, asked: front-desk stories. If you treat cluster size as priority, the front desk goes to the top of the list. A causal model on the same reviews disagrees. Holding hotel, tier, language, review length and reviewer type fixed, it puts the effect of a front-desk mention on one- to two-star risk at about two points, not statistically significant. A dirty room adds 34 points. Rude or unprofessional staff adds 44. One likely reason front-desk stories pile up: angry guests tell the whole story, and the story usually passes the desk, where the complaint gets voiced. ## Where clustering stops ### Each review goes in one cluster K-means puts each review in exactly one cluster. A quick keyword check on the 1,967 bad reviews found that 33.1% name two or more distinct problems out of five we looked for (cleanliness, front desk, charges, staff attitude, something broken). Inside the front-desk cluster, 22.1% also mention a charge or fee and 23.2% mention something broken. Clustering files each of those reviews under one heading and drops the rest. ### A big cluster isn't necessarily a costly one A cluster tells you how many people said something similar. It says nothing about whether that thing moved the rating, the renewal or the refund. In the hotel data, the largest pile of bad reviews was the front desk, and it had close to no effect. ### Clusters change between runs Change the number of clusters, the random seed or a month of data and the boundaries shift. That is fine for exploring and a problem for anything you want to track week over week. ### Clusters follow word choice The first run sorted largely by adjectives, because in short reviews the adjectives are the most distinctive words. Nothing in the method knows that "great" is a feeling and "parking fee" is a problem. ## What to do after clustering Use clustering to learn the vocabulary of your feedback. Then switch to a fixed set of questions asked of every record. Did this review mention a charge? Was the room clean? Was someone rude? Each answer becomes a field, so one review can carry several of them at once. We call these fields Dimensions . Once every record has the same fields, you can join them to an outcome (the star rating, a renewal, a cancellation) and test which ones move it. That is the step that put the front desk near zero and dirty rooms (+34 points) and rude staff (+44) at the top. We walk through that test in Causal Inference: How to Move From Correlation to Cause , and compare the same pattern across hotels, gyms and restaurants in Which Complaints Cause Bad Reviews? . ## FAQ ### What is semantic clustering? A way of grouping text by meaning. Each text becomes a vector, and texts whose vectors sit close together form a cluster. It finds similar comments even when they use different words. ### Which algorithm should I use? Start with sentence embeddings, reduced with UMAP, then HDBSCAN if you have no idea how many themes to expect, or k-means if you want a fixed number of groups. TF-IDF is fine for a first look and for short, consistent text. ### Is semantic clustering the same as topic modeling? Close cousins. LDA models each document as a mix of topics. BERTopic is closer to clustering: it groups embeddings and gives each document one topic by default. Neither tells you which topic changes an outcome. ### Can clustering tell me what to fix first? No. At best it tells you which themes are common. To rank them by cost, tag every record with every issue it names and measure each issue's effect on the metric you care about. ## Try it on your own feedback Dimension Labs tags each review, ticket or survey response with Dimensions, then tests which ones move your numbers. Start with one file . ## More from blog Oct 19, 2026 Which Complaints Cause Bad Reviews? Hotel, Gym and Café Data In public reviews of a hotel group, a gym chain and a café chain, the most-mentioned complaint was not what drove the one- and two-star reviews. The numbers, and how to check your own. Oct 21, 2026 Why Gym Members Say They Will Cancel: Evidence From 9,186 Reviews In 9,186 public reviews of a gym chain, staff problems had the biggest effect on bad ratings, but billing complaints were the ones most likely to say the member was cancelling or warning others away. Aug 19, 2026 Causal Inference: How to Move From Correlation to Cause Causal inference is having a moment in the data industry, and it is not because the math is new. Rubin’s potential outcomes framework dates to the 1970s. Judea Pearl’s do-calculus is 30 years old. Difference-in-differences goes back to a ch Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## What Does a Conversation Designer Do? URL: https://www.dimensionlabs.io/blog/conversation-designer Blog # What does a Conversation Designer do? Since the world has gone digital, the demand for chatbots and voice assistants has been rising fast. Due to this, new roles like conversation designers are emerging that allow companies to create better conversational ex… Eric Griffing , Conversational AI Advocate Nov 12, 2024 8 min read Since the world has gone digital, the demand for chatbots and voice assistants has been rising fast. Due to this, new roles like conversation designers are emerging that allow companies to create better conversational experiences for their users through their knowledge of artificial intelligence and human psychology. This article will discuss what a conversation designer is, what they do, and what skills you need to become one. Here's what we'll cover: ## What is a conversation designer? Conversation design refers to the practice of determining the purpose of a conversational interface and how it'll work. A conversational designer is responsible for the entire user experience of a chatbot. They define the chatbot's purpose, design its user experience and content strategy, create a conversational flow, and test and iterate on it until everything works perfectly. Essentially, they design natural and easy-to-use chatbots and monitor them to ensure that the chatbot is doing its job well. Conversational designers work on everything from chatbots to voice assistants to video games. So, they need to understand human psychology as well as how computers process information to create an accessible and useful chatbot. For conversational interfaces to be truly successful, a designer must understand how to design for voice and text and how to think about designing conversations as a whole. Any company that wants to leverage technology to allow people to interact with products or systems more naturally will require a conversation designer. Some of the most popular companies leveraging these conversational AI technologies include Apple, Google, Microsoft, Zendesk, IBM, etc. ## How much is a conversation designer salary? According to Glassdoor, the average salary in 2022 is $41,678 per year . However, sources like ZipRecruiter mention that it could be as high as $97,473 per year . Several factors impact the salary, such as location, company size, amount of experience, job title, responsibilities, etc., explaining the variability in the data. ## What are the tasks and responsibilities of a conversation designer? Conversation designers typically work with a team of developers and other designers to create chatbots that meet the needs of their clients. They’re responsible for creating dialogue flow charts, writing dialogue, and testing chatbots to ensure they function correctly. A successful conversation designer must think creatively, solve problems quickly, and communicate effectively. Ankur Patankar, Conversation Designer at SmartAction , says, “No two days are quite the same for me. I handle multiple clients and projects at the same time. Some clients are patient and very open to new ideas. Some have a very specific idea of what they want and they just want it done. Oftentimes I'm trying to bridge that gap.” Here are three phases to which they contribute: ### Designing the conversation flows They’re responsible for creating the overall design of the conversation flow and interface. Designers consider the user's needs and objectives when designing the conversation. They also consider the company's branding when developing a chatbot's personality. - Understanding user behavior: It requires understanding user behavior, like what motivates users and how they behave within different contexts. For example, what kinds of questions do they ask, what's prompting them to ask these questions, what's the next logical step, etc. - Understanding user intent: User intent is a vital conversational AI concept, it drives everything from message length to response time. Understanding why a user wants to interact with the bot is crucial. - Personalizing based on user needs: For a conversational experience to be successful, the bot should adapt and change based on what the user says or does. If a designer creates a model that can analyze data from previous conversations and iterate accordingly, they can achieve this outcome. ### Developing and prototyping the conversations Works with developers to create the actual chatbot conversations. They write the script for the chatbot and design the conversation flow. They also test the chatbot to ensure that it is engaging and natural-sounding. - Designing mockups:   They need to create wireframes and mockups to explain the conversation flows based on which the bot offers responses. - Mapping conversation flows: Mapping the conversation beforehand gives developers and stakeholders an overview of how the chatbot will function. For example, it'll answer questions like: - How will your bot respond to different types of inputs? - How should it handle ambiguous or incomplete input? - What happens when the user doesn't say anything? - Prototyping with technical teams: Collaboration with engineers and the rest of the product team is a given. Designers must work with the entire team to ensure that the final prototype doesn't waver from the initial goal. ### Testing for accuracy and efficiency They're responsible for testing the chatbot to ensure it is functioning properly. Designers also test the chatbot's conversational skills to make it sound natural and human-like. - Validating the flow: Testing is a critical part of the designing process because it ensures that the flows designers have created produce the desired results once deployed. - Maps the error handling steps: As with any technology, chatbots are prone to errors too. So, designers must also include error-handling steps so that the failure rate is as low as possible. - Continuous iteration: Once the bot is deployed, the designer's job is not done. They still have to gather user feedback to ensure it works how users need it to. It means they have to monitor the bot's progress using analytics tools continuously and modify it. Ankur also adds, “Not much of my day is spent reworking the actual design; a large portion is reviewing change requests and tickets and communicating with internal and external stakeholders on my (and their) recommendations.” It shows that validation and testing is where a designer might spend most of their time. ## What tools does a conversation designer use? Typically, designers use wireframes for prototyping the user experience of a website or mobile app. The same principle applies to conversation flows too. To test these ideas, they can prototype and validate the ideas to ensure they have the correct logical setup. We’ve listed a few tools that are a part of every conversation designer’s tech stack: - Voiceflow : It's a tool that lets you design, prototype, and launch voice chatbots. You can create your voice chatbot and publish it with just a few clicks. - BotMock : It's a codeless rapid prototyping tool for building conversational apps. Users can build, test, and ideate their customer experiences for various platforms using the WYSIWYG editor. - Adobe : You can use Adobe XD to demonstrate your designs' interactive functions with prototypes and live demonstrations. - Miro : Although Miro is widely considered a whiteboarding tool, they have several templates you can use to prototype conversation flows. They have robust collaboration features too. - Figma : Figma is a cloud-based application for interface design with additional offline features. It's one of the most popular tools for prototyping and collaboration, as it's free but has many robust features. - LucidChart : It’s a web-based diagramming application that allows users to develop and share diagrams and charts for various purposes, not limited to design. - Sketch : It's a diagramming and prototyping tool native to the mac OS and can be used for the entire design-to-handoff process. - Amazon Lex : It's a service for building conversational interfaces into any application. This cloud-based service, trained using deep learning technologies, powers Amazon's Alexa. - Google Dialogflow : Dialogflow is an NLU platform that designs and integrates a conversational user interface into applications like voice-activated apps, bots, devices, and software. ## What skills does a conversation designer need? Conversation designers must have several hard and soft skills to contribute to this emerging field. Here's what is typically expected in the market: Based on our experience, we’ve listed a few must-have skills to excel in this field below: ### Natural Language Understanding (NLU) NLU is the ability of a computer to understand human language, and it's a critical part of conversational design. This knowledge is necessary for conversational designers to create a virtual assistant to understand natural speech and respond. They also need to know how natural language processing (NLP) and response generation works. All these technologies form a part of the base technology required to create these conversation flows. ### Interaction design It’s a field of study focusing on how users interact with products and services and how designers can use these interactions to create better interfaces. They do this by creating prototypes that allow them to test the usability of their designs, which helps them improve them before they go live. Ankur says that it’s important to study the transfer of data. “A conversation can be boiled down to how information is moved between multiple parties. If we talk about a football game, we're both recalling specific plays and deciding which were relevant to the point currently being made. How did the quarterback do? Well I can't remember, so maybe I'll talk about the defense instead. That's just information being (re)collected and transferred to the other person,” he adds. ### Prototyping & design Prototyping allows designers to test ideas before committing to them, which helps them determine if they work or need changes before moving forward. For this, they need to have the capabilities like creating logical flow diagrams and knowledge of tools like Voiceflow, BotMock, Figma, etc. ### Data analysis Conversational designers need to have data analysis skills to analyze how people use their products or services to determine what changes need to be made to improve them. It can include analyzing past interactions, listening closely for tone when communicating with users, or observing where drop offs and escalations occurred using chatbot analytics tools. ### Project management Conversational designers also need strong project management skills to work collaboratively with other team members, including product managers, visual designers, and engineers, to complete projects on time and within budget. Creating alignment across several teams and stakeholders can be a challenging part of the role. Agile scrum is a popular project management method because it allows them to break down large tasks into smaller pieces that you can complete over time as milestones are reached. ### Communication skills They need strong communication skills to communicate what they need to their team and vice versa. They must also be able to take feedback from their peers and clients, identify any issues with the design, and fix them. Moreover, they need excellent verbal and written communication skills as they're responsible for copywriting the conversations. ## Final thoughts on conversation designers Conversational designers make interfaces more natural, human, and conversational, i.e., less common, predictable, and more immersive for their users. They must understand the business’s goals and KPIs, create user scenarios, prototype conversational experiences, test with users (and iterate on their work), and create reusable components. They work on making the process from the first contact with a customer to the last step more user-friendly and pleasant, and are a great addition to any modern business. Considering that it’s a newly emerging field, the scope for this role is only bound to increase with time as more intuitive conversational interfaces will be needed. ## More from blog Nov 15, 2024 Chatbots vs Conversational AI: A Complete Guide Chatbots have become commonplace in the business world. You can find them on almost every website these days, which can be backed by the fact that 80% of customers have interacted with a chatbot previously. Nov 16, 2024 What Are Enterprise Chatbots And How Do They Work? Gartner defines chatbots as “A domain-specific conversational interface that uses an app, messaging platform, social network or chat solution for its conversations.” Nov 1, 2024 What is a Conversational Agent? A conversational agent is any dialogue system that conducts natural language processing (NLP) and responds automatically using human language. Conversational agents represent the practical implementation of computational linguistics and are Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## What Is Textual Data and Why It Matters to Support Teams URL: https://www.dimensionlabs.io/blog/textual-data Blog # What is Textual Data and Why Does It Matter to Customer Support Teams? Manually analyzing hundreds of unstructured text-based data sources is tedious and time-consuming. What’s worse is that since it’s in text format, you know this process can be made easier, but you’re unsure how to do it. Eric Griffing , Conversational AI Advocate Nov 17, 2024 6 min read Manually analyzing hundreds of unstructured text-based data sources is tedious and time-consuming. What's worse is that since it's in text format, you know this process can be made easier, but you're unsure how to do it. If you’re in a data-driven company that relies on such data sources to make critical decisions to optimize and improve your customer experience processes, keep reading. “The syndrome of Data-Rich Information-Poor (DRIP) is often encountered when a business tracks a lot of Key Performance Indicators (KPIs), and so, in theory, they have a lot of data on the operations of the business, but in practice, this KPI tracking does not lead to sustainable process improvements.” says Daniel Shapiro , CTO & co-founder of Lemay.ai. And he's right. Too often, we get stuck in the DRIP syndrome where we're happily generating data, but can't generate meaningful insights from them. Also, when you consider that 80% to 90% of data is unstructured , there’s so much potential waiting to be unlocked. In this article, we’ll discuss the concept of textual data and how you can use it to extract valuable insights for your customer support operations. ## What is textual data? Textual data is information that is stored and written in a text format. It can be anything from emails to blog posts to social media posts and online forum comments. In short, it's any data that has been expressed in words. As more people communicate online, the amount of textual data available grows exponentially, offering unprecedented opportunities for businesses and researchers alike. For instance, a customer support agent can extract real-time insights into their customers' wants by analyzing email tickets, chatbot conversations, and social media feedback. They have to process using a machine learning-powered (ML) tool that can do this for them. ## The advantages and disadvantages of textual data ## How is textual data related to dark data? Gartner defines dark data as "Information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships, and direct monetizing)." Drawing a parallel from dark matter in Physics, it means that there's a lot of untapped potential in this type of data, but organizations fail to realize it. It usually happens when they don't have the resources to analyze this type of unstructured data. But how does textual data fit into this? Here are a few examples: - Internal data: Internal data, like product data, daily logs, user search data, etc., can provide actionable insights into how customers interact with the product or service. You can spot trends such as common issues, user preferences, and correlations between products or services that may not have been apparent before. - Multimedia sources: You gain valuable insights into how customers interact with their products or services using text-based data such as chatbot transcripts. For example, teams can identify improvement areas and customer sentiment by analyzing conversations between customers and agents to optimize the customer experience. - Contact center data: support-related data sources like email transcripts, service desk tickets, call transcripts, etc., are helpful because it allows professionals to get a better understanding of consumer sentiment and behavior. You can consolidate feedback from multiple channels to extract data indicating sentiment at any given time. ## Techniques to analyze textual data Here are a few ways to analyze and extract critical insights from textual data: ### Natural Language Processing (NLP) - Text categorization: Also known as text classification, it's an automated process that analyzes text documents and classifies them into predefined topics or categories. It assigns labels accurately to unstructured data based on semantic content, enabling machines to comprehend and process natural language. - Sentiment analysis: This task evaluates the sentiment expressed in internal or external data sources (like customer reviews). It detects positive or negative sentiment from structured and unstructured data, allowing businesses to track changes in customer attitudes over time. - Dependency Parsing: Dependency parsing is responsible for analyzing the grammatical structure of a text by detecting which words depend on each other and how they relate to each other semantically. - Word Sense Disambiguation (WSD): This technique aims to identify the meaning of a word by looking at its context within a sentence. WSD can be used in language translation applications to improve machine learning models by reducing ambiguity. ### Textual Data Mining - Feature selection: The process of choosing the most relevant features from a given dataset to inform ML models. By selecting the most critical features, models can predict outcomes with fewer false positives or negatives. - Named Entity Recognition (NER): This sub-task of entity recognition focuses on identifying proper nouns that refer to specific entities such as people, locations, or organizations. For instance, it can recognize your customer's name or where they're from in your customer support tickets, adding more context. ## What is textual data visualization? Textual data visualization is the process of representing text data visually. It makes it easier to find patterns, trends, and similarities between different pieces of text. It uses topic modeling, linguistic analysis, and sentiment analysis techniques to analyze the data and create visual representations depending on the context. For instance, Dimension Labs helps you analyze thousands of customer conversations to discover how your customers feel about your product, what prompted them to purchase, and what issues they face. Using that data, you can avoid problems while visualizing the entire customer journey in one place. ## Use cases for textual data ### Identify important or trending topics from email tickets Tools like Dimension Labs can use textual data such as email tickets to track customer trends, issues, and inquiries. Doing this lets you stay one step ahead of customer needs and problems by quickly identifying trending topics. Plus, it gives customers the impression that their needs are being taken seriously, resulting in increased satisfaction and brand loyalty. ### Show the sentiment of conversations by analyzing the text You can also use Dimension Labs to understand customers' feelings and deal with negative conversations more strategically. For instance, if many customers are having an issue with a specific feature, the software will highlight that and the sentiment associated with it. Based on that, you can create specific resolution guides or allot more agents to tackle the incoming tickets. It also allows you to tailor the conversation in real-time and provide personalized solutions. ### Analyze the tone and sentiment of conversations to train agents You can use sentiment analysis to understand your customers and can uncover critical issues to take action on them. You can do this via the Key Activities feature, where topics are categorized by source, type of conversation, resolution rate, and sentiment. It helps you train support agents with concrete data, resulting in improved customer service and experience. ### Discover new trending support topics in real-time Dimension Labs can keep you ahead of the curve with its sophisticated unstructured data extraction capabilities. This means it continuously monitors customer support data to determine customer issues in real time. It ensures your team won't get caught off guard, and they'll be prepared for any sudden increase in user inquiries. ## Analyze your textual data using Dimension Labs Textual data helps organizations unlock their dark data insights, make better decisions, and gain real-time visibility. However, extracting valuable insights from textual data is a complex process. It requires advanced technological solutions such as AI and ML models to analyze it. Additionally, employing textual data visualization tools ensures that findings are presented in easy-to-understand visualization charts and graphs so you can share them across departments or organizations. You can use a tool like Dimension Labs to analyze and visualize it in one dashboard, making it an invaluable asset for any organization with access to it. Interested in seeing how you can use Dimension Labs to analyze textual data? Start with one file today, from $299 a month. ## FAQs: - What is textual data? Textual data refers to any information or message conveyed in written or printed form. It can include everything from books, articles, and surveys to social media posts, chat conversations, and emails. It is processed using NLP techniques that analyze the text's words, syntax, and context to extract meaningful insights. - How do you collect textual data? Several ways to collect textual data include surveys, chat transcripts, and social media monitoring. Here's how they work: - Surveys: Gather input from people directly with open-ended questions allowing for free-form text answers. - Social media monitoring tools: Track mentions of specific keywords or topics on various platforms and collect relevant text data automatically. - Chat/audio transcripts: Gather customer data from live conversations through chatbots or phone calls. - What is an example of textual analysis? An example of textual analysis is when a researcher analyzes written communication, such as books, magazines, or websites, to identify themes and patterns in the text. For instance, a tool like Dimension Labs can analyze customer conversations from multiple sources (chat transcripts, audio calls, support tickets) and highlight sentiment, trending topics, and critical issues using its machine learning-powered model. ## More from blog Nov 13, 2024 The Ultimate Guide To Unstructured Data Analytics According to the OECD, “Unstructured data are by far the most frequent type of data and thus provide the greatest potential for data analytics today.” Nov 14, 2024 Conversation Analytics: What It Is and Why It Matters Conversation analytics refers to the use of artificial intelligence (AI), specifically natural language processing (NLP), to analyze customer conversations and use them to understand their sentiment, intent, and interests. Nov 10, 2024 How to Build a Customer Experience Dashboard in 4 Steps Due to the lack of data, most customer service professionals struggle to triage requests and track customer sentiment over time. But that’s not the case anymore. Customer experience dashboards resolve this issue, giving them more direction Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## What Is a Conversational Agent? Types and Examples URL: https://www.dimensionlabs.io/blog/conversational-agent Blog # What is a Conversational Agent? A conversational agent is any dialogue system that conducts natural language processing (NLP) and responds automatically using human language. Conversational agents represent the practical implementation of computational… Eric Griffing , Conversational AI Advocate Nov 1, 2024 5 min read A conversational agent is any dialogue system that conducts natural language processing (NLP) and responds automatically using human language. Conversational agents represent the practical implementation of computational linguistics and are usually deployed as chatbots and virtual or AI assistants. A conversational agent is a virtual agent you can use to communicate with a human in natural language. It simulates human-to-human interaction and understands context and meaning just as humans do. It can talk to people on phones, computers, and other devices, allowing them to order food or do other functions through voice, text, or chat. It can achieve these using technologies like natural language processing (NLP), machine learning (ML), speech recognition, text-to-speech synthesis, and dialog management to interact with people through various mediums. In this article, we'll discuss how they differ from chatbots, how they work, and how they can be applied in different industries. ## Difference between chatbots and conversational agents A chatbot is a software program designed to simulate human conversation. You can use it to provide information, answer questions, perform tasks, and make purchases. They can be rule-based, too, which means they can only respond based on specific text or button inputs. In addition, these bots are more narrowly focused on their objectives. For example, they're either employed to resolve particular customer queries such as looking up an insurance policy or help with the e-commerce checkout process. On the other hand, conversational agents are programs that use NLP and natural language understanding (NLU) technology to converse with humans. The program can understand human emotions, answer basic questions, respond to commands, and interact through natural language conversations. These agents are often used to automate customer support and marketing campaigns. Basically, chatbots are conversational agents, but conversational agents are not necessarily chatbots. ## How does a conversational agent work? The programs used by conversational agents use technologies like NLU, semantic analysis, text generation , dialogue management, and dialog state tracking. Due to this, they’re able to understand what you say and respond appropriately. They do this by converting spoken words into machine code. This process is called automatic speech recognition (ASR). Then, NLU identifies the parts, names, and semantics (meaning) of words. Finally, text generation creates sentences based on the information gathered. While the NLU algorithms are doing their job, a dialog manager is also running simultaneously to ensure that the conversation starts from where it was left earlier. This process prevents the conversation from going off track and allows conversational agents to be used in multiple ways. ## Use cases of a conversational agent Conversational agents are often deployed via mobile apps, desktop applications, web pages, or other Interfaces. You can use them to automate customer support, sales, marketing, education, entertainment, and other tasks. Some of them include the following: ### Customer service Businesses can use conversational agents to answer questions quickly and efficiently without hiring additional staff or paying agency fees to an outsourced call center. Additionally, chatbots can handle routine tasks such as resetting passwords or booking flights which is quite common in enterprise chatbots. It can also answer basic product questions that don't require human judgment, making them ideal for low-cost services like online banking, where customers may not want to wait for a real person to get back to them. ### Information retrieval You can offer information about products or services through a chat interface instead of having the user search through articles on your website or in an app store search box. The user can ask about the price of an item, for example, and you can provide that information in real-time. It can be anything from pointing a customer towards an item they're looking to purchase or providing details on how to use your product. It can also offer answers to more nuanced queries like how many days it'll take to receive a product, etc. ### Revenue optimization Conversational agents can optimize sales by suggesting products to customers who have not yet purchased them. They can collect first party data and be connected to customer relationship management (CRM) or email marketing software to send cart abandonment emails. It can also prompt users within the app/browser window to encourage a purchase. It can help increase conversion rates by providing additional information about products that would go unnoticed. A conversational agent can replace popup windows and act as a modern-day concierge service for your site visitors. ## Examples of a conversational agent Here are a few examples of conversational agents that are currently being used in the market: - Iris: Conversational agent for data science tasks While most conversational agents focus on simple tasks like offering customer support and booking appointments, Iris is coded differently. This agent can help users accomplish complex data science tasks like plotting a histogram from a dataset or conducting statistical analysis for those datasets. The bot maps user inputs with pre-existing commands to decide what responses to offer. It does that by transforming those commands into automata that the bot can compose, sequence, and execute, providing the desired output. These commands require additional input, which the agent gets by either speaking to the user to resolve arguments or relying on previous conversations to understand the intended task. Using this agent, data scientists can complete predictive modeling tasks 2.6 times faster, decreasing the analysis time dramatically. - Woebot: Mental Health App Woebot is a mental health conversational agent that can help you monitor your mood and manage your mental health. It uses NLP, psychological expertise, and excellent copywriting to form a human-like conversation, making it easier for individuals to interact with it. It works on the principles of Cognitive Behavior Therapy (CBT), a therapeutic approach to challenge recurring problematic thoughts. It can help anyone, irrespective of age, and a recent study confirmed its ability to reduce anxiety and depression in those who use it. - Roof.ai: Real estate conversational AI chatbot Roof.ai is a conversational agent that helps real estate marketers automate interaction with leads and lead score assignment. Using Facebook as its prime channel, the bot interacts with potential leads and prompts them with questions that can help them qualify the lead. Once it assigns the score, it passes the conversation to a real estate agent who can take it forward. ## Leverage conversational agents for business productivity With the current rise of conversational agents, businesses have better customer access, and operation costs have been significantly reduced. They have several use cases in different departments like logistics, marketing, customer support, etc. Although we still need human involvement, the agent can handle more challenging tasks and take care of more straightforward questions without involving a human operator. It also helps build trust with customers and increases conversion rates. They also act as a shield between the user and human operator, reducing costs and allowing people more time to focus on other things while waiting for assistance. As the technology progresses, conversational agents will become more accurate with time, increasing the pool of potential applications. ## More from blog Nov 16, 2024 What Are Enterprise Chatbots And How Do They Work? Gartner defines chatbots as “A domain-specific conversational interface that uses an app, messaging platform, social network or chat solution for its conversations.” Nov 15, 2024 Chatbots vs Conversational AI: A Complete Guide Chatbots have become commonplace in the business world. You can find them on almost every website these days, which can be backed by the fact that 80% of customers have interacted with a chatbot previously. Nov 12, 2024 What does a Conversation Designer do? Since the world has gone digital, the demand for chatbots and voice assistants has been rising fast. Due to this, new roles like conversation designers are emerging that allow companies to create better conversational experiences for their Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Chatbot Analytics: 13 Metrics Every Business Should Track URL: https://www.dimensionlabs.io/blog/chatbot-analytics-to-improve-chatbot-performance Blog # Chatbot Analytics: 13 Metrics That Every Business Should Track How often have you built a chatbot and spent days thinking through every possible workflow only to deploy it and see that users never use them? Our guess: too often. The bigger issue is that your chatbot analytics tool m… Eric Griffing , Conversational AI Advocate Nov 18, 2024 7 min read How often have you built a chatbot and spent days thinking through every possible workflow only to deploy it and see that users never use them? Our guess: too often. The bigger issue is that your chatbot analytics tool may have marked this case as successfully closed as it contained the conversation. In reality, the customer interaction was negative, but your tool didn't account for that. Building and implementing a chatbot is not a one-time task. You need to regularly evaluate its key performance indicators (KPIs) to understand whether it’s impacting your bottom line. Simply put, you can’t improve its performance without measuring the right metrics. There are three broad categories of chatbot analytics you need to account for: - Engagement metrics: You can measure how users interact with the chatbot and its interaction level. You can use the data to decide if your strategy and conversation flows are working. - Conversion metrics: You can measure the number of conversions the chatbot achieves. The conversion goals can differ depending on the industry vertical and chatbot strategy. For example, software companies might measure demo sign-ups via chatbot conversations. - Retention metrics: You can measure your chatbot's relevance and acceptance by your target audience. It can indicate the success or failure of your chatbot strategy. Based on these categories, you can pinpoint specific metrics you need to track under each category and create a holistic chatbot strategy. In this article, we'll discuss critical chatbot analytics you should track to improve your chatbots, and maximize their return on investment (ROI). ## Which chatbot metrics should you track? Before understanding which metrics to track, let's understand what the term 'chatbot analytics' means. Chatbot analytics or chatbot metrics refers to the real-time data your bot produces every time it interacts with users. The benefits include: - Identifying customer issues via frequently asked questions - Determining how your website contributes to your lead generation process - Understanding how customers currently engage with your brand In a recent press release, Uma Challa , Senior Director Analyst, Gartner Customer Service & Support, said, "Customer Support & Service leaders have a positive future outlook for chatbots, but struggle to identify actionable metrics, minimizing their ability to drive chatbot evolution and expansion, and limiting their ROI." So, how do you know which KPIs to monitor to analyze its performance and success? Here's a list of critical metrics you need to look out for to test your chatbot's performance. ## User Metrics When we talk about users, we mean potential or existing customers. To understand how your chatbot is anchoring them to your website, here are a few metrics you can track: ### Total number of users The total number of users refers to the number of users interacting with the chatbot over its entire lifetime. You can use this information to determine how many users interact with your chatbots and pinpoint granular metrics like: - New users - Active users - Engaged users By measuring this KPI, you can determine whether or not your chatbot is enticing website/ app visitors. That's how you know your chatbot's design, call-to-action (CTA), and initial conversation prompt work well. ### New users New users refer to the number of unique users interacting with your chatbot within a specific period. This metric can gauge the success of external promotion efforts like paid advertising and organic social campaigns. You can also use this data to identify your current conversion rate by quantifying the number of leads generated via these campaigns. ### Engaged users Engaged users refer to active users who have conversations with your chatbot daily or weekly. This KPI can be used to understand how many users find your bot engaging, and prefer interacting with it to answer their queries. There are two key benefits of a high number of engaged users: - It could lead to potential or recurring business over time - It indicates the successful implementation of your chatbot ### Chat volume Chat volume refers to the number of successful interactions between users and the chatbot. By measuring this number, you can get more clarity on its usefulness, and the success of its conversation flows. The higher the chat volume, the better the capabilities of your chatbot. It helps you understand whether users find it easy to converse with the chatbot and how frequently a specific user is conversing with it. The latter overlaps as an engagement metric too. ### Goal completion rate (GCR) Goal completion rate refers to the rate at which the chatbot achieves specific goals the company sets. The key distinction between GCR and conversion metrics is that the former focuses on goals for various purposes, not just conversion. E.g., if the goal is to resolve 50 user requests in a day, and it resolves 40 of them, the bot achieves an 80% GCR. ## Engagement Metrics You can use these metrics to identify how your users interact with the chatbot and the common queries they have in mind. Here are a few metrics to measure engagement: ### Conversation length Conversation length refers to the total length of the bot’s and user’s interaction. A longer conversation can mean that the user is engaging with the chatbot, it doesn’t always mean that they’re a positively engaged customer. If the user closes the conversation due to lack of options, it’s an unsuccessful interaction. Instead, you can measure the average conversation length and determine whether it works for specific use cases. Average conversation length = Total duration of conversations in a specific timeframe (seconds) / Total number of conversations in the same timeframe ### Bounce rate Bounce rate refers to the number of users who visit the page but don't interact with the chatbot. It also detects the number of users who visit a single page and leave without interacting with other pages on the website. You can use this metric to determine if the website's design and content are interfering with the chatbot or if the chatbot fails to satisfy user intent. ### Flow completion rate (FCR) Flow competition rates (FCR) refer to the number of conversations that reach the endpoint of the conversation flow. It's useful when you want to understand whether the flow engages the user, and helps them find answers to their questions. Flow completion rate = Total number of completed flows / Total number of initialized flows ## Customer Satisfaction Metrics From a business perspective, if these conversational analytics fall short in these aspects, it could impact the overall customer experience. Here are a few metrics to measure customer satisfaction: ### Retention rate Retention rate refers to the percentage of users that return and engage with the chatbot within a specific timeframe. This KPI can be used to identify if customers are truly becoming part of your funnel (and eventually converting), but it’s context-dependent. E.g., if the customer returns the next day with the same issue they reported the previous day, the chatbot was ineffective in helping them. ### Fallback rate (FBR) Fallback rate (FBR) refers to the number of conversations that fail to understand the user's query. This metric is handy for businesses that use rule-based chatbots. A high FBR could indicate that the flow is not satisfactory and yields low user satisfaction. If a user chooses to speak to a support agent too quickly, it points toward a potential issue with the conversation flow. ### Customer satisfaction score The users provide customer satisfaction scores based on their experience with the chatbot. It's a straightforward way to gauge whether the quality of customer experience was optimal. Typically, the bot does this by offering a yes/ no question or a sliding scale (1-5 or 1-10). These options allow the company to gather feedback with minimal resistance as users can provide their inputs via a chat box. ### Human takeover rate or escalation rate Human takeover rate (HTR) or escalation rate refers to the number of times a customer executive had to take over the conversation and address the user’s questions. Usually, a high HTR indicates that the conversation flow is not good enough and needs to be improved. Although, there's a caveat. In specific scenarios, the next step would naturally involve engaging human agents, so it would be wrong for the bot to be deemed a failure. ### Leads captured This metric refers to the number of users who gave their personal information, like an email address or phone number, during the conversation. Most businesses use chatbots for lead generation, and it's an excellent way to capture high-quality leads as it can use qualifying questions to vet the lead and capture their data. It's important to note that not all chatbots need to offer this option, but it's one of the most direct ways you can use a chatbot for lead generation. ## Dimension Labs: Measure key chatbot analytics with ease Now that we know what bot metrics you need to track to analyze its performance levels let's look at a chatbot analytics tool that can do all this for you. Dimension Labs helps you easily understand all of the metrics that define success for your bot initiatives. It benchmarks and monitors the success of your bots and provides easy-to-interpret reports. You can monitor changes over time and receive alerts via our customizable dashboards to understand where the chatbot is improving or underperforming. It allows you to prioritize improvement projects with reports identifying underperformance factors quickly. Plus, you can integrate all your conversational data into one place and create a chatbot analytics  dashboard where learnings can be shared across your entire organization. ## Track the right chatbot metrics and maximize its ROI Listening to your users and using what they say can be a powerful way to improve your chatbot performance. Once you have launched your chatbot, the real work begins. It's essential to create a feedback loop between what workflows your bot currently handles, use conversational analytics to identify problems and make iterative changes to improve your KPIs. The more relevant data you have, the better you can optimize your chatbot for conversational success. Fortunately, chatbot analytics tools like Dimension Labs help you get these insights without having to do the heavy lifting. If you are looking for a better way to measure, analyze, and improve your chatbot's performance, start with one file today, from $299 a month. ## More from blog Nov 10, 2024 How to Build a Customer Experience Dashboard in 4 Steps Due to the lack of data, most customer service professionals struggle to triage requests and track customer sentiment over time. But that’s not the case anymore. Customer experience dashboards resolve this issue, giving them more direction Nov 14, 2024 Conversation Analytics: What It Is and Why It Matters Conversation analytics refers to the use of artificial intelligence (AI), specifically natural language processing (NLP), to analyze customer conversations and use them to understand their sentiment, intent, and interests. Nov 16, 2024 What Are Enterprise Chatbots And How Do They Work? Gartner defines chatbots as “A domain-specific conversational interface that uses an app, messaging platform, social network or chat solution for its conversations.” Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## A Guide to Unstructured Data Management | Dimension Labs URL: https://www.dimensionlabs.io/blog/unstructured-data-management Blog # A Guide to Unstructured Data Management In today’s data-driven world, organizations are inundated with vast amounts of information, much of which is unstructured. Unlike traditional structured data neatly organized in databases, unstructured data includes emai… Eric Griffing , Growth at Dimension Labs Sep 26, 2025 10 min read In today’s data-driven world, organizations are inundated with vast amounts of information, much of which is unstructured. Unlike traditional structured data neatly organized in databases, unstructured data includes emails, call transcripts, social media posts, live chats, and more. Managing this type of data effectively is becoming a critical business imperative. According to DemandTalk , in 2022, 90% of organizational data was unstructured, totaling an astonishing 57,280 exabytes. This explosive growth presents both challenges and opportunities for enterprises aiming to leverage their data fully. This guide explores what unstructured data management entails, the common challenges organizations face, best practices to adopt, and how modern data architectures are evolving to address these needs. ## What Is Unstructured Data Management? ### Definition and Core Concepts Unstructured data management refers to the processes, technologies, and strategies used to collect, store, organize, analyze, and derive insights from data that does not conform to traditional database schemas. This data type includes text documents, images, audio files, video content, social media interactions, and conversation data, among others. Unlike structured data, which fits into rows and columns, unstructured data is inherently more complex and diverse. Effective management involves cataloging all sources, normalizing formats, ensuring data quality, and enabling advanced analytics such as natural language processing (NLP) and machine learning. As Edward Calvesbert , Vice President of Product Management at IBM watsonx, notes, “ Most data being generated every day is unstructured and presents the biggest new opportunity ”. Organizations are increasingly recognizing the importance of unstructured data management as a critical component of their overall data strategy. With the rise of big data and the Internet of Things (IoT), the sheer volume of unstructured data generated daily is staggering. For instance, social media platforms alone produce billions of posts, comments, and images every hour, all of which can provide valuable insights into consumer behavior and preferences. By harnessing this data, businesses can enhance their decision-making processes, improve customer engagement, and drive innovation in their products and services. ### How It Differs From Structured Data Management Structured data management typically revolves around relational databases and data warehouses where data is organized into predefined schemas. This allows for straightforward querying and reporting. In contrast, unstructured data lacks a fixed format, making it harder to index, search, and analyze using traditional tools. Unstructured data often requires specialized tools that can clean & normalize the data, enrich or tag the data with LLMs, and output the data in analytics-ready formats. The volume and variety of unstructured data also tend to be much larger, demanding scalable storage and processing solutions that can handle diverse formats and sources. For example, while structured data might be easily managed through SQL queries, unstructured data often necessitates the use of machine learning algorithms to identify patterns and extract meaningful information. This complexity not only poses challenges in data management but also opens up new avenues for innovation, as organizations leverage advanced technologies to unlock the potential hidden within their unstructured data assets. ## Why Unstructured Data Management Matters in 2025 ### 80% of Enterprise Data Is Still Invisible Despite advances in data analytics, a significant portion of enterprise data remains unstructured and largely untapped. Recent reports indicate that approximately 85% of enterprise data is unstructured, complicating data governance and utilization efforts. This “invisible” data often holds critical insights into customer behavior, operational efficiency, market trends, and key product feedback. Without proper management, organizations risk missing out on valuable intelligence that can drive competitive advantage. The challenge lies not only in recognizing the existence of this data but also in developing the tools and frameworks necessary to analyze and leverage it effectively. As businesses evolve, the ability to harness unstructured data will become a key differentiator in the marketplace, allowing organizations to make more informed decisions and respond swiftly to changing customer preferences. ### The Cost of Ignoring Customer Conversations Customer interactions through calls, chats, emails, and surveys generate massive amounts of unstructured data. Ignoring these conversations can lead to missed opportunities for improving customer experience, identifying pain points, and anticipating needs. For example, sentiment analysis on call center recordings or chat transcripts can reveal emerging issues before they escalate. As organizations increasingly prioritize customer-centric strategies, integrating unstructured data from these channels into analytics workflows becomes essential to understand and respond effectively. The integration of machine learning algorithms can further enhance this process, providing predictive insights that allow businesses to tailor their offerings proactively. By analyzing historical customer interactions, companies can identify trends and preferences, enabling them to create personalized experiences that resonate with their audience and foster loyalty. ### Opportunities Hidden in Calls, Chats, and Surveys Unstructured data from voice calls, chatbots, and survey responses contains rich qualitative insights. Advanced AI techniques enable organizations to extract themes, detect sentiment, and even automate responses. Recent industry collaborations highlight this trend. In June 2024, IBM partnered with Telefónica Tech to develop AI and analytics solutions enhancing multilingual content discovery and cognitive automation across regulated sectors, showcasing the growing focus on unlocking value from unstructured data. Companies are beginning to recognize the potential of leveraging unstructured data not just for operational improvements but also for strategic decision-making. By employing natural language processing (NLP) techniques, organizations can analyze customer feedback at scale , uncovering insights that inform product development and marketing strategies. ## Common Challenges of Managing Unstructured Data ### Volume and Variety Across Sources The sheer volume of unstructured data is staggering and comes from diverse sources, chats, calls, surveys, and social, that vary widely in format and quality. Managing such variety requires flexible architectures and scalable storage capable of handling heterogeneous data types. For instance, social media platforms generate vast amounts of text, images, and videos, each requiring different processing techniques. Moreover, the rapid growth of IoT devices adds another layer of complexity, as these devices continuously stream data that must be captured, analyzed, and stored in real-time. As a result, organizations must invest in advanced data management solutions that can adapt to the evolving landscape of unstructured data. ### Tool Sprawl and Siloed Dashboards Organizations often deploy multiple point solutions to manage different unstructured data types, leading to tool sprawl. This fragmentation results in siloed dashboards and inconsistent data governance, making it difficult to get a unified view or conduct comprehensive analysis. Consolidating data and integrating workflows is necessary to break down silos and improve operational efficiency. Without this, teams struggle with duplicated efforts and incomplete insights. Additionally, the lack of a centralized data strategy can hinder collaboration among departments, as each team may rely on different tools and metrics to assess performance. This disjointed approach not only wastes resources but also limits the organization’s ability to leverage data for strategic decision-making. Implementing a unified data platform can help streamline processes and foster a culture of data-driven collaboration, ultimately enhancing overall productivity. ### Data Quality, Lineage, and Trust Ensuring data quality and traceability is particularly challenging with unstructured data. Unlike structured data, which benefits from strict validation rules, unstructured data can be noisy, incomplete, or inconsistent. Establishing data lineage, tracking the origin and transformations of data, is critical for compliance and trust. Organizations must implement robust data governance frameworks and employ AI-driven data cleansing and enrichment techniques to maintain reliability and regulatory compliance. Furthermore, fostering a culture of data literacy among employees is essential. When team members understand the importance of data quality and are trained to recognize potential issues, they can contribute to maintaining high standards. Regular audits and assessments of data quality can also help organizations identify gaps and implement corrective measures proactively. By prioritizing data integrity, businesses can enhance their analytical capabilities and make more informed decisions based on trustworthy insights. ## Best Practices for Unstructured Data Management ### Discover and Catalog Every Source Effective management starts with comprehensive discovery and cataloging of all unstructured data sources. This includes communication platforms, support tools, CRMs, and external data feeds. Automated metadata extraction and classification tools help maintain an up-to-date inventory, enabling better governance and searchability. Cataloging also lays the foundation for subsequent data processing and analysis. Moreover, it is crucial to establish a governance framework that outlines roles and responsibilities for data stewardship. This ensures that the cataloging process is not a one-time effort but rather an ongoing initiative that adapts to new data sources as they emerge. Regular audits of the catalog can also help identify redundant or obsolete data, thereby streamlining the overall data management strategy. ### Normalize and Clean Raw Logs Raw unstructured data often requires normalization, converting data into a consistent format, and cleaning to remove noise, PII, and errors. This step is essential for making data usable and reliable for analytics. Techniques such as text parsing, deduplication, and format standardization improve data quality and enable downstream processes like indexing and machine learning. Additionally, implementing automated cleaning pipelines can significantly reduce the manual effort involved in data preparation. By leveraging machine learning algorithms, organizations can identify patterns of errors and anomalies in the data, allowing for more intelligent cleaning processes that adapt over time. This proactive approach not only enhances data quality but also accelerates the analytics cycle, enabling quicker insights and more informed decision-making. ### Enrich with AI and LLM Pipelines Artificial intelligence (AI) and large language models (LLMs) play a transformative role in enriching unstructured data. They can automatically tag content, extract entities, detect sentiment, and summarize information. These enrichments add semantic layers that make unstructured data more accessible and actionable. Integrating LLMs into data pipelines allows organizations to harness the power of natural language processing to derive insights from vast amounts of text data, such as customer feedback and conversations. This not only enhances the understanding of customer sentiment but also informs product development and marketing strategies, creating a more responsive and customer-centric business model. ### Push BI-Ready Outputs Into Your Warehouse After processing and enrichment, unstructured data should be transformed into business intelligence (BI)-ready formats and pushed into data warehouses or lakes. This integration enables analysts and data scientists to combine structured and unstructured data for holistic insights. Providing clean, enriched datasets in familiar BI tools accelerates decision-making and maximizes the value extracted from unstructured data assets. Moreover, establishing a feedback loop where users can provide input on the usability of the BI outputs can lead to continuous improvement of data models and reporting formats. This iterative process not only enhances the relevance of the insights generated but also fosters a data-driven culture within the organization, empowering teams to rely on data for strategic initiatives. ## A Modern Approach: The Unstructured Data ETL Layer ### How It Fits Into the Modern Data Stack The traditional Extract, Transform, Load (ETL) process has evolved to accommodate the complexities of unstructured data. The unstructured data ETL layer acts as an intermediary that ingests raw unstructured inputs, applies normalization, enrichment, and quality checks, then delivers curated outputs to downstream systems. This transformation is crucial as unstructured data can come from various sources, including chats, calls, emails, and documents, each with its unique challenges and requirements. This layer integrates seamlessly with modern data stacks, supporting cloud-native architectures, real-time processing, and AI-driven analytics. It bridges the gap between raw unstructured data and actionable business intelligence. By leveraging advanced algorithms and machine learning techniques, organizations can derive meaningful insights from vast amounts of data that were previously considered too chaotic or inconsistent for traditional analytics. ### Why Data Teams (Not Just CX) Should Own It While customer experience (CX) teams often spearhead unstructured data initiatives due to their focus on calls, chats, and surveys, ownership should extend to broader data teams. Centralizing management under data engineering and analytics groups ensures consistent governance, scalability, and alignment with enterprise data strategies. This holistic approach enables organizations to maintain a single source of truth, reducing discrepancies and fostering collaboration across departments. Data teams bring expertise in data pipelines, quality assurance, and integration, enabling more robust and reusable solutions that benefit multiple departments. By having a dedicated team that understands the intricacies of unstructured data, organizations can implement best practices for data management, ensuring that insights derived from customer interactions are not only accurate but also actionable. ### The Rise of the Language Data Engineer As unstructured data increasingly involves natural language processing and AI, a new role is emerging: the Language Data Engineer. These specialists design and maintain pipelines that process text, speech, and unstructured data, applying NLP models and AI techniques to unlock insights. The importance of this role cannot be overstated, as the ability to analyze and interpret human language is becoming a cornerstone of competitive advantage in many industries. The Language Data Engineer blends skills from data engineering, computational linguistics, and AI, becoming vital in managing the growing complexity and volume of unstructured data in modern enterprises. This role not only requires technical proficiency but also a deep understanding of language nuances and proprietary business knowledge , enabling the extraction of richer insights from diverse data sources. With the unstructured data market projected to grow significantly, investing in such specialized talent and infrastructure is becoming a strategic priority. ## Final thoughts In closing, unstructured data is no longer a side note in enterprise analytics, it’s becoming the core driver of differentiation and innovation. The organizations that succeed in 2025 and beyond will be those that treat unstructured data not as a messy burden but as a strategic resource, one that, when properly cataloged, enriched, and integrated into the modern data stack, delivers insights impossible to capture from rows and columns alone. The challenge is real, but so is the opportunity: those who harness conversations, feedback, and free-form text at scale will not just understand their customers better, they’ll outpace their competitors. If you’re ready to get started, start with one file in Dimension Labs and see how we can help you unlock the hidden value in your unstructured data. ## More from blog Jan 13, 2025 Data Enrichment: Enhance Decisions with Next Gen Insights In today’s data-centric world, enterprises are inundated with vast amounts of data. A staggering 90% of the world’s data was generated in just the last two years, compelling organizations to seek innovative methods to extract meaningful ins Jan 31, 2025 Customer Interaction Analysis: Enhancing CX through Data-Driven Insights Customer Interaction Analysis (CIA) refers to the systematic evaluation of various customer touchpoints and interactions within an organization. By applying advanced analytical techniques, businesses can transform raw data from these intera Aug 28, 2025 The Missing Layer in the Modern Data Stack: Unstructured Data By 2025, more than 80% of enterprise data will be unstructured. Yet if you look at the modern data stack it’s almost entirely designed for structured data. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## The Ultimate Guide to Unstructured Data Analytics URL: https://www.dimensionlabs.io/blog/unstructured-data-analytics Blog # The Ultimate Guide To Unstructured Data Analytics According to the OECD, “Unstructured data are by far the most frequent type of data and thus provide the greatest potential for data analytics today.” Eric Griffing , Conversational AI Enthusiast Nov 13, 2024 6 min read According to the OECD , “Unstructured data are by far the most frequent type of data and thus provide the greatest potential for data analytics today.” You come across this type of data daily without even realizing it. For instance, the barrage of customer support tickets with text-based information is unstructured data. Because of their lack of uniformity, you can't analyze them in bulk without the help of advanced analytics tools. To help you navigate this, we’ll share an overview of unstructured data analytics and how it can help organizations like yours gain actionable insights that significantly impact your bottom line. We'll also discuss why it's important for customer experience (CX) teams, explore which data types are most often analyzed, and show you how unstructured data analysis works. ## What is unstructured data? Unstructured data is a form of data that doesn't have a predefined structure or format, making it difficult to manage and organize it. This data type is often found in business documents, such as SMS data, call transcripts, chatbot messages, emails, PDF documents, and more. For example, it’s hard to categorize your social media posts into definitive categories since each post has a different structure, topic, and goal. The lack of uniformity is what makes it unstructured. At Dimension Labs, we also refer to it as “ dark data ” because of its untapped potential. As dark data tends to be more information-dense due to its nature, analyzing it can open a doorway into unseen yet actionable insights for businesses. ## Structured data vs. unstructured data: what’s the difference? Before we explore how to analyze unstructured data, let's explore the key differences between structured and unstructured data. It should help you understand why analyzing unstructured data is not as simple as it seems. ## Examples of unstructured data Here are a few examples of unstructured data you can generate in a CX environment: - Customer feedback: These can be sourced through third-party review sites, chatbot transcripts, email feedback, customer feedback forms, blog comments, etc. - Open-ended surveys: You can get this from open-ended responses you receive from market research, customer feedback, event registration forms, post-service feedback forms, etc. - Emails: You can source this from any email you receive from customers. It can include unprompted customer feedback, customer support requests, responses to regular email communication, and more. - Chatbots: You can source this from your chatbots which can be present on any channel. For instance, it's common for companies to use multiple use cases or page-specific chatbots on their website, help center bots, social media bots, and internal support bots. The data can be in the form of text transcripts or audio files (voice calls with customers). - Social media: You can source this through the channels that your company is active on. For example, LinkedIn, Twitter, Reddit, Facebook, Instagram, Discord, etc. The files can be in the form of text or image/video format, depending on what the request is. - Websites: This can come from blog comments, contact form data (open-ended), product reviews, frequently asked questions, and more. - Customer support calls: These don't necessarily have to be handled by a bot. If you run a dedicated help center, exchanges between the customer and support representative act as a good data source for CX teams. ## How to analyze unstructured data Analyzing unstructured data is crucial as it can yield valuable insights into customer behavior and trends. It typically involves three steps: - Collecting relevant data from various sources like email, chatbots, social media, etc. - Cleaning and preprocessing the data using data analysis tools - Applying multiple AI-powered data analysis methods like sentiment analysis to extract insights Data collection usually starts with gathering data from unstructured data sources. Depending on the context, this could include social media posts, customer reviews, market research surveys, news articles, etc. Once you have relevant data, clean and preprocess it to ensure that only valid and useful information is used for analysis. It usually involves removing any irrelevant or invalid entries from the dataset and formatting the remaining entries into a suitable format for analysis. For example, text data like "Hi, how are you?" are removed to include only contextual data. Once the dataset is ready, it can be analyzed using various methods such as text mining, sentiment analysis, or machine learning. Here's how they help: - Text mining: Identifies patterns and trends in textual sources by searching for keywords or phrases within documents. - Sentiment analysis: Identifies emotional sentiment towards a specific topic in text-based sources such as social media posts or customer reviews. - Machine learning: Identifies patterns and finds correlations between different variables, which can provide valuable insights into customer behaviors and preferences. You should choose the technique based on the type of data you have. For instance, text mining would be a good fit if you need to analyze 1000 customer support tickets. On the other hand, if you're analyzing voice call transcripts, you will need advanced conversational AI tooling. Ultimately, this process requires specialized knowledge and technical expertise to achieve accurate results. If you’re curious about how to unlock your dark data, check out our recent webinar on the topic below: ## Use cases for unstructured data analytics Here are a few use cases of unstructured data analytics tools like Dimension Labs, especially for customer support professionals: ### Show customer data by channel With the help of these tools, users can dive deep into relevant metrics like customer sentiment and resolution rate across SMS, email, voice call, and more. Businesses can then use these insights to verify if their support systems, like chatbots, are working optimally or need adjustment for better results. ### Highlight negative sentiment conversations Using unstructured data, customers can identify negative reactions toward different types of support requests, giving them insight into what issues are causing the most dissatisfaction. If there's a noticeable increase in customers reporting specific issues like password change, Dimension Labs will highlight it and alert you. It helps you tackle the problem before it gets out of hand. ### Determine the top customer support requests Dimension Labs can help you identify trends like customer channel preference for specific use cases. Based on that, it can detect which topics and channels have the highest frustration, allowing you to better coach agents or redirect customers accordingly. ### Analyze tone and sentiment for agents to improve or train them You can quickly analyze tone and sentiment to understand the customer's experience. This capability helps you access Key Activities categorized by topics to act on them. For example, you can see what percentage of customers seek information or make transactions. It combines this data with context like resolution rate, average time spent, and sentiment so you can create a data-driven strategy to train your support agents. ### Show new trending support topics With its advanced unstructured data search and extraction capabilities, Dimension Labs can list topics emerging in customer conversations. It allows your internal teams to proactively prepare for a surge of user inquiries, helping ensure that customers have satisfactory experiences. ## Analyze your unstructured data using Dimension Labs Unstructured data is a potent tool for customer support teams, providing valuable insights and advantages you can't find elsewhere. This data type provides a more comprehensive view of customers' needs and experiences than structured data alone, leading to more informed decisions and better customer satisfaction. In addition, you can stay ahead of the curve by quickly identifying trends or issues that may arise. Tools like Dimension Labs allow customers to customize their experience when interacting with a business or organization. These unique benefits clarify why customer support teams should use unstructured data to maximize efficiency and effectiveness. ## FAQs for unstructured data analytics - What is unstructured data? Unstructured data is any type of data that does not have a predefined structure or format. It could include audio, images, videos, email messages, and web pages. It requires specialized tools such as NLP or image processing algorithms to extract information from it. Unlike structured data, which is organized in a tabular form and can easily be queried using SQL, unstructured data needs to be heavily processed to extract valuable insights. - What tools are used for analyzing unstructured data? Any tool that offers the capabilities of NLP, text mining, or machine learning can help you analyze unstructured data. Dimension Labs is an excellent example for this purpose. You can process and analyze your customer support data through our NLP model to determine how your customer truly feels about your product/service. - Why is unstructured data difficult to analyze? Unstructured data lacks structure and does not adhere to a predefined data model. This lack of structure makes retrieving and interpreting information from this type of data more challenging. The data can be messy and incomplete, making it hard to parse necessary details without AI-powered data analysis tools. ## More from blog Nov 17, 2024 What is Textual Data and Why Does It Matter to Customer Support Teams? Manually analyzing hundreds of unstructured text-based data sources is tedious and time-consuming. What’s worse is that since it’s in text format, you know this process can be made easier, but you’re unsure how to do it. Nov 10, 2024 How to Build a Customer Experience Dashboard in 4 Steps Due to the lack of data, most customer service professionals struggle to triage requests and track customer sentiment over time. But that’s not the case anymore. Customer experience dashboards resolve this issue, giving them more direction Sep 26, 2025 A Guide to Unstructured Data Management In today’s data-driven world, organizations are inundated with vast amounts of information, much of which is unstructured. Unlike traditional structured data neatly organized in databases, unstructured data includes emails, call transcripts Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Predictive Churn: Models, Data and the Missing Why URL: https://www.dimensionlabs.io/blog/predictive-churn Blog # Predictive Churn: A Complete Guide to Models, Data, and the Missing "Why" Layer A Head of Customer Success at a mid-market SaaS company opens her Monday churn report. Forty-seven accounts sit above the 70% risk threshold, representing $4.2M in ARR. She has eight CSMs covering nearly 600 accounts. Sh… Eric Griffing , Growth Marketing Apr 14, 2026 8 min read A Head of Customer Success at a mid-market SaaS company opens her Monday churn report. Forty-seven accounts sit above the 70% risk threshold, representing $4.2M in ARR. She has eight CSMs covering nearly 600 accounts. She forwards the list to her team to review. Two weeks later, retention on the flagged accounts looks no better than on the unflagged ones. A handful were saved through the usual escalation playbook. Several churned on schedule. The model did its job. The score was accurate. The probabilities were well-calibrated. And yet the intervention failed, because a risk number by itself doesn't tell anyone what to say, what to fix, or who to call. So what's missing between an accurate churn score and an effective save? Key takeaway: Predictive churn models tell you who is likely to leave and how likely they are to leave, but not why . Without a causal layer built on unstructured customer data, risk scores produce lists, not retention plans. ## What is predictive churn? Predictive churn is the practice of using historical customer data and statistical or machine-learning models to estimate the probability that a given customer or account will stop using a product, cancel a subscription, or reduce spend within a defined future window. The output is usually a risk score between 0 and 1, a percentage probability, or a tiered segment (low, medium, high). The goal is straightforward: surface at-risk customers early enough that a retention team can act before the cancellation is final. For subscription businesses, this is a high-leverage discipline. Gartner's 2025 survey of revenue leaders found that 73% of organizations are prioritizing growth from existing customers, which makes predictive churn one of the more heavily invested analytics use cases in B2B SaaS today. The discipline is mature. Most mid-market and enterprise SaaS companies have some version of a churn model running, whether built in-house, inherited from a CRM, or pulled from a customer success platform. What varies enormously is what teams actually do with the output. ## How predictive churn models work Every predictive churn model, regardless of its sophistication, follows the same four steps. Historical customer records are labeled as "churned" or "retained" over a past window. Features are engineered from behavioral, transactional, and firmographic data. A model is trained on the labeled dataset to learn which feature patterns precede churn. New customers are scored against the trained model on a recurring basis, weekly, daily, or in real time. Typical data inputs fall into five buckets: product usage (logins, feature engagement, session frequency), billing and contract data (payment history, plan tier, renewal dates), support interactions (ticket volume, severity, resolution time), firmographics (industry, company size, region), and tenure (days since signup, days since last expansion). The strongest models combine all five. What's worth noticing is that every one of those data sources is structured . Numeric, categorical, tabular. That constraint shapes everything the model can see, and everything it can't. ## Common predictive churn models compared There's no universally best model for churn prediction . The right choice depends on data volume, feature complexity, interpretability requirements, and whether the team needs to explain predictions to non-technical stakeholders. The five models below cover the large majority of production churn systems in use today. Model How it works Best for Main limitation Logistic regression Estimates churn probability from a linear combination of features Teams that need interpretability and a reliable baseline Struggles with non-linear relationships and feature interactions Decision trees Splits data into branches based on feature thresholds ("if logins < 5 AND tickets > 3, high risk") Teams that need human-readable rules for CS handoff Prone to overfitting on small datasets Random forest Averages predictions from many decision trees trained on data subsets Mid-sized datasets with mixed feature types Less interpretable; harder to explain individual predictions Gradient boosting (XGBoost, LightGBM) Builds trees sequentially, each correcting errors from the last High-accuracy production systems with tabular data Requires tuning; computationally heavier Neural networks Learns complex patterns through layered connections Very large datasets with raw signals or text/image inputs Low interpretability; hard to justify individual scores to CSMs In enterprise B2B contexts, where churn is rare and each account carries significant revenue, gradient boosting and random forest tend to dominate production deployments. Logistic regression remains the preferred baseline because precision and recall, not raw accuracy, are what matter when the base rate of churn is low. ## Where predictive churn falls short Here is where the practice hits a ceiling that no amount of model tuning can break through. Predictive churn fails in three specific and related ways. It produces scores without drivers. A customer flagged at 82% risk offers no information about why the model assigned that score. Feature importance charts can tell a data scientist which variables moved the needle in aggregate, but they don't explain a specific account. Was it the drop in weekly active users? The escalated ticket about the integration? The new executive sponsor who went quiet in October? The score collapses all of those into a single number. It inherits the blind spots of structured data. Gartner has estimated for years that 80–90% of enterprise data is unstructured, the transcripts, emails, survey verbatims, call recordings, chat logs, and meeting notes where customers actually explain their dissatisfaction. Predictive churn models are trained almost exclusively on the 10–20% that's structured. Deloitte's research found that only 18% of organizations leverage unstructured data in their analytics, and that those who do are 24% more likely to exceed their business goals. The churn reasons live in the data most models never see. It doesn't surface a next action. Even when a risk score is accurate, it terminates in a list. CSMs receive a spreadsheet of flagged accounts and are expected to generate the retention strategy themselves, on top of their existing workload. The model produces a symptom; the human has to produce the diagnosis and the treatment. That's why so many churn programs feel like busy work, the score is the beginning of the real analytical work, not the end of it. ## What makes a predictive churn program actionable The difference between a churn program that produces reports and one that produces saves comes down to five requirements. Teams that consistently retain at-risk accounts have all five in place; teams that struggle usually stall somewhere between the second and the third. - A clear and consistent churn definition. Is churn non-renewal, downgrade, seat reduction, or a 30-day usage gap? The definition shapes every downstream decision, from model features to CSM escalation thresholds. Ambiguity here cascades into everything else. - Driver-level explanation for each prediction. Every flagged account should arrive with a specific, named reason, not just a score. A CSM opening an account view should see "primary driver: declining feature adoption in the reporting module" rather than "risk: high." - Coverage of unstructured signals. Calls, tickets, survey responses, and chat logs need to be parsed, labeled, and fed into the same analytical layer as usage and billing data. Public reviews can add a signal too: in 9,186 reviews of a gym chain , members who raised a billing problem said they were leaving about half the time. Without this, the model will always miss the reasons customers actually give. - Cohort-level pattern detection. If eight accounts in the manufacturing segment are all flagging for the same reason in the same quarter, that's a product or GTM signal, not just a CS escalation. Actionable programs roll causes up to patterns. - A named owner for every intervention. A risk score without an owner is a notification, not a plan. The best programs route flagged accounts automatically to a specific CSM, AE, or executive sponsor with a suggested action attached. The first and fifth are operational. The middle three are data problems, and they're the ones most teams underestimate. ## Closing the gap: adding causal context to predictive churn A causal layer sits on top of predictive scoring rather than replacing it. The model still flags the account. What changes is what arrives with the flag: the specific reason the score moved, drawn from the unstructured data the predictive model can't read on its own. This is where most existing stacks break down. The question worth asking isn't "do we have a churn model?", it's "can the model answer the follow-up question?" Capability Structured-only churn model Predictive + causal intelligence Flag at-risk accounts Yes Yes Assign a numeric risk score Yes Yes Identify which product-usage features drove the score Partially Yes Surface the specific reason a customer gave on their last call No Yes Group flagged accounts by root cause across cohorts No Yes Recommend a next action tied to the cause No Yes Connect rising churn risk to a product or pricing change No Yes If most of your answers stop after the third row, you have a prediction system without a diagnosis system, and that gap is what turns churn reports into busy work. This is the gap Stake.com closed when they began processing 1.5M+ monthly player conversations as a structured intelligence layer alongside their behavioral data. Instead of retention teams working from usage-based risk scores alone, they now see the specific reasons players disengage, surfaced from chat, support, and in-product conversations at scale. The full story is here . What does the "after" state look like in practice? Instead of a 47-row spreadsheet of flagged accounts, a CS leader opens her Monday report and sees something closer to this: Churn risk elevated on 12 accounts in the mid-market segment. Primary driver: dissatisfaction with the new pricing tier introduced in Q3, mentioned in 9 of 12 most recent calls. Supporting evidence: 47% increase in pricing-related support tickets from this cohort since August; NPS verbatims flagging "confusing tier structure." Recommended action: executive sponsor outreach with the pricing exception playbook; flag to product and RevOps for structural review. That is the difference between a risk score and a retention plan. ## From risk scores to retention plans Predictive churn, as a discipline, has largely solved the question of who . The models are accurate enough. The data inputs are well understood. The algorithms are mature. So why do so many churn programs still feel like treading water? And why do the saves that do happen so often depend on a single CSM's intuition rather than the model's output? The ceiling isn't in the prediction. It's in everything that has to happen between the score and the save, the explanation, the diagnosis, the pattern recognition, and the handoff. A risk score is a starting point. The work of retention begins with understanding the causes behind why the score moved. ## Frequently asked questions about predictive churn What's the difference between predictive churn and churn analysis? Predictive churn looks forward, it uses historical data to estimate the probability that a specific customer or account will leave in a future window. Churn analysis looks backward, explaining what happened after the fact: which cohorts left, when, and under what conditions. Most mature retention programs run both in parallel, but prediction is what drives proactive intervention. How accurate are predictive churn models? Accuracy is the wrong metric to optimize for, because churn is usually a rare event, a model that predicts "no one will churn" can score 85–90% accurate and still be useless. Precision (the percentage of flagged accounts that actually churn) and recall (the percentage of actual churners the model caught) are the numbers that matter. Well-tuned enterprise models typically land in the 60–80% range on both, depending on data quality and how churn is defined. What data do you need to build a predictive churn model? At a minimum: product usage data, billing and contract history, support ticket history, and firmographic attributes like plan tier and tenure. The stronger the unstructured signal layer, call transcripts, survey verbatims, chat logs, ticket free-text, the more the model can explain why a score is moving rather than just flagging that it has. Most teams underinvest in the unstructured side and over-index on usage metrics. How often should churn predictions be refreshed? For most B2B SaaS businesses, weekly refresh is the practical baseline, fast enough to catch deterioration before a renewal window closes, slow enough to avoid noisy score volatility. High-velocity or usage-based products sometimes move to daily scoring. Quarterly or monthly refresh cycles, which are still common, are usually too slow to support timely intervention. ## More from blog Apr 14, 2026 The Analytics Stack in 2026: Layers, Tools, Trends, and What’s Still Missing A VP of Analytics at a SaaS company just received the monthly operational report. Net revenue retention dropped from 112% to 104% in Q3, an eight-point swing that rewrites the board deck. Aug 11, 2026 How to Build a Churn Risk Report: The Methodology, the Joins, and the Dimensions Your CRM can tell you exactly which accounts churned last quarter, but it cannot tell you which ones are about to. Jun 4, 2026 Voice of the Customer Best Practices: A Quick Guide Voice of the customer programs fail quietly. The surveys go out, the tickets get tagged, the sentiment dashboard stays green, and then churn spikes and nobody can explain why. Not because the feedback wasn’t there, but because the program w Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## How to Build a Customer Experience Dashboard in 4 Steps URL: https://www.dimensionlabs.io/blog/customer-experience-dashboard Blog # How to Build a Customer Experience Dashboard in 4 Steps Due to the lack of data, most customer service professionals struggle to triage requests and track customer sentiment over time. But that’s not the case anymore. Customer experience dashboards resolve this issue, giving … Eric Griffing , Conversational AI Enthusiast Nov 10, 2024 7 min read Due to the lack of data, most customer service professionals struggle to triage requests and track customer sentiment over time. But that's not the case anymore. Customer experience dashboards resolve this issue, giving them more direction on what to focus on. In this article, we'll take you through what a customer experience (CX) dashboard is, how it can benefit your organization, what to measure using it, and how to build one in four simple steps. ## What is a customer experience dashboard? A customer experience dashboard is a powerful tool for businesses looking to get the most out of their customer interactions. This data-driven platform provides invaluable customer insights into how customers engage with your brand by tracking key metrics such as satisfaction scores, effort scores, journeys, customer churn rate, and sentiment analysis. Unless your customer experience (CX) is top notch, you won't be able to survive as a business. And many studies have proved that. For instance, a recent report from Watermark Consulting found that if a company prioritizes CX as a function, they’re highly likely to outperform the market, almost 3 times more than those who don’t. This dashboard helps you monitor customer feedback and measures the impact of changes over time. The intention is to always stay on top of your customer's needs, even as they keep changing with the seasons. ## Why do you need a customer experience dashboard? Let's say one of your customer service reps reports facing an influx of return requests for a specific product. But how do you know if it's just one rep's observation or a commonality across the board? This is where a customer experience dashboard can help. By surfacing these critical insights, you can quickly identify new areas for improvement and use cases for customer experience automation. In the CX Leader podcast , Gary Szeszycki , Vice President of Client Services at Walker Information, says, "When you think about what our CX professionals have to deal with on a day-to-day basis, in most of their organizations, they actually have to compete for mind share with everyone else in the organization. Unless your entire company lives and breathes CX data, the only people that are going to be living and breathing CX data is you. And now you've got to bring that message to your organization." Essentially these dashboards consolidate qualitative and quantitative data from different customer feedback channels and show you the customer health score of your business. You understand how customers interact with your products or services, enabling you to make informed decisions about optimizing the customer experience. For instance, New Day USA, a mortgage company, noticed that 15% of their customers were opting out of their plans, costing them $400,000 in revenue each month. To avoid this, they investigated why this was happening and found that it was due to a lack of communication during the loan disbursement process. Eventually, they implemented an engagement program to keep them apprised, resulting in a 7% reduction in drop-off numbers and $200 million in additional revenue each month. Also, you become data-driven over time and double down on what's working while improving areas of weaknesses before it's too late. ## What can you measure with a customizable customer experience dashboard? Here are a few customer service metrics you need to keep an eye on: - Customer satisfaction score (CSAT): Measures customer satisfaction with a product or service, determined by gathering customer feedback through channels like surveys. - Churn rate: Percentage of customers who stop working with a company over a set period. For example, if, on average, 5 out of 100 customers stop working with you, your churn rate is 20%. - First contact resolution rate: Measures the percentage of customer support requests resolved on the first attempt by a customer service representative. The higher this number, the more efficient your customer support system is. - Ticket volume: Represents the number of customer support requests a company receives over a given period. It is a crucial metric for tracking customer demand and helps companies allocate resources and prioritize support issues. - Average resolution time: Measures the time for a customer support ticket to be resolved. The lower this number, the better your customer support system. - Net Promoter Score (NPS): A customer loyalty metric that measures if a customer is likely to recommend a company to others. Customers rate the product/service on a 10-point scale, with 10 being "highly likely to recommend." - Customer retention rate (CRR): Measures the percentage of customers a company retains over a given period. If your CRR is high, it shows that your product meets the target market's needs and that customers are satisfied with the quality. - Customer Effort Score (CES): It's a metric that measures the ease with which customers can resolve a support issue. For instance, if a customer has a problem with a product feature and they could fix it with a knowledge base article, it reduces the effort it takes on their part. - Customer sentiment: It's a qualitative metric that measures the attitude and emotions of customers towards the brand. While this can change with time, your goal should always be to maintain a positive sentiment. - Customer journey flows: This maps the path customers take when interacting with a company. It helps companies understand CX, identify areas where customers are encountering problems or expressing dissatisfaction, and take action to address them effectively. - Topic insights: These represent the most common issues customers experience when interacting with a company. It shows common points of frustration, giving you data points on what support efforts to prioritize in the future. ## How to build a customer experience dashboard in 4 steps Let’s look at how you can create a customer experience dashboard for your organization: ### 1. Define your customer experience goals, KPIs, and metrics By understanding the metrics used by your customers, you can uncover behavioral patterns, identify areas of improvement and create more personalized services, all of which are necessary for creating a successful CX Dashboard. Include a mix of these metrics: - Perception metrics: How they feel about it (NPS survey, CES scores) - Descriptive metrics: Observable events (First call resolution, Average Handle Time, Escalations) - Outcome metrics: Actions customers took (Booked a demo, viewed a demo, etc.) It’s crucial to choose qualitative and quantitative metrics so that the former supports and provides context to the latter. For instance, you might see that a customer scored you a 7 on the NPS survey, but what's the context behind that? Were they unhappy with certain features, customer support, or something else? Including a text box and an AI-powered BI platform to analyze these insights would give you a better picture. The most important metrics include customer satisfaction scores, retention rates, topic insights, and sentiment. By tracking these components over time, you'll better understand how your customers interact with your products or services, and where they'd like to see improvements. ### 2. Choose the right data sources for your dashboard Choose the correct data sources for your dashboard. Platforms like Dimension Labs integrate with multiple channels like chatbots, voice, survey tools, SMS, and support ticketing systems to gather data from these sources. So, pick your channels and use our API to pull the data into your dashboard. For example, surveys are an easy way to get direct feedback from customers. Reviews can provide valuable insight into customer needs and expectations. And monitoring social media conversations related to your brand can reveal much about what people think of your services or products. ### 3. Organize your data in Dimension Labs data platform Choose a solution that integrates with your data sources and auto-organizes it. Dimension Labs' platform can help you do just that. It does that in three simple steps: - Connect your data sources so that our natural language processing (NLP) model can run a qualitative analysis - Automate the data organization process and convert unstructured data into a structured form - Explore the extracted data and hone into areas of improvement and high satisfaction initiatives ### 4. Analyze the results and make informed decisions Before you analyze the results, decide what you need to evaluate in the first place. In our experience, most clients like to focus on different metrics and change the dashboard's flow based on internal preferences. So, adjust the dashboard first and then begin the analysis process. In the image below, you can see how we've grouped critical insights like total sessions, Key Activities, Topic Insights, Sentiment, and User Journey Flows on one interface. It gives users a high-level overview of qualitative metrics that shows how their customers perceive them at this very moment and the most critical issues they're facing. You can make faster data-driven decisions using this, as the proof is in the pudding. ## Analyze the voice of your customer base with Dimension Labs A customer experience dashboard is an essential tool for any customer support team as it helps you drill down into the customer health of your business. Dimension Labs' platform allows you to quickly build and customize your dashboard and effectively measure the metrics that are important to you. Integrating and organizing your conversational data into one place creates a single source of truth, giving you confidence in your support efforts. Plus, as a customer support leader, you can increase your voice of the customer (VoC) data, automate the generation of actionable insights, and identify areas for cost savings so that you invest in channels that work best. Are you interested in learning how Dimension Labs can help you create a customer experience dashboard for qualitative data? Start with one file today, from $299 a month. ## FAQs for Customer Experience Dashboards: - What is a customer experience dashboard? A customer experience dashboard is a tool businesses use to monitor and analyze customer interactions with their brands. It visually represents customer data, allowing companies to quickly identify customer experience trends, pinpoint issues, and evaluate customer satisfaction levels. By having a centralized location for all customer-focused metrics, businesses can make data-driven decisions that improve customer retention and loyalty. - What should a customer experience dashboard include? At a minimum, it should include customer satisfaction, feedback, and engagement data. It might also include key performance indicators such as most requested support topics, customer satisfaction scores, customer sentiment, average resolution rates, and customer journey flows. - How do I create a customer experience dashboard? You can create a customer experience dashboard in 4 simple steps: - Define your customer experience goals, KPIs, and metrics - Choose the right data sources for your dashboard - Organize your data in a BI tool (Dimension Labs) - Analyze the results and make informed decisions When you do so, you create an automated dashboard that regularly pulls and analyzes data without manual data annotation from your end. ## More from blog Nov 18, 2024 Chatbot Analytics: 13 Metrics That Every Business Should Track How often have you built a chatbot and spent days thinking through every possible workflow only to deploy it and see that users never use them? Our guess: too often. The bigger issue is that your chatbot analytics tool may have marked this Nov 13, 2024 The Ultimate Guide To Unstructured Data Analytics According to the OECD, “Unstructured data are by far the most frequent type of data and thus provide the greatest potential for data analytics today.” Nov 14, 2024 Conversation Analytics: What It Is and Why It Matters Conversation analytics refers to the use of artificial intelligence (AI), specifically natural language processing (NLP), to analyze customer conversations and use them to understand their sentiment, intent, and interests. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## How Knowledge Graphs and Agentic Workflows Use Unstructured Data URL: https://www.dimensionlabs.io/blog/how-knowledge-graphs-and-agentic-workflows Blog # From Chaos to Context: How Knowledge Graphs and Agentic Workflows Are Reimagining Unstructured Data For years, the analytics world has revolved around structure. Rows, columns, schemas, and dashboards have been our tools for understanding reality. Yet beneath this structured world lies a vast ocean of unstructured data… Andrew Hong , CEO at Dimension Labs Nov 17, 2025 5 min read For years, the analytics world has revolved around structure. Rows, columns, schemas, and dashboards have been our tools for understanding reality. Yet beneath this structured world lies a vast ocean of unstructured data, customer conversations, support chats, survey responses, emails, transcripts, the raw language of how customers think, feel, and act. This unstructured layer holds extraordinary intelligence. But it’s historically been too messy, too dynamic, and too context-rich for traditional analytics. BI tools can visualize, but they can’t reason . Databases can query, but they can’t understand . That’s changing, fast. We’re now entering a new era where Knowledge Graphs and Agentic Workflows are redefining how we interpret, connect, and act on language data. This fusion marks the beginning of what I call The Age of Language Intelligence , where every insight, every decision, and every workflow can be dynamically powered by the meaning encoded in human language. At Dimension Labs , we’ve spent the past several years building toward this frontier. Our goal: transform the world’s unstructured conversational data into structured, queryable intelligence, not by forcing it into rigid schemas, but by giving it a living, contextual brain. ### The Hidden Layer of Enterprise Intelligence Every organization already has its data warehouse, the backbone of structured analytics. But ask any data leader, and they’ll admit: the real insights are locked in their “dark data.” Customer feedback forms, transcripts of support calls, sales notes, chat logs, these are where the truth lives. They reveal why customers churn, how they feel about product changes, and where inefficiencies originate. Traditional NLP pipelines and sentiment analysis gave us a glimpse, but they’re still fundamentally shallow. They treat language as a flat surface, a collection of keywords and emotions. What they miss is the web of relationships , between entities, intents, causes, and effects. To make unstructured data truly usable, we don’t just need better models, we need structure that understands meaning . That’s where Knowledge Graphs come in. ### The Missing Intelligence Layer: Knowledge Graphs A Knowledge Graph transforms disconnected data points into a network of meaning . Each node represents an entity, a concept, a person, a product feature, a customer issue, and each edge defines how those concepts relate. In a customer analytics context, this means mapping out the entire semantic landscape of interactions: - Which product features are most discussed alongside “frustration”? - What topics correlate with high and low NPS scores? - Which issues tend to precede churn events? By encoding these relationships, Knowledge Graphs allow machines (and humans) to navigate complexity intuitively, moving beyond “keyword counts” to actual knowledge reasoning . At Dimension Labs, every dataset processed by our platform is automatically converted into a knowledge graph of insights . This graph evolves dynamically: new conversations enrich existing clusters, new themes emerge organically, and correlations strengthen over time. What emerges is not just an index of language, it’s a living model of customer intelligence. ### Agents: The New Workforce for Data Analysis If Knowledge Graphs represent context , then Agents represent action . The rise of agentic AI, autonomous, multi-step systems that can reason, retrieve, and execute, introduces a new way to interact with data. Agents are no longer passive responders to prompts; they are collaborators that plan, decompose, and act. At Dimension Labs, we’ve embedded specialized agents throughout the analytics workflow to automate what used to be manual, disjointed steps. Let’s walk through how these come together. ### The AI Orchestrator: A New Interface for Unstructured Data In the AI Orchestrator , our conversational analytics interface, users can ask open-ended questions like: “Summarize the most important insights and correlations found in my dataset, highlighting key patterns and anomalies.” Behind the scenes, the system calls a Knowledge Graph Agent (rag_query) to retrieve the relevant subset of the graph. The graph serves as the reasoning substrate, grounding the model’s response in structured context rather than raw text. The output isn’t just a summary, it’s an explanatory insight map , backed by relationships the system can show, trace, and quantify. This is the future of querying: ask a question in natural language, and the system dynamically constructs the reasoning path across unstructured data. ### Charting Agents: From Query to Visualization When users ask, “Give me a chart of all the major topics discussed,” the Charting Agent activates. It automatically gathers the structured results from the Knowledge Graph and generates a visualization, no manual SQL, no data prep. This turns data exploration into conversation. Instead of predefined dashboards, users engage in iterative discovery, each chart, insight, and query refining the next. It’s analytics at the speed of thought. ### Prompt Agents: Auto-Build Analytical Dimensions Most of the world’s unstructured data analysis starts with schema design, defining “dimensions” like category, emotion, or churn risk. Historically, this has required data scientists or prompt engineers to manually encode logic. Our Custom Prompt Agent changes that. Users simply describe their business objective , and the agent auto-generates the necessary dimensions, complete with categories, definitions, and structured output fields. This transforms unstructured analytics into a no-code semantic modeling layer . What used to take hours of iteration now happens in seconds. ### The Compound Effect: Agents + Graphs = Autonomous Insight When combined, Knowledge Graphs and Agentic Workflows form a self-improving system : - The Knowledge Graph contextualizes new data automatically. - Agents retrieve, analyze, and act upon that context. - The results of those actions (charts, insights, enrichments) feed back into the graph. Over time, the system learns not just what to analyze, but how to reason. This is how the next generation of data teams will work, not by writing SQL queries, but by orchestrating intelligent systems that continuously extract signal from chaos. ### What This Unlocks The implications extend far beyond dashboards. - Autonomous Discovery of Emerging Themes Agents can continuously scan live data for emerging correlations, surfacing “unknown unknowns” that traditional dashboards miss. - Cross-Domain Reasoning Knowledge Graphs unify disparate sources, call transcripts, chat logs, survey data, into a single reasoning network. This enables questions like: “Which customer frustrations mentioned in chat correlate with support ticket escalations?” - Predictive Action Loops Agents can automatically flag churn risks or opportunities based on graph patterns, triggering alerts or suggested follow-up actions. - Continuous Feedback Learning As analysts interact with insights, their corrections and feedback become part of the system’s knowledge base, making it smarter over time. - Enterprise Memory Every discovery, every pattern, correlation, or anomaly, becomes a node in the organizational memory graph, ensuring knowledge compounds rather than resets. ### The Future of Work in Data Intelligence This new paradigm doesn’t replace analysts or engineers, it amplifies them. In the near future, data teams will evolve from query writers to system orchestrators . They’ll define objectives and guardrails, while agentic systems autonomously explore, visualize, and validate hypotheses in real time. We’re already seeing the rise of a new role, the Language Data Engineer , responsible for designing and maintaining the pipelines that turn raw conversation data into structured, actionable insights. Their job isn’t to manage infrastructure, it’s to architect intelligence. ### Dimension Labs and the Road Ahead At Dimension Labs, we’re defining this next layer of the modern data stack, one purpose-built for language. Our mission is to make the world’s unstructured data intelligible. To give enterprises the tools not just to measure customer behavior, but to understand it . To move from dashboards to dialogue, from queries to reasoning. We believe the combination of Knowledge Graphs and Agentic Workflows will transform how businesses discover truth, make decisions, and act on intelligence. In the same way data warehouses unlocked the quantitative revolution, this new Language Intelligence Layer will unlock the qualitative one. It’s not about more data, it’s about deeper understanding. And that’s the world we’re building. ## More from blog Dec 2, 2025 The Meaning Layer for Unstructured Data: Why the Next Decade Belongs Upstream Over the last decade, the modern data stack has centered itself around the warehouse. The warehouse became the gravitational center for analytics, BI, and increasingly ML. But something interesting is happening, and the recent dbt + Fivetra Jun 23, 2025 What Is Customer Observability? A New Way to Understand Every Customer Interaction In the early 2010s, Uber entered New York City and transformed the transportation landscape. For decades, the city had limited taxi supply through a tightly controlled medallion system, capping the number of yellow cabs to under 14,000. Thi Sep 18, 2025 The Rise of the Language Data Engineer Over the last decade, the modern data stack has gone from niche idea to mainstream. Tools like Snowflake, dbt, and Looker redefined how companies ingest, transform, and activate data. Entire roles were born out of this movement, most famous Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## The Analytics Stack in 2026: Layers, Tools and Trends URL: https://www.dimensionlabs.io/blog/analytics-stack Blog # The Analytics Stack in 2026: Layers, Tools, Trends, and What’s Still Missing A VP of Analytics at a SaaS company just received the monthly operational report. Net revenue retention dropped from 112% to 104% in Q3, an eight-point swing that rewrites the board deck. Eric Griffing , Growth Marketing Apr 14, 2026 10 min read A VP of Analytics at a SaaS company just received the monthly operational report. Net revenue retention dropped from 112% to 104% in Q3, an eight-point swing that rewrites the board deck. Her analytics stack is in good shape. Fivetran feeds Snowflake cleanly. dbt models run on schedule. Looker dashboards are polished and trusted. Every metric is accurate, traceable, and on time. Then the CEO asks why retention fell. She opens three dashboards, two slide decks, and a Slack thread with CS leadership. Ninety minutes later, she still doesn't have a clean answer, just a handful of plausible hypotheses, a list of accounts, and meetings scheduled with the account management and sales leaders. What's missing from a stack that tracks every metric but can't explain any of them? Key takeaway: The modern analytics stack has expanded dramatically since 2021, new layers for orchestration, observability, and activation, but every layer is still architected around structured data. The "why" behind metric movements lives in unstructured customer conversations no layer currently reads, which is the gap most 2026 stacks still need to close. ## What is an analytics stack? An analytics stack is the set of integrated tools that an organization uses to collect, store, transform, analyze, and act on data. It's the end-to-end pipeline between raw data generated by business systems, CRM, product, billing, support, web, and the reports, dashboards, and decisions that pipeline is meant to produce. The term borrows from software engineering, where "the stack" describes the layered set of technologies powering an application. Analytics stacks follow the same logic: each layer depends on the one beneath it, and each layer specializes in a specific job. A well-designed stack lets teams swap components without rebuilding the whole thing. A poorly designed one turns every new question into an engineering project. The modern stack, as the category is usually defined, emerged in the mid-2010s alongside cloud data warehouses like Snowflake, BigQuery, and Redshift. The warehouse became the gravity well of the stack, and everything else, ingestion, transformation, analysis, reorganized around it. ## The core layers of the modern analytics stack in 2026 Most production analytics stacks in 2026 look something like the table below. The layer names vary across vendors and teams, but the shape is consistent. Data enters through ingestion, lands in storage, gets transformed into analytical models, is orchestrated and monitored across pipelines, is presented to end users, and is often activated back into operational systems. Layer Purpose Representative tools Common failure modes Ingestion Move raw data from source systems into storage Fivetran, Airbyte, Segment, Snowplow, Kafka / Confluent Broken connectors, schema drift, incomplete event capture Storage House raw and modeled data for analytical workloads Snowflake, BigQuery, Databricks, Redshift; lakes on S3; vector DBs like Pinecone Ballooning warehouse costs, governance gaps across lakes Transformation Turn raw tables into clean, modeled analytical assets dbt, dbt mesh, Coalesce; Flink / Flink SQL for streams Untested models, slow pipelines, duplicated logic across teams Orchestration Schedule, sequence, and manage pipelines across the stack Airflow, Prefect, Dagster, Astronomer Silent failures, missing lineage, retry storms Presentation Deliver insight to human users through dashboards and apps Tableau, Looker, Power BI, Sigma, Superset, Streamlit Dashboard sprawl, low adoption, stale metrics Activation Push modeled data back into operational tools (reverse ETL) Hightouch, Census Data drift between warehouse and source systems A seventh, increasingly non-optional component sits alongside the others: observability , tools like DataHub, Monte Carlo, and Great Expectations that track data quality, lineage, and contracts across the pipeline. In mature stacks, observability isn't a layer so much as a horizontal fabric that runs through all the others. That's the 2026 baseline. What's worth noticing is how much of it didn't exist, or wasn't taken seriously, even five years ago. ## What's changed in the analytics stack since 2021 The modern analytics stack has gotten meaningfully more sophisticated in a short period. Categories that were footnotes in 2021 became foundational by 2024, and the trajectory has only accelerated through 2026. Seven shifts stand out. - Ingestion fractured into three distinct modes. What used to be "ETL" is now batch (Fivetran, Airbyte), streaming (Kafka, Confluent), and eventing (CDC via Debezium; click streams via Segment or Snowplow). Most stacks now run all three in parallel, which is why orchestration matters so much more than it used to. - The data warehouse became a data store. Lakes, lakehouses, and specialized stores now sit alongside the classic warehouse. Distributed SQL engines like Trino and Starburst let teams query across them. Vector databases like Pinecone and Weaviate emerged to support AI and LLM workloads that the traditional warehouse wasn't built for. - Orchestration graduated to a first-class layer. In 2021, orchestration was an implementation detail, a cron job, a Lambda, or a scripted Airflow DAG. By 2026, it's the spine of the stack. Prefect and Dagster have pushed the category forward, and managed Airflow is everywhere. Without orchestration, the modularity that defines the modern stack simply doesn't work. - Reverse ETL closed the loop. Hightouch and Census created the "activation" layer, letting modeled warehouse data flow back into Salesforce, HubSpot, Marketo, and dozens of other operational systems. The stack stopped being a one-way pipe into dashboards and became a two-way system feeding the business. - Observability became non-negotiable. DataHub, Monte Carlo, and Great Expectations turned data quality, lineage, and contracts into a discipline rather than an afterthought. As stacks got more modular and pipelines more complex, observability became the only way to keep trust in the numbers. - Streaming moved from nice-to-have to baseline. Flink and Flink SQL matured. Managed streaming services from Confluent, Decodable, and others lowered the barrier. For use cases where latency matters, fraud, personalization, operational alerting, batch-only is no longer competitive. - AI workloads reshaped storage and compute. Vector stores, embedding pipelines, and RAG architectures entered the stack as their own concern. Most analytics teams now maintain some version of an AI data pipeline adjacent to their main warehouse. The result is a dramatically more capable stack than the one most companies were running in 2021. And yet. ## The one thing every modern analytics stack still can't do For all of that expansion, new layers, new categories, new vendors, the modern analytics stack has a consistent blind spot. Every layer in it is built for structured data. Ingestion tools pull rows and columns. Warehouses store rows and columns. dbt transforms rows and columns. Dashboards visualize rows and columns. Even the newer additions follow the same pattern: reverse ETL pushes rows and columns back to operational systems; observability tracks the health of rows and columns; vector databases store numerical embeddings for retrieval, not causal business meaning. Gartner has estimated for years that 80–90% of enterprise data is unstructured, the calls, tickets, chat transcripts, survey verbatims, emails, and meeting notes where customers actually explain what they want, what frustrates them, and why they're leaving. Deloitte's research found that only 18% of organizations meaningfully leverage unstructured data in their analytics, and that those who do are 24% more likely to exceed their business goals. The stack, in other words, is optimized for the 10–20% of data that's easy to tabulate and blind to the majority of it. The consequence shows up every Monday in every business review. A dashboard shows NPS dropped eight points. The reasons are sitting in 400 open-ended survey responses no layer in the stack can read. Churn ticked up in the mid-market segment. The explanation is sitting in the last 60 days of CSM call transcripts. Support cost per ticket jumped. The driver is visible in the free-text complaint patterns no BI tool surfaces. The modern stack can tell you what happened with remarkable precision. It almost never tells you why . ## The missing layer: causal intelligence on unstructured data Closing that gap requires a layer most stacks don't have yet, one that reads unstructured customer data, extracts meaning from it, and joins that meaning back to the structured metrics the rest of the stack already tracks. Call it a causal intelligence layer, a meaning layer, or a semantic layer for unstructured data. The name matters less than what it does. Architecturally, it sits between storage and presentation. It pulls from the same warehouse and lakes the rest of the stack uses, plus direct feeds from conversational systems, contact center platforms, survey tools, chat and messaging, CRM notes. It outputs structured data: labeled themes, attributed causes, sentiment trends, and cohort-level patterns that can be joined against any structured metric in the warehouse. A causal layer that actually produces business value has five requirements: - Coverage of every meaningful conversational surface. Calls, tickets, surveys, chats, emails, in-product messages. If a channel is missing, the explanations it contains are missing too. - Structured outputs that join to warehouse metrics. Themes and causes need to become rows and columns that can be joined to revenue, retention, usage, and support data. Free-text summaries aren't enough; the layer has to produce analytical primitives. - Cohort and segment awareness. An explanation is most valuable when it rolls up, "mid-market accounts in manufacturing are citing the same pricing objection" is an insight; "one customer complained" is a ticket. - Freshness that matches the rest of the stack. If dashboards update daily and the causal layer updates quarterly, the two will never answer the same question at the same time. The "why" has to arrive as fast as the "what." - Governance and auditability. Causes and themes need to be traceable back to the underlying customer conversations, with the same lineage and trust guarantees the rest of the stack has moved toward. The first two are where most DIY attempts fail. Teams run sentiment analysis or topic modeling on a batch of tickets, produce a slide, and declare victory, without ever joining the output to the structured metrics that would make it actionable. ## Auditing your current analytics stack The question worth asking about any stack isn't "do we have all the modern layers?", it's "can the stack answer the follow-up question after a dashboard shows a metric moved?" Capability Structured-only stack Stack with a causal layer Track a metric change over time Yes Yes Break the change down by segment, cohort, or region Yes Yes Identify which product usage patterns correlate with the change Partially Yes Surface the reasons customers gave on calls and tickets in that window No Yes Group those reasons into themes across thousands of conversations No Yes Attribute the metric movement to a specific cause No Yes Join the cause back to the structured metric in a dashboard No Yes If most of your answers stop after the third row, the stack has a reporting problem dressed up as an analytics problem, it can show movement but not explain it. This is the gap Travelers closed when they built a unified meaning layer across millions of customer interactions, turning contact center conversations into structured intelligence that sits alongside their operational data. The full story is here . ## What the "why" layer looks like in practice The practical difference is what shows up in the Monday business review. Instead of a dashboard that shows NRR dropped eight points and a 90-minute hunt for the reason, a causal layer produces something closer to this: Net revenue retention declined 8.1 points in Q3, from 112% to 104%. Primary driver: dissatisfaction with the Q3 pricing tier restructure, surfaced in 47% of churn-related conversations over the last 60 days. Concentrated in the mid-market segment (62% of affected ARR). Supporting evidence: 3.2x increase in pricing-related support tickets from this cohort since August; NPS verbatims citing "confusing tier structure" up 4x. Recommended action: executive sponsor outreach on the 18 largest affected accounts; flag to product and RevOps for structural review of the tier boundaries. That's the output of a stack that reads the 80–90% of data the modern stack currently ignores. It's also, not coincidentally, the kind of output that turns an analytics team from a reporting function into a decision-making one. So what's the real gap in most analytics stacks in 2026? It isn't another ingestion tool, another warehouse optimization, or another BI refresh. Those layers are mature. The gap is between the data the stack already tracks and the data that explains it, and closing that gap is what separates stacks that report from stacks that decide. If you're evaluating what a causal intelligence layer would look like on top of your existing stack, start with one file to see how Dimension Labs joins unstructured customer data to the structured metrics you already track. ## Frequently asked questions about the analytics stack What's the difference between a data stack and an analytics stack? The terms overlap heavily and are often used interchangeably. "Data stack" tends to emphasize the underlying infrastructure, ingestion, storage, transformation, orchestration, while "analytics stack" tends to emphasize the end-to-end system including the presentation and activation layers where humans actually use the data. In practice, most modern stacks are both. What's the difference between the modern analytics stack and the traditional one? Traditional stacks were typically on-premises, built around ETL processes, and centered on a single enterprise data warehouse managed by a small central team. Modern stacks are cloud-native, favor ELT over ETL, and are assembled from best-of-breed SaaS tools that each specialize in one layer. The modern approach prioritizes modularity, self-service, and scalability, at the cost of more integration work across vendors. Where does AI fit in the analytics stack? AI shows up in three places. Specialized AI data stores (vector databases like Pinecone and Weaviate) sit alongside the warehouse to support retrieval-augmented generation and LLM applications. AI-powered features are embedded across existing layers, natural language querying in BI tools, anomaly detection in observability, code generation in transformation. And increasingly, AI powers the causal intelligence layer itself by parsing unstructured conversational data into structured themes and causes. Do I need to rebuild my analytics stack to add a causal intelligence layer? No. A causal layer is additive, not replacive. It reads from the same warehouse and storage systems the rest of the stack already uses, adds direct feeds from conversational sources the stack currently ignores, and outputs structured data that joins back into existing BI and activation workflows. Teams don't swap out Snowflake or dbt; they add a layer that finally makes the 80–90% of unstructured data analytically useful. ## More from blog Apr 6, 2026 Unstructured Data Tools: A Practical Guide to Turning Messy Inputs Into Real Answers The modern data stack was built around a comfortable assumption: that the data worth analyzing fits in rows and columns. Ingest it from a source system, model it in the warehouse, expose it through a BI tool, and the dashboards take care of Aug 28, 2025 The Missing Layer in the Modern Data Stack: Unstructured Data By 2025, more than 80% of enterprise data will be unstructured. Yet if you look at the modern data stack it’s almost entirely designed for structured data. Apr 14, 2026 Predictive Churn: A Complete Guide to Models, Data, and the Missing "Why" Layer A Head of Customer Success at a mid-market SaaS company opens her Monday churn report. Forty-seven accounts sit above the 70% risk threshold, representing $4.2M in ARR. She has eight CSMs covering nearly 600 accounts. She forwards the list Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Conversation Analytics: What It Is and Why It Matters URL: https://www.dimensionlabs.io/blog/conversation-analytics Blog # Conversation Analytics: What It Is and Why It Matters Conversation analytics refers to the use of artificial intelligence (AI), specifically natural language processing (NLP), to analyze customer conversations and use them to understand their sentiment, intent, and interest… Eric Griffing , Conversational AI Advocate Nov 14, 2024 6 min read ## What is conversation analytics? Conversation analytics refers to the use of artificial intelligence (AI), specifically natural language processing (NLP), to analyze customer conversations and use them to understand their sentiment, intent, and interests. You can use it to identify opportunities for improvement so that you can enhance your overall customer experience. We can apply the technology to many industries and situations, but it's especially effective when paired with other forms of data collection like surveys or phone calls. In turn, it can help you measure the effectiveness of your marketing campaigns, social media strategies, and customer service. A recent report by Grandview research found that the global conversation AI market will reach $41.39 billion by 2030, because of rising demand, reduced chatbot development costs, AI-powered customer support services, and omnichannel deployment. This finding only indicates how valuable it is when you need insights into how customers feel about the product or service they're receiving and how they feel about their experience interacting with your brand online, i.e., customer conversation analytics. In this article, we'll explain how conversational analytics works and how you can leverage it for your business, and improve your overall customer experience. ## How does conversational analytics work? Typically, conversation analytics tools can identify trends and opportunities based on the information you provide in the form of chat transcripts, voice or video call transcripts, and other forms of customer data. It analyzes this data and provides reports that help improve your customer experience by identifying problems and potential customer behaviors you may not be aware of. For example, the customer may have chatted with your chatbot because their insurance plan has changed, and they're not sure what that entails. Over time, companies will develop a large dataset containing all these stories from different customers about the same intent or behavior. You can use this in two ways, one to predict future customers' questions and the other, what concerns customers have with specific insurance plans. Artificial intelligence helps the natural language processing model learn from past experiences to predict outcomes accurately based on new data input. In addition, the model eventually identifies positive or negative feelings from the written text by analyzing the tone and sentiment of the language. We can also train the model to extract specific keywords and match them to one particular intent. It slowly makes associations between these words, enabling it to decode the sentiment and context behind these conversations, and predict outcomes more accurately. It can make these predictions by breaking down the conversation into fragments called vectors. The more customer data you feed it, the more it learns, and the better the output will be. By continually improving the model, it will unlock better customer experiences and higher satisfaction. ## Benefits of conversation analytics - Understand the nuances of customer conversations Conversation analytics is a new way of understanding the nuances of customer conversations. It combines the power of multiple features like sentiment analysis, key chatbot metrics, and more to offer a holistic overview. It's about looking at the entire context of an interaction, including the topics discussed, tone, language used, and emotion expressed. For example, when analyzing conversations from two customer calls, we can see that one person is having trouble with something they ordered and went back and forth while the other customer just returned the product. By analyzing the conversation, it shows us that this particular product may be defective or have some other issue that needs to be addressed immediately. - Predict customer behavior The idea is simple: if you know what customers say about you, you can use that data to improve your business. Also, it's not just about what customers say but how they say it. The goal of conversational analytics is to gain insights into what consumers want and need, before they even know it themselves, so you can offer them products or services that meet their needs. It helps you improve how your company responds to customer service requests and support tickets, transforming the customers' experience. - Make data-driven business decisions Conversation analytics tools can help companies make better decisions about allocating resources, optimizing processes, and adapting their business models over time. You can: - Identify your most influential customers over a specific period - Monitor customer satisfaction and loyalty over time - Track trends in social media conversations about your brand or product - Find out what types of content resonate best with your audience - Use social media monitoring tools to discover new leads Conversational analytics lets you see which customers are likely to churn so that you can take action before they leave your business or service. You can make data-driven decisions and implement strategies to alleviate critical issues by measuring these metrics. - Reduce your internal workload Your employees are already pressed for time, so burdening them with the task of manually going through hundreds of chatbot conversations is often a waste of resources. With conversational analytics, you can identify common questions or requests outside of standard procedure and create automated responses that your employees can use instead of repeatedly answering the same question. This process allows your employees to focus on high-value tasks that drive growth or productivity. For example, many teams waste time sorting through multiple data sources across different channels and blending them to create a view of their customer's journey. Instead, by using the right conversational tools, your team can automate the undifferentiated heavy-lifting of data work and spend their time focusing on identifying new use cases and improvements to their conversational channels/chatbots. ## Use cases of conversational analytics ### Understanding intent Customer questions can be complex, making it harder to understand the intent, especially when you offer multiple products. For example, insurance companies usually provide various products (home, car, or boat insurance), and conversation analytics can help understand these complex intents (questions). When you process this information via a conversational data cloud, it becomes a lot easier to understand the intent behind popular customer queries. It also helps mitigate the need for live agents by identifying routine questions customers ask, such as policy details, policy numbers, policy expiration dates, policy prices, account questions, etc. Using this data, you can optimize your chatbot's conversation flow by improving critical metrics like fallback rate, human escalation rate, and increasing customer engagement. ### Integrating with traditional analytics tools Traditional analytics tools are not meant to ingest and analyze conversational data (which is unstructured by nature), making it hard to categorize and comprehend. They don't have enough context, which prevents you from understanding the bottlenecks that led to specific issues in the first place. To resolve this, you must measure the bots' conversation/user journeys across various paths. For example, using tools like Power BI to generate data visualizations that go beyond the numbers and understand the user intent is difficult. You can only do this with technologies like sentiment analysis, as you need more sophisticated tools to uncover such insights. Tools likecan measure the health of your chatbot based on specific metrics and create customizable dashboards that provide an in-depth consolidated overview of the ground reality. ## How Intuit reduced its human escalation rate by 57% In 2017, when Intuit QuickBooks introduced its conversational chat experience, it wanted to reduce its human escalation rate. But at the time, they were dealing with a massive amount of unstructured data that would take weeks to analyze. As their customers were unsatisfied with mishandled or unhandled responses, this had to be done as quickly as possible. To circumvent this issue, they turned to Dimension Labs, which gave them an in-depth insight into every customer conversation they had. Dimension Labs could generate full transcripts for each conversation and map the conversational paths. Based on that information, they optimized their chatbot’s conversation flow, reducing their fallback rate by 35% and human escalation rate by 57%. “Our ultimate goal was speed-to-benefit. When someone wants to know how to do something or needs information, it should be faster than even the fastest human help.”, Scott Ganz, Principal Content Designer ## Analyze your customer conversations using Dimension Labs Conversation analytics is a powerful tool for companies who want to improve the customer experience. It can help you identify areas of improvement to enhance your business, so it’s worth taking some time to learn more about it. Through your chatbot's conversational transcripts, you can access an invaluable stream of information for you to use. When you use your customers' words to understand them better and quickly resolve their issues, you can master your chatbot's performance and personalize it to match your customers' needs. If you’re looking for conversational analytics software to help you uncover such insights, start with one file today, from $299 a month. ## More from blog Nov 16, 2024 What Are Enterprise Chatbots And How Do They Work? Gartner defines chatbots as “A domain-specific conversational interface that uses an app, messaging platform, social network or chat solution for its conversations.” Nov 17, 2024 What is Textual Data and Why Does It Matter to Customer Support Teams? Manually analyzing hundreds of unstructured text-based data sources is tedious and time-consuming. What’s worse is that since it’s in text format, you know this process can be made easier, but you’re unsure how to do it. Nov 18, 2024 Chatbot Analytics: 13 Metrics That Every Business Should Track How often have you built a chatbot and spent days thinking through every possible workflow only to deploy it and see that users never use them? Our guess: too often. The bigger issue is that your chatbot analytics tool may have marked this Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## The Meaning Layer for Unstructured Data | Dimension Labs URL: https://www.dimensionlabs.io/blog/the-meaning-layer-for-unstructured-data Blog # The Meaning Layer for Unstructured Data: Why the Next Decade Belongs Upstream Over the last decade, the modern data stack has centered itself around the warehouse. The warehouse became the gravitational center for analytics, BI, and increasingly ML. But something interesting is happening, and the … Andrew Hong , CEO @ Dimension Labs Dec 2, 2025 7 min read Over the last decade, the modern data stack has centered itself around the warehouse. The warehouse became the gravitational center for analytics, BI, and increasingly ML. But something interesting is happening, and the recent dbt + Fivetran merger put a spotlight on it: Power is shifting upstream. Upstream is where data movement and meaning are defined. Upstream is where the semantics, the business logic, and the true shape of data are created. Upstream is where context, once scattered or implicit, gets encoded into systems. And upstream is where unstructured data, the largest and fastest-growing class of enterprise data, still has no meaning layer at all. The warehouse is no longer where the value is created. It’s where value is stored . For structured data, the semantic layer tried to fill the gap. But for unstructured data, customer conversations, sales calls, support chats, surveys, notes, emails, transcripts, voice interactions, there is no equivalent layer that defines meaning, ensures consistency, or generates reusable logic. As unstructured data becomes core to operations, that gap becomes a competitive bottleneck. This article lays out why: - the semantic layer is struggling - upstream transformation is finally becoming the locus of value - unstructured data is the biggest blind spot in the stack - and why the future requires a Meaning Layer for Unstructured Data ### 1. The Semantic Layer Isn't Dead, But It’s Far From What We Need A recent viral LinkedIn post from Kenny Ning highlighted something the industry has whispered for years: Semantic layers haven’t delivered on their promise. He’s not wrong, but also not for the reasons most think. The problem is not that semantic layers don’t create value. They do. The problem is where they live and what they assume . Semantic layers were designed for structured data , where: - schemas are known - tables are stable - metrics can be cleanly defined - joins, relationships, and dimensions are explicitly modeled In other words: semantic layers work when meaning already exists. They simply “codify” it. But in today’s world, meaning is increasingly generated , inferred , contextual , and dynamic . It is shaped by language, intent, and behavior, none of which live in your warehouse tables. The semantic layer assumed the lowest common denominator of structure. It never considered the complexity and richness of unstructured data. So semantic layers are not failing, they are bound to a shrinking fraction of enterprise data . This mismatch is why we see: - metric layers constantly breaking - inconsistent logic between tools - BI dashboards drifting from operational reality - teams reinventing the same definitions - “semantics” being brittle and fragile Semantic layers weren’t the wrong idea. They were simply built for a world that no longer exists. ### 2. The Fivetran + dbt Merger Signals a Power Shift: Meaning Moves Upstream Constance Martineau’s (Product Leader at Astronomer) analysis of the dbt/Fivetran merger hit on a profound point: Power is shifting upstream into ingestion and transformation. Historically, data moved like this: In the past the warehouse defined the shape of every data stack. Meaning was applied after the fact. It was downstream, brittle, and disconnected. But the modern reality is different: 🟥 Value is created upstream, not downstream. Why? Because upstream is where the critical questions are answered: - What does this event actually mean? - How should this conversation be interpreted? - What logic, transformations, or classifications define the business? - How should ambiguous, narrative, or qualitative signals be structured? Once logic moves upstream… - transformations become reusable - models become consistent - lineage becomes reliable - and analytics becomes far easier downstream The warehouse is becoming a passive substrate. The action is happening before data ever lands there. But this shift exposes a glaring hole in modern data architecture: ✔️ We have transformation engines for structured data ❌ but we have no transformation engine for unstructured data. We have dbt, SQL, and metric layers for tables. But for language, conversations, free-form text? We have nothing equivalent. ### 3. The Largest Data Source in the Enterprise Has Zero Semantics Every company today is drowning in unstructured data: - support interactions - customer chats - sales conversations - call transcripts - surveys and open-ends - user reviews - email threads - field notes - chatbot logs - agent messages This data contains the richest signal about customer expectations, product weaknesses, churn risks, unmet needs, friction points, and emerging opportunities. Yet it lacks everything structured data takes for granted: - no schema - no modeling conventions - no standard transformations - no metric layer - no semantic governance - no lineage - no shared definitions - no versioning - no upstream logic Every company reinvents the wheel: new prompts, new tags, new dashboards, new classifications, each one a one-off. It’s an ecosystem without Meaning Infrastructure . Without standards. Without shared logic. Without the equivalent of dbt, LookML, or any modern transformation layer. In other words, unstructured data is the most valuable data, and the least treated as data. This is precisely the gap the industry must address next. ### 4. What the Industry Actually Needs: A Meaning Layer for Unstructured Data If structured data had its revolution, metric layers, transformation tooling, SQL standards, unstructured data is now overdue for its own. The solution is a new class of system: - The Meaning Layer for Unstructured Data supported by.. - Meaning Infrastructure and operationalized through.. - Meaning Orchestration. This isn’t text analytics. This isn’t classification. This isn’t sentiment scoring or topic extraction. This is infrastructure that acts as the semantic foundation for all unstructured data. A Meaning Layer is not an add-on to the stack. It becomes the new center of gravity once you recognize that unstructured data cannot participate in BI, governance, or AI without shared semantics. At that point the challenge is no longer “extracting insights.” It becomes defining the common interpretation of language so every downstream system is operating on the same reality. That requires more than NLP. It requires an infrastructure layer that turns text into something durable, governed, and computable. It has to formalize how meaning is defined, how context is encoded, and how those decisions propagate through the rest of the stack. The checklist below outlines the non-negotiable capabilities of a real Meaning Layer and what it must deliver before unstructured data can behave like a first-class analytical asset: This layer becomes the core semantic engine for the enterprise’s language data. Without it, unstructured data will remain “unusable dark matter.” With it, unstructured data becomes “operational intelligence.” A Meaning Layer is to unstructured data what: - dbt is to transformations - Fivetran is to ingestion - the semantic layer is to structured metrics - reverse ETL was to activation It’s the missing foundational layer. ### 5. Why This Layer Must Live Upstream Just as the dbt/Fivetran merger highlights, meaning must be defined before data reaches the warehouse, not after. Why? Because meaning is part of the data lineage. Therefore, meaning has to be defined before the warehouse. Once unstructured data enters the stack, every downstream tool inherits whatever semantics you set upstream. If you skip that step, models disagree, dashboards drift, and analysis becomes guesswork. Define meaning upstream and the opposite happens. Structure is created once, logic stays consistent, and every downstream query becomes trivial. AI agents finally have something solid to stand on. If meaning is defined downstream, every tool interprets differently. If meaning is defined upstream, every tool benefits from semantic stability. This is why Meaning Infrastructure must sit at the ingestion + transformation boundary, the same place where the modern data stack just shifted its center of gravity. Upstream is where dbt won. Upstream is where Fivetran moved. And upstream is exactly where the Meaning Layer for Unstructured Data must live. ### 6. Meaning Infrastructure Is the Foundation for Agentic Workflows AI agents, orchestrators, and knowledge-graph-driven systems are exploding across the enterprise. But there’s a catch: Agents cannot operate on raw text. Knowledge graphs cannot ground themselves in unstructured chaos. Automated workflows break when semantics drift. Agents need structure . But more importantly, they need meaning. In our last article on How Knowledge Graphs and Agentic Workflows Are Reimagining Unstructured Data , we discussed how knowledge graphs and agentic workflows that sit on top of unstructured data transformed through a Meaning Layer could open up the possibilities for endless new automations and capabilities for different teams. But without a way to establish a Meaning Layer, organizations are left in the dark in terms of how to fully operationalize their unstructured customer data. Meaning Infrastructure provides exactly that: - consistent semantic grounding - interpretation logic - event/context understanding - canonical dimensions - standardized signals - reliable reason extraction It becomes the substrate upon which: - autonomous agents act - workflows self-correct - knowledge graphs stay fresh - customer intelligence becomes operational - predictive models become reliable - analytics becomes frictionless This is why the Meaning Layer is not an analytics feature. It is a foundation for the next era of intelligent enterprise systems. ### 7. The Future: A Unified Meaning Pipeline for Language Data The next era of language data starts by moving meaning to the front of the pipeline. The diagram illustrates this new path: raw text is interpreted upstream, transformed into stable dimensions, and only then handed off to the systems that depend on consistent semantics. It’s a small change in order, but it reshapes the entire stack. Once meaning is upstream, you stop doing “analysis,” and start doing: automated reasoning. Automated reasoning means systems can synthesize signals across conversations, channels, and contexts without human stitching, producing conclusions that reflect the true state of the customer, not just isolated data points. It’s the moment when unstructured data stops being something you analyze and becomes something your organization can act on automatically. And once that shift happens, the nature of the modern data stack (and where value is created) changes entirely. ### 8. The Takeaway: Meaning Is the Next Strategic Layer of the Enterprise The Next Frontier of the Modern Data Stack Is Meaning We are entering a world where: - metrics are commoditized - BI is commoditized - LLMs are commoditized - warehouses are commoditized - ingestion is commoditized But meaning is not. Meaning is the rarest and most valuable layer. Meaning is where interpretation lives. Meaning is where organizations differentiate. Meaning is what powers every AI agent, every analytics workflow, every customer insight. The modern data stack created massive leverage for structured data. Now the opportunity is 100x larger, because unstructured data is 80%+ of what companies actually collect. The companies who build a Meaning Layer, and the companies who adopt one, will own the next decade of data advantage. Because in the end: Structure lets you query data. Meaning lets you use it. ## More from blog Aug 28, 2025 The Missing Layer in the Modern Data Stack: Unstructured Data By 2025, more than 80% of enterprise data will be unstructured. Yet if you look at the modern data stack it’s almost entirely designed for structured data. Nov 17, 2025 From Chaos to Context: How Knowledge Graphs and Agentic Workflows Are Reimagining Unstructured Data For years, the analytics world has revolved around structure. Rows, columns, schemas, and dashboards have been our tools for understanding reality. Yet beneath this structured world lies a vast ocean of unstructured data, customer conversat Apr 14, 2026 The Analytics Stack in 2026: Layers, Tools, Trends, and What’s Still Missing A VP of Analytics at a SaaS company just received the monthly operational report. Net revenue retention dropped from 112% to 104% in Q3, an eight-point swing that rewrites the board deck. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Top 10 Text Analytics Tools for Unstructured Data URL: https://www.dimensionlabs.io/blog/top-10-text-analytics-tools-to-unlock-insights-from-unstructured-data Blog # Top 10 Text Analytics Tools to Unlock Insights from Unstructured Data In the age of information overload, organizations are inundated with vast amounts of unstructured data, from customer feedback and social media comments to support tickets and chat logs. The challenge lies not in gatheri… Rumeer Keshani , Growth & Strategy Jan 31, 2025 5 min read ## How to Choose the Right Text Analytics Tool In the age of information overload, organizations are inundated with vast amounts of unstructured data, from customer feedback and social media comments to support tickets and chat logs. The challenge lies not in gathering this data, but in converting it into actionable insights. A significant statistic underscores this: 70% of businesses report that making sense of unstructured data is critical to their success. This has led to a growing demand for sophisticated text analytics tools. In this article, we will explore the top 10 text analytics tools poised to help organizations unlock insights from their unstructured data. Ranking Criteria The tools are ranked based on their features, usability, integration capabilities, and specialization in unstructured data analytics. Let's delve into these powerful offerings. When selecting a text analytics tool, consider scalability, integration capability with existing data systems, ease of use, and the ability to produce real-time insights. Each organization should align its choice with specific business goals, be it improving customer experience, enhancing operational efficiency, or gaining competitive insights. ## 1. Dimension Labs Brief Overview Dimension Labs stands out as a market leader in analyzing unstructured data with a particular focus on customer interactions. Its platform utilizes AI to transform qualitative conversation data into quantifiable insights. Key Features - Text Dimensionality: A unique approach to uncovering multiple meanings in text. - Predictive Insights: Offers real-time monitoring and anomaly detection. - Omni-Channel Integration: Consolidates data from various customer touchpoints for a unified view. Best Use Cases Ideal for organizations seeking to improve customer support and experience through actionable insights derived from conversation data. Pricing & Accessibility Pricing is tailored to organizational needs, with options available for businesses of different sizes. ## 2. Qualtrics TextIQ Brief Overview Qualtrics TextIQ harnesses the power of AI to provide nuanced analysis of customer feedback across multiple channels, allowing businesses to identify trends and sentiments effectively. Key Features - Sentiment Analysis: Automatically detects sentiment trends in customer feedback. - Natural Language Processing (NLP): Delivers insights based on linguistic nuances. Best Use Cases Recommended for companies looking to optimize customer feedback loops and enhance overall customer satisfaction. Pricing & Accessibility Available under the Qualtrics Experience Management platform, often requiring a subscription. ## 3. Medallia Text Analytics Brief Overview Medallia Text Analytics leverages advanced machine learning to process customer interactions, transforming raw data into strategic insights that drive decision-making. Key Features - Real-Time Insights: Instantaneous analysis of customer feedback. - Integration with Medallia’s Ecosystem: Seamlessly connects with other Medallia solutions. Best Use Cases Best suited for enterprises focused on customer experience management and operational excellence. Pricing & Accessibility Enterprise-level pricing model based on usage and company size. ## 4. Phospho Brief Overview Phospho specializes in extracting insights from diverse textual data, offering unique visualization tools that simplify complex datasets for business users. Key Features - Customizable Dashboards: Easily configurable to fit various analysis needs. - Multi-Data Source Integration: Can pull data from multiple platforms for comprehensive analysis. Best Use Cases Designed for businesses that require in-depth visualizations alongside their textual analyses. Pricing & Accessibility Pricing varies by organization size and needs; demo requests are encouraged for personalized options. ## 5. TextQL Brief Overview TextQL is emerging as a user-friendly tool that empowers business users to query unstructured data using SQL-like syntax, thus democratizing data access. Key Features - Natural Language Queries: Users can ask questions in plain language. - Integrative API: Allows seamless integration with existing data infrastructures. Best Use Cases Ideal for small to medium enterprises that want to make data analytics more accessible without advanced technical skills. Pricing & Accessibility Freemium model available, with advanced features locked behind tiered subscriptions. ## 6. SuccessKPI Brief Overview SuccessKPI specializes in performance measurement and analytics across various business functions, utilizing text analytics to enhance visibility into customer interactions. Key Features - Comprehensive Reporting: Analytics that cover multiple KPIs through a single interface. - Data Enrichment Capabilities: Enhances raw data through contextual analysis. Best Use Cases Best for organizations focused on broad performance measurement across customer experience. Pricing & Accessibility Pricing is competitive and designed to fit enterprise-level requirements. ## 7. Halosight Brief Overview Halosight focuses on improving customer engagement through advanced sentiment analysis and predictive analytics, helping companies proactively respond to customer needs. Key Features - Real-Time Sentiment Analysis: Tracks customer sentiment across various touchpoints. - Behavioral Insights: Predicts customer behavior based on interactions. Best Use Cases Great for companies looking to enhance engagement strategies through data-driven decisions. Pricing & Accessibility Pricing is based on user requirements, available through a subscription model. ## 8. Enterpret Brief Overview Enterpret centers on customer feedback analytics, converting qualitative data into actionable insights that inform product and service improvements. Key Features - Feedback Loop Optimization: Systematically collects and analyzes customer suggestions. - AI-Driven Insights: Leverages machine learning to identify feedback trends. Best Use Cases Ideal for product managers and teams looking to enhance product offerings based on direct customer feedback. Pricing & Accessibility Subscription-based pricing available, tailored to the size and needs of the organization. ## 9. UnitQ Brief Overview UnitQ focuses on the continuous monitoring of customer feedback, providing real-time insights that assist in product development and customer satisfaction improvements. Key Features - Automated Feedback Tracking: Monitors customer inputs across numerous platforms. - Actionable Alerts: Sends alerts based on feedback patterns. Best Use Cases Great for fast-paced product teams needing agile responses to customer feedback. Pricing & Accessibility Offers tiered pricing based on user needs and team sizes. ## 10. InMoment Brief Overview InMoment harnesses customer feedback to drive actionable insights through predictive analytics, helping organizations enhance customer experiences effectively. Key Features - Predictive Analytics Tools: Helps forecast customer needs based on past interactions. - Customer Journey Mapping: Visualizes customer interactions across touchpoints. Best Use Cases Recommended for enterprises focused on comprehensive customer journey analytics. Pricing & Accessibility Enterprise pricing available with custom quotes based on usage. ## Emerging Trends in Text Analytics The industry is increasingly moving towards the use of artificial intelligence to enhance the accuracy of insights drawn from unstructured data. As customer interactions grow, tools that leverage deep learning and natural language processing are becoming vital in predictive analytics. Moreover, organizations are prioritizing data privacy and compliance as key factors in their analytics strategies. ## Final Recommendations & Takeaways Choosing the right text analytics solution can drastically improve how organizations leverage unstructured data. Dimension Labs stands out for its comprehensive approach, predictive insights, and capability to analyze substantial volumes of conversational data, while other tools like Qualtrics TextIQ and Medallia Text Analytics bring valuable features suited for specific enterprise needs. Are you ready to explore how text analytics can elevate your business? Try Dimension Labs today , from $299 a month, and unlock unprecedented insights from your unstructured data. ## Conclusion & FAQ Strategic Recap The right text analytics tool can transform your data into actionable insights, fostering better product decisions and enhanced customer experiences. CTA Discover the potential of your unstructured data with Dimension Labs . Frequently Asked Questions - What is text analytics software? Text analytics software uses algorithms to analyze text data, extracting insights related to sentiment, themes, and trends. - How do enterprises integrate text analytics software? Integration typically involves connecting the software to existing CRM or ERP systems, enabling it to pull data from various sources for comprehensive analysis. - What are the top trends in text analytics? Major trends include increased use of AI for predictive insights, real-time analytics capabilities, and greater focus on data compliance and privacy solutions. ## More from blog Jan 13, 2025 Data Enrichment: Enhance Decisions with Next Gen Insights In today’s data-centric world, enterprises are inundated with vast amounts of data. A staggering 90% of the world’s data was generated in just the last two years, compelling organizations to seek innovative methods to extract meaningful ins Sep 26, 2025 A Guide to Unstructured Data Management In today’s data-driven world, organizations are inundated with vast amounts of information, much of which is unstructured. Unlike traditional structured data neatly organized in databases, unstructured data includes emails, call transcripts Mar 26, 2025 Brand Sentiment Analysis: Leveraging Unstructured Text Analytics and LLMs for Strategic Insights In an age where brands can rise and fall at the speed of a trending tweet, understanding consumer sentiment has never been more crucial. A staggering 71% of consumers believe that brands should listen to their feedback to improve products a Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## LLM Analytics: How to Improve AI Accuracy and Performance URL: https://www.dimensionlabs.io/blog/how-llm-analytics-help-improve-ai Blog # How LLM Analytics Improve Accuracy & Performance As large language models (LLMs) become embedded in apps across customer service, product support, and digital experiences, one question still remains unanswered: how do you know your AI is performing well? Eric Griffing , Head of Growth Marketing Apr 24, 2025 6 min read As large language models (LLMs) become embedded in apps across customer service, product support, and digital experiences, one question still remains unanswered: how do you know your AI is performing well? Most teams track tokens , cost, and latency. Few measure whether the model actually solved the user’s problem without going off-brand. This guide shows you how to instrument both: classic LLM observability and experience-level evals, then close the loop so accuracy, cost, and CSAT improve together. In this blog, we’ll walk through how LLM Analytics improves AI performance by combining traditional observability metrics with deeper experiential analysis. We’ll break down the metrics you should care about and showcase a real-world example on how businesses can close the loop between model behavior and business goals. ## What Are LLM Analytics? LLM Analytics refers to the process of tracking, analyzing, and improving the performance of large language models used in AI applications. These analytics can be applied to AI agents, chatbots , customer support tools, internal copilots, or any use case where LLMs engage users in natural language. Most teams today monitor surface-level metrics like latency, token counts, and conversation volume. While these observability metrics are useful, they don’t reveal whether the LLM is actually providing accurate, helpful, or brand-aligned experiences. LLM Analytics bridges that gap by combining experiential insights with traditional LLM monitoring. It turns opaque conversations into structured data and provides a framework to evaluate what the model said, why it said it, and what impact it had for the user. LLM Analytics overlaps with related disciplines such as LLM Observability, LLM Tracing, and LLM Evaluation (“evals”). It goes beyond raw metrics like latency or token usage by incorporating guardrails, groundedness checks, and RAG evaluation for retrieval-augmented systems. Together these practices give data and ML teams a unified view of model accuracy, cost, and compliance. ## Why AI Teams Need LLM Analytics LLMs are powerful but unpredictable. They can hallucinate, drift from training data, respond off-brand, fall into loops, and fail to resolve issues. Left unchecked, these behaviors lead to: - Poor customer experiences - Increased escalations and agent handoffs - Regulatory and compliance risks - Brand trust erosion | LLM Analytics gives product and AI teams the ability to: - Diagnose problems quickly - Prevent model hallucinations - Quantify helpfulness, customer effort, and sentiment - Identify common conversation patterns and friction points - Set up guardrails and workflows to fix issues By combining observability and experiential analysis, you can move from the AI black box towards proactive LLM optimization. ## Observability Metrics: Basic LLM Monitoring Most AI teams already track basic observability metrics, which serve as the foundation for performance monitoring. These typically include: - Total Conversations : How many sessions are your LLMs handling? - Engagement Rate : What percentage of sessions include meaningful interaction? - Token Usage : How many tokens are being consumed, and by which models? - Average Session Duration : How long are users engaging with the model? These metrics help answer questions like: - Are we scaling costs efficiently? - Which models are cost-effective? - Is usage trending up or down? Teams often combine these observability metrics with error tracing tools like Langfuse , LangSmith , or Helicone to gain deeper visibility into latency, p95/p99 response times, and token cost telemetry. But they don’t answer the most important question: Is the LLM doing a good job? That’s where experiential analytics comes in. ## Experiential Metrics: Measuring LLM Quality At Dimension Labs, we believe the true value of LLM Analytics lies in experiential metrics. These are indicators of conversation quality, customer satisfaction, and LLM brand alignment. We base our analysis in the data coming from your customers interacting with the LLM. Our platform introduces a four-tier framework called the AI Health Score , which combines: - Completion Score – Did the model successfully complete or resolve the user’s request? - Sentiment Score – What emotional tone emerged in the conversation? - Helpfulness Score – Did the user find the model’s responses useful? - Effort Score – How hard did the user have to work to get a good outcome? These scores can be combined into a single health index or analyzed independently. They offer a more comprehensive view of performance, surfacing conversations that may look fine on the surface but fail to meet user expectations. Unlike traditional analytics tools, this approach unifies AI agent analytics, chatbot analytics, and LLM eval scores in one place, covering both structured metrics and qualitative experience data. ## Real Example: LLM Support Agent for a Fortune 500 Telco Provider Let’s look at an AI agent deployed by a large telco company. Observability metrics showed high session volume, stable latency, and moderate escalation rates. But the AI Health Score told a different story: only 40.4 out of 100 , signaling serious experiential issues. Diving into the dashboard, we found: - Frequent hallucinations, with the bot making up company policies - Off-brand responses like “Maybe you should switch providers” - High user effort in resolving billing issues - Inconsistent escalation behavior By visualizing dissatisfaction drivers and mapping conversation flows, the team could now: - Identify where drop-offs occurred in billing conversations - Spot overly creative responses tied to high temperature settings - Add guardrails and fallback responses to prevent brand damage - Update knowledge base content to improve helpfulness In one case, the bot replied to a customer with, “As an overworked representative, there’s nothing more I can do,” revealing a major oversight in fine-tuning and response filtering. With experiential LLM Analytics, these red flags become visible and actionable. ## Using Visual Insights and Semantic Mapping Experiential analytics also includes tools for semantic mapping . In our example, the telco team used a heatmap to visualize which types of issues the LLM handled best vs. where it struggled. Clusters of conversations were grouped by intent, outcome, and emotional tone, revealing latent issues no one had seen before. Another key feature used was AI Cards : dynamic summary cards that automatically extract top insights from conversation clusters and recommend specific fixes. These include: - Adjusting model temperature - Rewriting flawed responses - Adding escalation triggers - Updating training data This new level of detail allows teams to spot problems and understand why they’re happening and how to fix them. ## Who Should Use LLM Analytics? LLM Analytics are essential for any organization deploying AI agents and apps, especially if they’re customer-facing. Teams we’re seeing adopt LLM analytics include: - Product and Engineering Teams managing chatbots, support agents, or internal copilots - CX and Contact Center Leaders focused on support quality, costs, and KPIs - Data and ML Teams overseeing model accuracy, training, and governance Even marketing, quality assurance, and operations teams can benefit by understanding how AI is interacting with customers and representing the brand. If you’re scaling AI across departments or digital channels, LLM Analytics becomes the system of record for model behavior and performance. ## Driving AI Improvement with LLM Analytics The ultimate value of LLM Analytics is not just diagnosis, it’s action . After deploying LLM Analytics, they quickly become the engine for closed-loop feedback and fuel real-time improvements to your organization’s AI and AI-assisted apps. With LLM Analytics you can: - Detect and resolve customer issues before they escalate - Continuously retrain your model based on historical conversation data - Optimize for CSAT, cost, and compliance in parallel - Provide visibility across teams into what the AI is doing and why This is how organizations move from launching an LLM agent to truly operationalizing it. You’re no longer reacting to failures of the model, you’re proactively working on improving the performance. At Dimension Labs, we help companies do exactly that. Our platform unifies observability and experiential metrics to give you full visibility into LLM behavior, performance, and customer impact. Want to see it in action with your own data? Start with one file in Dimension Labs today, from $299 a month. ## Frequently Asked Questions (FAQs) ### 1. What are LLM Analytics? LLM Analytics refers to the process of tracking, measuring, and optimizing the performance of large language models (LLMs) such as ChatGPT or Claude. It helps teams understand how LLMs and AI Agents built on LLMs behave, respond to users, and impact the user experience. ### 2. Why are LLM Analytics important? LLM Analytics helps businesses improve performance of AI applications, reduce hallucinations and inappropriate messages, and increase customer experience and satisfaction. It provides visibility into model behavior and conversation quality at scale. ### 3. What metrics are used in LLM Analytics? Common LLM Analytics metrics include token usage, response time, user sentiment, intent accuracy, customer effort, and CSAT (customer satisfaction). These help teams assess the LLM’s effectiveness and optimize it. ### 4. Who should use LLM Analytics? Product managers, data scientists, and engineering teams use LLM Analytics to monitor and improve chatbots, AI agent, and assistant performance. It’s especially useful for companies deploying AI in customer-facing applications such as support or sales. ## More from blog Jun 23, 2025 What Is Customer Observability? A New Way to Understand Every Customer Interaction In the early 2010s, Uber entered New York City and transformed the transportation landscape. For decades, the city had limited taxi supply through a tightly controlled medallion system, capping the number of yellow cabs to under 14,000. Thi Nov 10, 2024 How to Build a Customer Experience Dashboard in 4 Steps Due to the lack of data, most customer service professionals struggle to triage requests and track customer sentiment over time. But that’s not the case anymore. Customer experience dashboards resolve this issue, giving them more direction Nov 18, 2024 Chatbot Analytics: 13 Metrics That Every Business Should Track How often have you built a chatbot and spent days thinking through every possible workflow only to deploy it and see that users never use them? Our guess: too often. The bigger issue is that your chatbot analytics tool may have marked this Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## What Is Intercom? Customer Engagement Through Chat URL: https://www.dimensionlabs.io/blog/what-is-intercom-empowering-customer-engagement-through-chat-systems Blog # What is Intercom: Empowering Customer Engagement Through Chat Systems In a world where the customer is king, organizations wrestle with how to provide a seamless experience that keeps customers engaged while fostering loyalty. With a staggering 79% of consumers preferring real-time interac… Rumeer Keshwani , Growth & Strategy Feb 13, 2025 4 min read ## What is Intercom? In a world where the customer is king, organizations wrestle with how to provide a seamless experience that keeps customers engaged while fostering loyalty. With a staggering 79% of consumers preferring real-time interaction over waiting for responses, the need for robust communication systems is palpable. Enter Intercom, an innovative software platform transforming customer engagement through chat systems. Intercom combines live chat with modern AI functionalities to not only answer inquiries promptly but also to enhance the overall customer experience. In this article, we will explore what Intercom is, its offerings, and how chat systems, both generative AI and live chat, play a pivotal role in customer engagement. Further, we will look at the essential role analytics plays in implementing and optimizing these solutions to equip customer-facing teams. Intercom is a customer messaging platform designed to facilitate seamless communication between companies and their customers. Initially launched in 2011, it has evolved into a comprehensive suite of tools addressing various customer interaction needs, including: - Live Chat : A real-time messaging service allowing businesses to engage directly with their website visitors. - Automated Messaging : Using chatbots (such as Fin AI ) to send targeted messages based on user behavior, enhancing proactive engagement. - Support Ticketing : A system to manage customer queries efficiently, track issues, and measure resolution effectiveness. - Product Tours : Interactive guides helping users understand how to navigate products, improving onboarding experiences. The platform shines brightly in its versatility, catering to businesses of different sizes and industries, from startups to established enterprises, each benefiting from enhanced lead generation, customer support, and user engagement. ### The Power of Chat Systems As the digital landscape evolves, chat systems have become crucial in improving customer interactions. Let’s break down two central aspects of chat systems: live chat and generative AI. ### Live Chat vs. Generative AI Live Chat is a tool, primarily facilitated by human agents, that allows businesses to interact with customers in real-time. This option is invaluable for dealing with complex inquiries and providing personalized service. It exemplifies the "human touch" necessary in customer interactions. Generative AI , notably the advancements seen in Large Language Models (LLMs), automates customer support by providing instant responses based on vast datasets and NLP capabilities. Unlike traditional automated systems that followed a structured flow and predetermined responses, generative AI can craft relevant, context-aware responses dynamically. This opens doors to intuitive conversations and a more engaging customer experience. ### The Role of Analytics No implementation of chat systems would be complete without a robust analytics backbone. Using data analytics: - Performance Monitoring : Regularly tracking key performance indicators (KPIs) such as engagement rates, response times, and customer satisfaction scores is vital for assessing both live and automated interactions. - Optimization : Analytics allows for ongoing optimization by identifying patterns in customer behavior, helping businesses tailor their messaging and improve user experience. - Feedback Loops : Through monitoring conversations, organizations create feedback loops that help refine the AI model’s accuracy and relevance over time, driving continued improvement in customer interactions. ## Implementing Intercom: Challenges on the Horizon While the potential benefits of implementing a system like Intercom are significant, organizations must navigate various challenges: - Integration Complexity : Businesses often rely on a patchwork of tools. Integrating Intercom with existing systems, like CRMs, marketing platforms, and analytics tools, can pose technological hurdles. - Data Privacy Compliance : Adhering to regulations such as GDPR and CCPA is crucial, requiring careful handling of personal customer data within chat exchanges. - User Adoption : Training staff and customers to utilize the full functionality of Intercom’s features can take time and may require strategic change management approaches. ### Real-World Use Cases of Intercom - E-commerce : Companies like Shopify leverage Intercom for real-time customer support, increasing conversion rates through personalized assistance during purchasing journeys. - SaaS : Software companies use Intercom to provide in-depth product guidance through chatbots that suggest features based on observed user behavior, facilitating higher retention rates by ensuring users derive maximum value from the product. - Healthcare : Providers utilize Intercom to facilitate immediate response times for patient inquiries, significantly enhancing patient satisfaction by providing timely support options. In the e-commerce sector, businesses saw a 30% increase in conversion rates after implementing Intercom's chat system. By using automated messaging to engage visitors during peak hours and allowing agents to jump in at key interaction points, companies created a fluid buying experience that reduced friction. ### Measuring ROI & Recommended Tools To ensure the effective use of Intercom, organizations need to track specific metrics and KPIs that indicate success. Here are key metrics for evaluating performance and ROI: - Conversational Volume : The number of interactions over a defined period, indicating customer engagement. - Resolution Rate : Percentage of inquiries resolved on the first contact, higher rates signal effective customer support. - Customer Satisfaction Score (CSAT) : Measures customer satisfaction based on post-interaction surveys. - Net Promoter Score (NPS) : Evaluates customer loyalty and willingness to recommend the service. Recommended Tools for Effective ImplementationBelow are several popular tools and software that complement the usage of Intercom, promoting seamless integration and operation: - Dimension Labs : An analytics tool specifically for messaging platforms that provides usage and engagement insights. - Zapier : Automate connection between Intercom and other web applications, streamlining workflows. - Google Analytics : For tracking user interactions on your site, providing valuable insights into client behaviors. - Segment : Facilitates keeping your user data consistent across Intercom and other channels, an important requirement for effective analysis. ### Conclusion Intercom represents a monumental shift in customer engagement through the adaptable power of chat systems. By effectively integrating live chat and generative AI, businesses can offer personalized experiences that foster deep customer loyalty. However, success hinges on their ability to continually optimize interactions through substantive analytics strategies. ### FAQ and Common Questions 1. What is Intercom? - Intercom is a customer messaging platform that facilitates direct communication between companies and their customers through various tools like live chat, automated messaging, and support ticketing. 2. How does Intercom enhance customer engagement? - Intercom enhances engagement by utilizing both live chat for real-time interaction and generative AI for automated responses, thus catering to customer needs efficiently. 3. Why is analytics important in using Intercom? - Analytics provide insights that allow businesses to monitor performance, optimize interactions, and create feedback loops, improving the overall effectiveness of customer engagement strategies. Transform your enterprise with Intercom’s robust customer engagement solutions. To see what your Intercom conversations say, start with one file in Dimension Labs. ## More from blog Jan 13, 2025 Data Enrichment: Enhance Decisions with Next Gen Insights In today’s data-centric world, enterprises are inundated with vast amounts of data. A staggering 90% of the world’s data was generated in just the last two years, compelling organizations to seek innovative methods to extract meaningful ins Jan 31, 2025 Customer Interaction Analysis: Enhancing CX through Data-Driven Insights Customer Interaction Analysis (CIA) refers to the systematic evaluation of various customer touchpoints and interactions within an organization. By applying advanced analytical techniques, businesses can transform raw data from these intera Mar 26, 2025 Brand Sentiment Analysis: Leveraging Unstructured Text Analytics and LLMs for Strategic Insights In an age where brands can rise and fall at the speed of a trending tweet, understanding consumer sentiment has never been more crucial. A staggering 71% of consumers believe that brands should listen to their feedback to improve products a Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## The Missing Layer in the Modern Data Stack: Unstructured Data URL: https://www.dimensionlabs.io/blog/the-missing-layer-in-the-modern-data-stack-unstructured-data Blog # The Missing Layer in the Modern Data Stack: Unstructured Data By 2025, more than 80% of enterprise data will be unstructured. Yet if you look at the modern data stack it’s almost entirely designed for structured data. Andrew Hong , CEO, Dimension Labs Aug 28, 2025 11 min read By 2025, more than 80% of enterprise data will be unstructured. Yet if you look at the modern data stack it’s almost entirely designed for structured data . That means the most common, richest sources of customer truth (chat transcripts, call recordings, email threads, open-ended survey responses) remain invisible in most data-driven decision-making. We’ve built an incredibly powerful stack… but it’s missing a critical layer . This post explores why that gap exists, what the “missing layer” should look like, and how adding it can transform how your organization uses data to make decisions. ### What the Modern Data Stack Gets Right, and What It Misses Over the past decade, the Modern Data Stack (MDS) has gone from an emerging concept to the default architecture for analytics at many organizations. It began with the rise of cloud data warehouses like Amazon Redshift and Google BigQuery in the early 2010s, which made large-scale analytics affordable and eliminated the need for on-premises infrastructure. Soon after, automated ingestion tools like Fivetran and Stitch shifted the industry from traditional ETL to ELT, enabling raw data to be loaded directly into the warehouse and transformed later. This set the stage for the next leap forward: the transformation layer. Tools like dbt standardized how data teams modeled, cleaned, and tested warehouse data, making transformation a first-class part of analytics workflows. By the late 2010s, the stack matured with powerful business intelligence tools like Looker, Tableau, and Hex, along with reverse ETL platforms such as Census and Hightouch that pushed insights back into operational systems. The result was a streamlined data pipeline: ingestion, storage, transformation, and visualization, all optimized for structured data. Data observability platforms like Monte Carlo and Bigeye emerged to keep this structured data reliable, while the adoption of AI in recent years has started to make querying and insight generation even faster. This architecture works beautifully for structured data, anything that fits neatly into tables, with rows and columns. 🛑 The problem: the MDS doesn’t natively handle unstructured data, especially the vast, messy, language-rich datasets that tell you why customers behave the way they do. By 2025, more than 80% of enterprise data will be unstructured, things like chat transcripts, call center notes, open-ended survey responses, and online reviews. These are some of the richest sources of customer truth, but they don’t fit neatly into rows and columns. As a result, they remain siloed in platforms like Zendesk, Qualtrics, and contact center software, invisible to the warehouse and BI tools that drive most business decisions. The next evolution of the MDS will be the Unstructured Data ETL layer , a standardized way to ingest, clean, enrich, and structure language data so it becomes a first-class citizen in the analytics ecosystem. ### The Consequences of Ignoring Unstructured Data Every business is sitting on a massive and constantly growing pile of unstructured customer data. This is the data your customers produce naturally in the course of interacting with you, not in carefully designed surveys, but in their own words, in real-world contexts. Examples of unstructured data can include: Support Interactions: - Live chat logs from your website or mobile app. - Call transcripts from your contact center (transformed with voice-to-text). - Email threads between customers and support agents. - Agent notes written during or after customer calls. Product Feedback: - Open-ended survey responses (NPS, CSAT, post-purchase feedback). - Beta tester feedback during early feature releases. - Bug reports from support portals or in-app feedback forms. Public Sentiment & Brand Conversations: - Online reviews (Google, Yelp, Trustpilot). - Social media posts & comments (Twitter/X, Facebook, LinkedIn, Reddit). - Community forums or user groups. Internal Operational Notes: - Field service reports from technicians in the field. - Customer success notes from QBRs or account reviews. - Sales call summaries from CRM platforms. Most of this is already collected somewhere , in Zendesk, Intercom, Salesforce, Qualtrics, Genesys, Gong, or other point solutions, but it is rarely: - ❌ Centralized in the data warehouse. - ❌ Normalized into a consistent structure. - ❌ Standardized to a shared schema or taxonomy. - ❌ Joined with other data sources. - ❌ Made queryable by BI or analytics tools. The result? Companies are sitting on a goldmine of “dark data” , information they already own but can’t see, search, or analyze systematically. And the consequences of ignoring it are serious: 1. Customer Experience Blind Spots - You’re basing CX strategy on the 5–10% of customers who fill out structured surveys, while ignoring the majority. - Silent churners, customers who leave without warning, often express frustration in support interactions long before they go. Without analyzing those conversations, you miss out on early warning signals. 2. Slower Product Iteration - Your product roadmap depends on anecdotal feedback from a few vocal customers or internal advocates. - Rich feature requests and bug descriptions in open-text feedback never reach product teams in a usable form. 3. Operational Inefficiency - Support teams manually tag tickets, leading to inconsistent categorization and wasted time. - The same root cause might be investigated multiple times because insights aren’t centralized or searchable. 4. Incomplete Business Intelligence - Dashboards track “what” is happening (churn rate, NPS, ticket volume) but not “why” it’s happening. - You can’t tie behavioral metrics to the actual language customers use, making it impossible to quantify the impact of recurring issues. 5. Missed Revenue Opportunities - Upsell and cross-sell signals hidden in conversations (e.g., “I wish you offered X…”) go unnoticed. - High-value accounts with growing dissatisfaction aren’t flagged early enough for intervention. 6. Weaker AI Performance - Without structured historical conversation data, AI models for customer support, churn prediction, or personalization have a weaker foundation and produce lower-quality outputs. - Gen AI experiences such as chatbots also introduce brand risk, if you are unable to analyze the quality of output in the conversations. Ignoring unstructured data is like running your business with 80% of your vision blocked . You may still move forward, but you’ll miss opportunities, overlook risks, and make decisions without the full context. ### Why the Gap Exists If unstructured data is so valuable, why isn’t it already part of the Modern Data Stack? The answer is simple: the MDS was never designed for it. The Modern Data Stack, as it exists today, is built on a foundation of structured, schema-defined data. That’s why it works so well for transactional data from ERP systems, clickstream data from analytics platforms, and operational data from SaaS apps. These sources already fit neatly into rows and columns. Unstructured data does not. Here’s why the gap exists: - No Native Schema - A chat log doesn’t have a consistent table structure. - Conversations can vary in length, topic, and language. - Even if you drop raw JSON or text blobs into a warehouse, you can’t query them meaningfully without first structuring them. - Complex Cleaning Requirements - Unstructured sources require advanced preprocessing: - Splitting long logs into individual conversational sessions. - Merging partial records from different systems. - Removing or anonymizing PII to maintain compliance (GDPR, CCPA). - Specialized Enrichment Needs - You need Natural Language Processing (NLP), Large Language Models (LLMs), and vector embeddings to extract sentiment, topics, intent, and root cause. - These capabilities aren’t built into the core MDS tools, they require specialized infrastructure. - Fragmented Tooling - The typical enterprise uses multiple tools for support, surveys, and social media monitoring. - Each tool has its own API, export format, and limitations. - Stitching these together is costly and brittle. - High Engineering Lift for In-House Solutions - Data teams often try to build custom unstructured pipelines themselves. - These are expensive to maintain, hard to scale, and require rare skill sets that blend data engineering, NLP, and domain knowledge. - BI Tool Limitations - Looker, Tableau, and other BI platforms are designed for querying structured data. - Even if you ingest raw unstructured data, BI tools can’t visualize or aggregate it without heavy preprocessing. ⚠️ The net effect: unstructured data remains trapped in the operational systems where it’s created, inaccessible to the very tools and workflows that drive the rest of the business forward. This is exactly why the next evolution of the Modern Data Stack will require a dedicated Unstructured Data ETL layer , purpose-built to bridge this gap. ### The Missing Layer: Unstructured Data ETL We believe the Modern Data Stack needs a new standard layer , one that does for conversational and free-text data what dbt did for structured tables. We call this Unstructured Data ETL. This isn’t a “nice-to-have”, it’s the only way to consistently unlock the 80% of enterprise data that’s currently invisible to decision-making. Right now, companies either ignore unstructured data entirely or hack together ad hoc pipelines that are brittle, expensive to maintain, and impossible to scale. The job of the Unstructured Data ETL layer is to: - Ingest from any unstructured source , APIs, batch exports, direct database pulls, or streaming sources like Kafka. The goal is source-agnostic flexibility , so you can unify call center transcripts from Genesys, NPS comments from Qualtrics, and sales call summaries from Gong into one pipeline. - Normalize and clean raw inputs , Split multi-interaction logs into sessions, merge partial records, remove or mask PII, and standardize metadata like timestamps and customer IDs. This transforms raw text chaos into consistent, analytics-ready records. - Enrich with AI-driven dimensions , Use LLM orchestration and embeddings to classify sentiment, detect topics, surface root causes, and score effort. Crucially, this can be tailored to your domain, the “effort” score for a banking call is different from one for a hotel booking. - Map to a reusable schema , Instead of dumping unstructured text directly into your warehouse, you output structured tables with defined columns: Category, Sentiment, Root Cause, Product Area, Resolution Time, etc. This is what makes language data queryable with SQL, visualizable in BI tools, and joinable to other datasets. - Push BI-ready outputs back into the warehouse , Just like structured ETL, the final step is delivering the transformed dataset where your data teams already work, Snowflake, BigQuery, Redshift, so it plugs directly into the rest of your analytics ecosystem. Why this layer is essential: Without it, unstructured data remains siloed in operational tools, unusable for analytics. With it, you transform “dark data” into first-class citizens in the MDS , available to the same dashboards, ML models, and decision processes as structured data. ### How This Fits into the Modern Data Stack The Unstructured Data ETL layer isn’t meant to replace any part of the MDS, it’s meant to slot in alongside the existing layers, enhancing data quality and completeness. Here’s how it fits: - Ingestion: Works in parallel to tools like Fivetran, Airbyte, or custom pipelines, but instead of pulling in rows from SaaS APIs into a table, it’s ingesting conversation logs, reviews, and other text data. - Transformation: Just as dbt models transactional data into analytics-ready tables, Unstructured Data ETL models conversational data into structured, dimensional datasets. - Storage: Output lands in your central warehouse, just like any other fact or dimension table, ready to be joined with other sources. - Visualization & Activation: Once it’s in the warehouse, Looker, Tableau, Hex, or reverse-ETL tools can consume it, surfacing language insights alongside your KPIs, or pushing them back into CRMs, support platforms, or marketing automation tools for activation. A complete MDS pipeline for unstructured data might look like this: - Ingestion: Pull chat, call, survey, and review data from source systems (via API or batch). - Normalization: Split into sessions, standardize metadata, remove PII. - Enrichment: Run through multi-model LLM orchestration to generate sentiment, topics, intent, root cause, and other domain-specific labels. - Schema Mapping: Map to a defined analytics schema (e.g., Category, Sentiment, Effort Score, Product Area). - Warehouse Output: Write BI-ready tables back into Snowflake/BigQuery. - Visualization & Activation: Use in BI dashboards, AI models, or push to operational systems for real-time action. The key shift: Unstructured data stops being an afterthought handled in separate “voice of the customer” tools, it becomes a native, queryable, joinable dataset in your MDS, powering cross-functional insights. Example: Instead of “Our NPS dropped by 5 points last quarter” (what), your dashboard now shows: “Our NPS dropped by 5 points last quarter, primarily due to check-in delays at three specific locations, as flagged in 18% of guest chats” (why). ### Business Impact: Why It Matters Now Implementing an Unstructured Data ETL layer doesn’t just improve analytics, it changes how the entire organization operates. Different stakeholders across the business feel this impact in unique, tangible ways: #### For Data & Analytics Engineers - Eliminate ad hoc pipelines: No more one-off scripts for every source of unstructured data, one standardized, reusable pipeline handles all ingestion, cleaning, and enrichment. - Increase data reliability: Clean, schema-mapped conversational data in the warehouse reduces data debt and ensures reproducibility. - Faster project delivery: Prebuilt enrichment and mapping functions mean analytics engineers can deliver new datasets to stakeholders in days, not months. #### For Data Leaders (CDOs, Heads of Analytics) - Full customer visibility: Finally integrate unstructured data into the MDS to achieve a 360° view of the customer, the “why” alongside the “what.” - Better ROI on warehouse investments: The data warehouse becomes the single source of truth for all customer data, not just transactional or survey data. - Scalable governance: Standardized ingestion, PII handling, and schema mapping reduce compliance risk while improving auditability. #### For CX and Support Leaders - Prioritize what matters most: See the actual top drivers of customer dissatisfaction in real time, not just anecdotal escalations. - Faster root cause resolution: Identify systemic issues (e.g., 23% of call transcripts in Q3 cite payment errors) and work cross-functionally to resolve them. - Cost savings: Reduce manual ticket tagging, repetitive customer investigations, and handle times. #### For Product Leaders - Faster feedback loops: Turn open-ended feedback into structured insights that can be acted on during sprint planning, improving feature adoption and reducing churn. - Risk detection: Spot emerging product issues before they escalate into PR crises or revenue loss. - Competitive advantage: Use real customer language trends to inform market positioning and product roadmaps. #### For Executives & Strategy Leaders - Decision-making with full context: Move beyond KPI dashboards to truly understand why metrics change. - Quantifiable ROI: Tie improvements in NPS, retention, or operational efficiency directly to actions taken from unstructured data insights. - Stronger AI strategy: Structured unstructured data powers more accurate downstream AI models, from churn prediction to personalization engines. ### Why This Moment is Different The idea of analyzing unstructured data isn’t new, companies have been trying for decades, often with keyword-based sentiment tools, social listening platforms, or expensive consulting projects. But until recently, the effort was costly, slow, and hard to scale. Today, four major shifts make this the perfect moment for Unstructured Data ETL to become a standard MDS layer: - ⚡ LLM Maturity & Affordability , Modern large language models can classify, summarize, and label text at scale with speed and accuracy that was impossible even two years ago, at a fraction of the cost of legacy NLP approaches. - ⚡ Data Stack Readiness , Most enterprises already have a robust MDS in place for structured data, making it far easier to slot in an unstructured layer without overhauling infrastructure. - ⚡ Economic & Competitive Pressures , Leaders are under pressure to find efficiency gains and revenue opportunities without expanding headcount. Unstructured data is a “goldmine” of already-collected insights that can drive measurable impact quickly. - ⚡ Cultural Shift in Data Teams , Analytics engineering and the dbt movement have redefined what’s possible for data teams, making them more open to adopting new modeling layers when they see clear ROI. The convergence of these factors means that what was once aspirational is now entirely practical, and those who move early will define the category. ### The Path Forward The “Unstructured Data ETL” layer is not a trend, it’s the inevitable next stage of the Modern Data Stack. Just as dbt became the standard for transforming structured data in warehouses, a similar standard will emerge for ingesting, cleaning, enriching, and modeling unstructured language data. Our vision: Every data team should be able to treat unstructured data as easily as SQL tables, no brittle pipelines, no manual tagging, no siloed tools. How to get started: - Audit your unstructured data sources , Identify where conversational and free-text data is stored today (e.g., Zendesk, Qualtrics, call center logs). - Pick a high-impact pilot use case , For example, combining support ticket data with product usage data to understand churn drivers. - Implement an Unstructured Data ETL pipeline , Focus on ingestion flexibility, schema mapping, and enrichment tailored to your domain. - Integrate into your MDS , Push the resulting datasets back into your warehouse and BI tools so they become part of standard analytics workflows. - Measure & expand , Track ROI through time savings, cost reduction, and revenue lift, then expand coverage to new sources and teams. The winners in this next phase of the data stack will be those who act now. If you’re not operationalizing your unstructured data, you’re making decisions with one eye closed, and your competitors won’t be. ## More from blog Sep 29, 2025 Why Building Your Own Unstructured Data ETL Pipeline Is Harder Than You Think For the last decade, enterprises have built powerful data stacks for structured information. With modern tools like Fivetran, Snowflake, dbt, and Tableau, data teams can ingest, store, transform, and analyze structured data at scale. But wh Sep 18, 2025 The Rise of the Language Data Engineer Over the last decade, the modern data stack has gone from niche idea to mainstream. Tools like Snowflake, dbt, and Looker redefined how companies ingest, transform, and activate data. Entire roles were born out of this movement, most famous Apr 14, 2026 The Analytics Stack in 2026: Layers, Tools, Trends, and What’s Still Missing A VP of Analytics at a SaaS company just received the monthly operational report. Net revenue retention dropped from 112% to 104% in Q3, an eight-point swing that rewrites the board deck. Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Data Taxonomy: The Power of Unstructured Text Analytics URL: https://www.dimensionlabs.io/blog/data-taxonomy-unveiling-the-power-of-unstructured-text-analytics Blog # Data Taxonomy: Unveiling the Power of Unstructured Text Analytics Data taxonomy refers to a structured classification system that organizes data into defined categories, roles, and relationships. Implementing a robust data taxonomy can be the difference between chaotic data silos and a… Rumeer Keshwani , Strategy & Growth Mar 27, 2025 5 min read ## Introduction Data taxonomy refers to a structured classification system that organizes data into defined categories, roles, and relationships. Implementing a robust data taxonomy can be the difference between chaotic data silos and a strategic powerhouse of insights.  Understanding the intricate relationship between data taxonomy and unstructured text analytics, will reveal strategies for custom prompting, data structures, and the pivotal role of large language models (LLMs) in analyzing this data. For an in-depth look at our advanced data solutions, visit our Data & Analytics page . ## Understanding Data Taxonomy Data taxonomy systems act as blueprints for data management, providing a hierarchical structure where information is organized and easily accessible. This structure is not merely a matter of organization; it’s a strategic asset that drives data quality and consistency in unstructured text analysis. A well-defined data taxonomy categorizes data into standard units, enabling organizations to find, retrieve, and use information with reliability and precision. In contrast, poor or absent taxonomy can lead to data duplication, misinterpretation, and lost opportunities, resulting in wasteful expenditures of resources. The principles behind effective data taxonomy include clarity in definitions, logical grouping of entities (like customer interactions, product feedback, or social media posts), and metadata tagging that enhances searchability. By establishing clear hierarchies, such as categorizing feedback by sentiment or topic, organizations can enable more targeted analytics. Many organizations struggle with the integration of disparate data sources, resulting in fragmented insights and delayed decision-making. A lack of effective taxonomy can lead to issues such as compromised data integrity, inefficient data retrieval, and a growing reliance on manual processes that are prone to error. ## Harnessing Unstructured Text Analytics Unstructured data encompasses a wide array of formats including text documents, emails, audio, video, and social media posts. While this data is rich in context and insight, its complexity makes analysis challenging. - Key Principles of Unstructured Text Analytics : Natural Language Processing (NLP) is central to extracting meaningful insights from unstructured text. Techniques such as tokenization, entity extraction, and sentiment analysis allow businesses to glean valuable information from customer interactions. For example, organizations can evaluate email correspondence to identify patterns in sentiment or correlate social media mentions with customer satisfaction metrics. - Integration with Data Taxonomy : By pairing unstructured text analytics with a strong data taxonomy, organizations can streamline their methodologies. Establishing a standardized taxonomy for categorizing and tagging unstructured text ensures a consistent approach to analysis, thus enhancing the reliability of insights derived from complex data. ## Practical Applications of Data Taxonomy in Unstructured Text Analytics ### Custom Prompting and Data Structures Implementing tailored prompting techniques and specific data enrichment protocols further bolsters the efficacy of unstructured text analytics. - Custom Prompting Strategies : Custom prompts are essential in guiding LLMs to generate relevant output. For instance, a business could structure prompts not only to ask what a customer thinks of a new product, but to nuance the query based on previously analyzed sentiment towards related products or issues. This contextual prompting maximizes the LLM’s capabilities in generating insightful analyses. - Enrichment Protocols : Data enrichment transforms raw data into actionable insights through various methodologies. For example, incorporating external datasets that provide demographic or psychographic information enhances the context around unstructured feedback. The enriched data can lead to better-targeted marketing efforts or more effective service recovery processes. - Data Structures Utilized in LLMs : Structured data plays a crucial role in empowering LLMs to operate effectively. Utilizing well-defined data structures ensures consistency in how the models learn and operate. Models like OpenAI’s GPT series employ vast training datasets, organized thematically to ensure that responses are context-sensitive and accurate. ## Measuring ROI on Data Taxonomy Implementations Implementing a data taxonomy is not merely an exercise in data organization; it's an investment that requires diligent measurement of ROI. Key performance indicators (KPIs) may encompass: - Data Retrieval Efficiency : Calculate the reduction in time spent finding data before and after taxonomy implementation. For example, a 30% decrease in retrieval times can directly translate into enhanced operational efficiency. - Actionable Insights Derived : Track the percentage of decisions made based on insights gleaned from structured unstructured data analysis. A business could aim for an increase in data-driven strategic decisions by 20% in a quarter. - Cost Avoidance : Measure the cost savings from reduced manual data handling and errors. A precise calculation here may uncover significant avoidance of penalties or lost revenues due to inefficiencies. ## Recommended Tools and Software for Enterprises To maximize the potential of data taxonomy and unstructured data analytics, organizations can leverage an array of tools: - Dimension Labs: Our tooling is purpose built to crack open your Taxonomy and rework it from the inside out. Our novel enrichments and custom prompting products provide you the platform you need to take a fine comb to your data and structure it for what it truly is, not just what you think. - Apache Solr : This open-source search platform provides robust indexing and search capabilities for unstructured data environments, enabling organizations to enhance data accessibility. - Spacy : A powerful NLP library, Spacy is designed for high efficiency in processing large-scale unstructured data, facilitating entity extraction and text analytics. - OpenAI’s GPT : As a frontrunner in generating human-like text, GPT excels in deriving insights when prompted correctly, highly advantageous for unstructured data applications. - Rasa : Enabling conversational AI, Rasa offers tools that integrate natural language understanding with data classification, supporting organizations in automating customer interactions accurately. - KNIME : This open-source analytics platform assists data science workflows in incorporating unstructured data, enriching the analysis process through visual programming. ## Conclusion In the ever-evolving landscape of data analytics, the importance of data taxonomy in unstructured text analytics cannot be overstated. It empowers organizations to turn vast amounts of chaotic data into coherent, actionable insights, driving improvements across all sectors. As organizations increasingly harness custom prompting, enriched data structures, and LLM capabilities, they stand poised to unlock unparalleled insights from unstructured data. ### FAQ What is Data Taxonomy? Data taxonomy is a structured classification system that organizes data into defined categories, enabling better data management, retrieval, and analysis. How does Data Taxonomy enhance Unstructured Text Analytics? By categorizing unstructured data into predefined taxonomies, organizations streamline data retrieval and enhance the reliability of insights derived from complex information. What tools can help in implementing Data Taxonomy? Tools like Apache Solr, Spacy, OpenAI’s GPT, Rasa, and KNIME can significantly enhance data taxonomy application and support unstructured text analytics. Enterprise-Focused CTA Transform your enterprise with AI-driven insights by implementing robust data taxonomy today. To try it on your own data, start with one file and revolutionize your data approach. Your journey into smarter data utilization begins here, don't let valuable insights slip away. ## More from blog Sep 26, 2025 A Guide to Unstructured Data Management In today’s data-driven world, organizations are inundated with vast amounts of information, much of which is unstructured. Unlike traditional structured data neatly organized in databases, unstructured data includes emails, call transcripts Mar 26, 2025 Brand Sentiment Analysis: Leveraging Unstructured Text Analytics and LLMs for Strategic Insights In an age where brands can rise and fall at the speed of a trending tweet, understanding consumer sentiment has never been more crucial. A staggering 71% of consumers believe that brands should listen to their feedback to improve products a Jan 13, 2025 Data Enrichment: Enhance Decisions with Next Gen Insights In today’s data-centric world, enterprises are inundated with vast amounts of data. A staggering 90% of the world’s data was generated in just the last two years, compelling organizations to seek innovative methods to extract meaningful ins Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## Chatbots vs Conversational AI: A Complete Guide URL: https://www.dimensionlabs.io/blog/chatbots-vs-conversational-ai-a-complete-guide Blog # Chatbots vs Conversational AI: A Complete Guide Chatbots have become commonplace in the business world. You can find them on almost every website these days, which can be backed by the fact that 80% of customers have interacted with a chatbot previously. Eric Griffing , Conversational AI Advocate Nov 15, 2024 5 min read ## Chatbots vs. Conversational AI: What's the difference? Chatbots have become commonplace in the business world. You can find them on almost every website these days, which can be backed by the fact that 80% of customers have interacted with a chatbot previously. They're popular due to their ability to provide 24x7 customer service and ensure that customers can access support whenever they need it. As chatbots offer conversational experiences, they're often confused with the terms "Conversational AI," and "Conversational AI chatbots." While they may seem to solve the same problem, i.e., creating a conversational experience without the presence of a human agent, there are several distinct differences between them. In this article, we'll explain the features of each technology, how they work and how they can be used together to give your business a competitive edge over other companies. ## Conversational AI is a Technology Simply put, chatbots are computer programs that mimic human conversations, whereas conversational AI is the technology that powers it and makes it more "human." The key difference is in the level of complexity involved. Chatbots use basic rules and pre-existing scripts to respond to questions and commands. At the same time, conversational AI relies on more advanced natural language processing methods to interpret user requests more accurately. With the chatbot market expected to grow to up to $9.4 billion by 2024 , it’s clear that businesses are investing heavily in this technology, and that won’t change in the near future. However, with the many different conversational technologies available in the market, they must understand how each of them works and their impact in reality. ## What is a Chatbot? A chatbot is a computer program that emulates human conversations with users through artificial intelligence (AI). The most common type of chatbot is one that answers questions and performs simple tasks by understanding the conversation's words, phrases, and context. These basic chatbots are often limited to specific tasks such as booking flights, ordering food, or shopping online. There are two types of chatbots: rule-based chatbots and AI-based chatbots. - Rule-based chatbots: Rely on predetermined answers to questions and don't learn from their conversations with humans. They can reply with canned responses triggered by specific keywords or phrases. Rule-based chatbots don't know how to respond to new questions or if the user says something different than expected. - AI-based chatbots: Use AI to learn from interactions with humans so they can better understand the needs of their users over time and respond intelligently based on those needs. AI chatbots can learn from past conversations and adapt based on what they learned during previous interactions with humans. ## What is Conversational AI? Conversational AI allows your chatbot to understand human language and respond accordingly. In other words, conversational AI enables the chatbot to talk back to you naturally. It uses speech recognition and machine learning to understand what people are saying, how they're feeling, what the conversation’s context is and how they can respond appropriately. Also, it supports many communication channels (including voice, text, and video) and is context-aware, allowing it to understand complex requests involving multiple inputs/outputs. The critical difference between chatbots and conversational AI is that the former is a computer program, whereas the latter is a type of technology. A few examples of conversational AI chatbots include Siri, Cortana, Alexa, etc. Depending on the sophistication level, a chatbot can leverage or not leverage conversational AI technology. If your chatbot is trained using Natural Language Processing (NLP), is context-aware, and can understand multiple intents, it’s a conversational AI chatbot. Chatbots are often leveraged by businesses to help meet certain marketing, sales, or support goals and their success is tracked by metrics such as goal completion rate . There are three kinds of conversational AI applications, and they are: - Chatbots: Chatbots use conversational AI to simulate a conversation flow with a human. Not all chatbots use conversational AI, but the ones that do, tend to provide a more natural and relevant output as it's trained using natural language processing (NLP) models. - Voice assistants: They're software programs that carry out tasks based on voice commands. It uses voice recognition, voice synthesis, and language processing algorithms to understand the command and provide the necessary outputs. A few examples include Amazon's Alexa, Apple's Siri, Microsoft's Cortana, etc. - Virtual assistants: They're powered by AI and are context-aware chatbots that help you perform specific tasks. They're powered by NLP and natural language understanding (NLU) models, making their output more personalized, accurate, and engaging. ## Chatbot vs. Conversational AI: Examples #### Examples of rule-based chatbots - SendinBlue SendinBlue’s Conversations is a flow-based bot that uses the if/then logic to converse with the end user. You can set it up to answer specific logical questions based on the input given by the user. While it's easy to set up, it can't understand true user intent and might fail for more complex issues. - Sprinklr In this example by Sprinklr, you can see the exact conversational flow of a rule-based chatbot. Each response has multiple options (positive and negative), and clicking any of them, in turn, returns an automatic response. This is more intuitive as it can recognize serial numbers stored within their system, requiring it to be connected to their internal inventory system. ### Examples of conversational AI - IBM Watson This conversational AI chatbot (Watson Assistant) acts as a virtual agent, helping customers solve issues immediately. It uses AI to learn from conversations with customers regularly, improving the containment rate over time. The chatbot is enterprise-ready, too, offering enhanced security, scalability, and flexibility. - Babylon Health Babylon Health's symptom checker uses conversational AI to understand the user's symptoms and offer related solutions. It can identify potential risk factors and correlates that information with medical issues commonly observed in primary care. Based on that, it provides an explanation and additional support if needed. ## Conversational AI is the future Chatbots and conversational AI are two very similar concepts, but they aren't the same and aren't interchangeable. Chatbots are tools for automated, text-based communication and customer service; conversational AI is technology that creates a genuine human-like customer interaction. For this reason, many companies are moving towards a conversational AI approach as it offers the benefit of creating an interactive, human-like customer experience. A recent PwC study found that due to COVID-19, 52% of companies increased their adoption of automation and conversational interfaces, indicating that the demand for such technologies is rising. Additionally, these new conversational interfaces generate a new type of conversational data that can be analyzed to gain better understanding of customer desires. Those who are quick to adopt and adapt to this technology will pioneer a new way of engaging with their customers. ## More from blog Nov 1, 2024 What is a Conversational Agent? A conversational agent is any dialogue system that conducts natural language processing (NLP) and responds automatically using human language. Conversational agents represent the practical implementation of computational linguistics and are Nov 16, 2024 What Are Enterprise Chatbots And How Do They Work? Gartner defines chatbots as “A domain-specific conversational interface that uses an app, messaging platform, social network or chat solution for its conversations.” Nov 12, 2024 What does a Conversation Designer do? Since the world has gone digital, the demand for chatbots and voice assistants has been rising fast. Due to this, new roles like conversation designers are emerging that allow companies to create better conversational experiences for their Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › --- ## What Is Customer Observability? | Dimension Labs URL: https://www.dimensionlabs.io/blog/customer-observability Blog # What Is Customer Observability? A New Way to Understand Every Customer Interaction In the early 2010s, Uber entered New York City and transformed the transportation landscape. For decades, the city had limited taxi supply through a tightly controlled medallion system, capping the number of yellow cabs … Jason Potteiger , Principal Solutions Engineer Jun 23, 2025 16 min read In the early 2010s, Uber entered New York City and transformed the transportation landscape. For decades, the city had limited taxi supply through a tightly controlled medallion system, capping the number of yellow cabs to under 14,000. This artificial constraint suppressed not just availability, but also latent demand. People didn’t hail cabs not because they didn’t want them, but because they couldn’t get them. When Uber lifted the supply constraint, something remarkable happened. The cost of a ride didn’t plummet. Instead, demand surged. Wait times dropped. Coverage expanded. A flood of riders who had previously relied on walking, public transit, or nothing at all embraced a new kind of access. Uber didn’t just meet existing demand, it revealed it. The same dynamic is playing out inside the modern enterprise. If you’re reading this, you’re already aware of a truth most organizations are only beginning to confront: customer data is abundant, but usable insight is not . Every day, your business generates tens of thousands of customer interactions across dozens of channels, support chats, call transcripts, surveys, reviews, app feedback, social media comments. Buried inside these conversations are the signals your teams need most: - Where friction is occurring - What customers actually value - Which products are confusing or failing expectations - Where your next competitive advantage lies But for most companies, these signals remain invisible. Feedback is scattered across tools, trapped in freeform text, and too complex for conventional business intelligence tools to process. Sentiment analysis is too shallow. Keyword searches are too brittle. Machine learning models often return black-box outputs that don’t align with real business questions. Meanwhile, the internal demand for this insight is dramatically undercounted. CX and VoC teams are often small, overextended, and working with limited tooling. What they’ve been able to deliver so far is largely constrained to structured surveys and time-consuming manual analysis. As a result, the full value of unstructured customer data has remained locked away, hidden behind technical bottlenecks and organizational silos. ## What is Customer Observability? In the age of digital-first business, having a “360-degree view of the customer” is no longer a competitive advantage, it’s a baseline expectation. But what if you could go further? Customer Observability is a new level of customer data unlocked by Dimension Labs. It moves beyond the static idea of a single, unified customer profile and introduces a dynamic, multi-dimensional framework for understanding customer behavior, sentiment, intent, and feedback, tailored to the goals of every team across your organization. At its core, Customer Observability is powered by two things: - A single source of truth for omnichannel customer data , including chat logs, call transcripts, online reviews, survey responses, social media mentions, and support tickets. - The ability to generate stakeholder-specific views of that data , using LLM-powered prompts, enrichment layers, and clustering tools to highlight only what’s most relevant to each function. For a CX leader, that might mean uncovering friction points in a digital onboarding flow. For a product manager, it might mean tracking feature-level feedback post-launch. For a compliance team, it could mean surfacing hallucinations in LLM-generated chatbot responses. Each view is grounded in the same customer data, but filtered, structured, and prioritized through the lens of that stakeholder’s objectives. In this way, Customer Observability is not just a better view of the customer, it’s the right view for the right person, at the right time. In the next section, we’ll explore six use cases that bring this idea to life, demonstrating how Dimension Labs transforms raw feedback into strategic, stakeholder-aligned intelligence at scale. ## Use Case: Customer Experience (CX) Unlocking the Voice Behind the Score: How Dimension Labs Reimagines Open-Ended Survey Feedback ### The Challenge: A Reliance on Metrics Without Meaning Customer experience (CX) has long been quantified through standardized metrics, CSAT, NPS, CES, designed to deliver fast, comparable snapshots of satisfaction and loyalty. Yet these scores alone tell a fraction of the story. Beneath every 7 out of 10 lies a question: why? For most organizations, the answer is inaccessible. While surveys often include open-ended fields intended to provide context, the reality is that these responses are underutilized. Manually reviewing thousands of free-text responses is time-consuming and inconsistent. As a result, CX teams fall back on numeric aggregates or anecdotal comments, and open-ends, the richest source of insight, remain dark data. Traditional approaches to text analytics have failed to solve this. Sentiment analysis lacks nuance, and keyword tagging struggles with complexity. Even advanced NLP models require time-consuming training and deliver results bound by rigid taxonomies. This presents a paradox: at a time when customers are providing more feedback than ever, CX leaders still struggle to understand the root causes of satisfaction, dissatisfaction, or churn. ### The Solution: Transforming Open-Ended Responses into Operational Insight Dimension Labs enables CX leaders to go beyond the metric and directly into the meaning behind it. By applying its proprietary text dimensionality framework, Dimension Labs analyzes every open-ended survey response, at scale and in context. Rather than viewing survey feedback as isolated snippets, the platform enriches each entry with predicted scores (e.g., effort, satisfaction, resolution) and automatically applies labeled drivers derived from LLM-powered prompts. What sets Dimension Labs apart is its ability to generate multiple dimensions of analysis from a single data set , tuned to the needs of different roles across the organization. For example: - CX teams can instantly surface the top drivers of NPS detractors, passives, and promoters. - Operations teams can isolate friction points across locations, shifts, or channels. - Digital teams can identify UI pain points or bottlenecks mentioned in feedback. - Executives can access role-specific summaries mapped to strategic KPIs. No model training. No keyword rules. No loss of fidelity. ### Why Legacy Tools Fall Short Legacy survey platforms focus on structured data, offering reliable score tracking but poor qualitative analysis. Sentiment analysis misinterprets tone for meaning, while keyword-based methods collapse nuance into generalized themes. Even ML-driven solutions struggle to scale across roles or adapt to business-specific priorities. Dimension Labs redefines what’s possible by eliminating these constraints. It delivers: - Speed without sacrifice : Full enrichment and analysis in minutes, not months. - Depth without rigidity : Open-ended prompts uncover themes ML can’t anticipate. - Breadth without duplication : One source of data supports unlimited views, tailored to each stakeholder. ### The Impact By unlocking the latent value of open-ended survey data, Dimension Labs empowers CX teams to make faster, more confident decisions grounded in actual customer voice. From optimizing journey touchpoints to elevating strategic planning, organizations gain a measurable advantage: - 50–70% reduction in manual feedback review time - 2x–3x increase in insight generation from the same dataset - Faster correlation between text-based themes and NPS, CSAT, or churn This is what it means to turn customer data into customer intelligence. ## Use Case: VoC Insights From Siloed Listening to Unified Intelligence: Redefining Voice of the Customer at Scale ### The Challenge: Fragmented Feedback, Generalized Insights In today’s experience economy, Voice of the Customer (VoC) teams are tasked with making the customer’s perspective legible, and actionable, across the business. But as the volume and diversity of customer data expands, most teams still rely on siloed tools and one-size-fits-all taxonomies. Survey platforms analyze structured responses. Social listening tools monitor brand mentions. Review aggregators visualize sentiment trends. Each of these systems offers a partial view. None offer integrated insight. Even more critically, the outputs are often too shallow, or too generic, to inform real decisions. Stakeholders across departments ask different questions: - “What’s driving low check-in satisfaction?” - “How can we improve app ratings?” - “Which amenities do guests mention when recommending the brand?” Traditional platforms can’t adapt to this diversity of need. As a result, VoC teams spend weeks re-slicing data, creating bespoke decks, or defending abstract metrics that fail to move the organization. ### The Solution: A Unified, Multidimensional VoC Platform Dimension Labs eliminates this fragmentation by unifying all sources of customer feedback, across all channels, into a single source of truth, enriched with stakeholder-specific insight layers. What sets the platform apart is its text dimensionality framework , which allows teams to extract hundreds of distinct perspectives from a shared corpus of data. Whether the feedback comes from surveys, support interactions, social media mentions, app reviews, or forum discussions, the platform applies LLM-powered prompts to isolate themes, map relationships, and surface meaning based on the unique needs of each business unit. This means that for the first time, VoC teams can serve the entire enterprise from one intelligence engine, without duplicating effort, sacrificing nuance, or waiting on model retraining. ### Why Existing Tools Fall Short Most VoC solutions are channel-specific: - Survey platforms provide score tracking but struggle with open-ended feedback. - Social listening tools offer trend monitoring but miss customer intent or experience detail. - ML-based systems impose rigid category models that require ongoing tuning and can’t pivot quickly to new business questions. These systems may provide visual dashboards, but not decision-grade intelligence. Dimension Labs breaks this paradigm. It offers: - A true omni-channel architecture that supports 1st- and 3rd-party sources, including forums, reviews, and social media. - Open-ended prompting for stakeholder-specific analysis, no retraining, no delay. - Clustering and dimensional mapping that reveals subthemes, patterns, and root causes. This is not sentiment analysis in a new wrapper. It’s a rethinking of how businesses listen to and act on what their customers are actually saying. ### The Impact VoC teams using Dimension Labs report measurable gains in insight velocity, executive alignment, and program credibility: - 5x faster delivery of stakeholder-specific insights - 30–50% reduction in analysis cycle times - Significant improvement in alignment between customer voice and strategic planning By turning fragmented feedback into stakeholder-aligned insight, Dimension Labs transforms VoC from a reactive reporting function into a strategic advantage. ## Use Case: Product Feedback Turning Raw Feedback into Roadmap Precision: Rethinking Product Intelligence with Dimension Labs ### The Challenge: Product Teams Are Drowning in Feedback, But Starving for Clarity Product teams today face a paradox. On one hand, they have access to more customer feedback than ever, support tickets, user reviews, social media posts, in-app surveys, sales notes, and more. On the other hand, this feedback is fragmented, inconsistently tagged, and difficult to act on at scale. Most teams still rely on anecdotal input from internal stakeholders or keyword tracking in support logs. When more structure is attempted, it’s often through machine learning models that require extensive training, offer only static categorization, and fail to adapt to fast-moving product cycles. As a result, many organizations struggle to answer critical questions: - What exactly are users saying about our new feature across all channels? - What objections are surfacing repeatedly in onboarding? - Which feature requests are gaining momentum, and with whom? This creates risk across the product lifecycle, from prioritizing the wrong roadmap items to missing early warning signs of feature failure. ### The Solution: Unified, Multi-Channel Product Intelligence, at Feature-Level Resolution Dimension Labs transforms the way product teams extract, interpret, and act on customer feedback. By ingesting unstructured text from every relevant source, support chats, community forums, online reviews, surveys, social mentions, and enriching it with multi-dimensional AI labels, Dimension Labs enables teams to isolate insights tied to specific features, launches, or use cases. The platform’s open-ended prompting system allows product managers to ask targeted questions of the data, such as: - What are users trying to accomplish with [feature X]? - What objections are surfacing for [feature Y]? - What themes are emerging around our latest release? Dimension Labs returns not just themes, but contextualized clusters , showing how ideas evolve over time, who is expressing them, and how they correlate with outcomes like retention, support volume, or conversion. This is not the standard voice of the customer analysis, it’s high-resolution product telemetry derived from a cross-section of all available sources of user feedback. ### Why Traditional Approaches Fail Most product feedback tools offer fragmented, channel-specific coverage: - Support platforms provide ticket counts, not context. - Review aggregators show sentiment but not root causes. - ML classifiers require long training cycles and can’t keep up with fast-changing releases. - BI tools can’t interpret unstructured customer language at all. These tools force product managers to choose between speed and precision, and often deliver neither. Dimension Labs changes the equation. - One interface for analyzing feedback across all channels - Unlimited prompt-driven insights aligned to evolving product structures - Feature-level clustering that adapts in real time, without retraining or re-tagging In short: product teams gain an on-demand system of record for what customers think, feel, and need. ### The Impact Dimension Labs delivers measurable improvements in product intelligence workflows: - 3x faster turnaround from signal detection to stakeholder reporting - Up to 50% reduction in support-related delays for newly launched features - Greater roadmap accuracy through real-time user validation From early-stage startups to enterprise product orgs, the result is the same: More informed decisions, less guesswork, and a direct line between what customers say and what gets built. ## Use Case: Chatbot & IVR Optimization From Automated Responses to Revenue Intelligence: Optimizing Chatbot Experiences with Dimension Labs ### The Challenge: When Chatbots Become a Blind Spot in the Digital Customer Journey Chatbots and IVR systems have become foundational to modern digital experiences, helping brands reduce support costs, streamline routing, and scale self-service. But as conversational volume grows, so too does the complexity of managing performance. Most businesses measure success using aggregate metrics: session completion rates, escalation frequency, or fallback trigger counts. These indicators are directionally useful, but not diagnostic. They fail to answer questions like: - What exactly are customers asking that the bot doesn’t understand? - Where do high-value users abandon their sessions, and why? - Which interaction paths generate conversions, and which ones deter them? Without visibility into the content of conversations, companies can’t identify breakdowns or replicate successful flows. In effect, chatbots become digital black boxes, automated, but not optimized. ### The Solution: Conversation-Level Clarity, Scaled Across Millions of Interactions Dimension Labs unlocks the black box by applying its text dimensionality framework to chatbot and IVR transcripts, treating each session as a structured conversation composed of turns between customer and agent (human or AI). By parsing every message, enriching it with AI-generated labels, and clustering similar patterns into visual maps, Dimension Labs enables teams to: - Analyze chatbot performance at the session level , not just in aggregate - Understand user intent , friction points, and abandonment triggers - Discover revenue-impacting use cases that traditional metrics miss - Optimize flows based on actual behavioral insight , not guesswork This isn’t just conversation analytics, it’s strategic automation intelligence. ### Why Traditional Tools Fall Short Legacy chatbot platforms provide basic performance metrics, fallback rates, session durations, click paths, but offer no understanding of conversational meaning. Sentiment tools misread tone as success. BI dashboards are blind to dialogue structure. Manual review of transcripts is unsustainable at scale. Dimension Labs closes this gap with a conversation-first approach that enables: - Turn-by-turn parsing of chatbot interactions - User intent labeling at scale using open-ended prompts - Pattern discovery via AI-powered clustering and similarity mapping - Performance benchmarking tied to business outcomes like revenue and retention This isn’t a better dashboard, it’s a new paradigm for understanding and optimizing how automation performs in the moments that matter most. ### The Impact Brands that adopt Dimension Labs to optimize their chatbot and IVR systems report: - 2–4x faster time to resolution on flow-level performance issues - Up to 30% lift in conversion rates for key automation paths - Significant reduction in escalations through proactive refinement By turning raw chat logs into actionable intelligence, Dimension Labs transforms conversational automation into a strategic growth engine. ## Use Case: AI Agents & LLM Observability Observability for the AI Era: Ensuring Accuracy, Trust, and Alignment in LLM-Powered Customer Support ### The Challenge: As AI Agents Scale, Trust Erodes Without Oversight As businesses adopt large language models (LLMs) to power AI agents, they enter a new phase of digital transformation, one where responses feel intelligent, but the risks are harder to detect. These agents can handle thousands of customer interactions across chat, email, and support portals. But unlike human agents, they don’t make mistakes visibly. Instead, they hallucinate with confidence, answer incorrectly with perfect grammar, and occasionally reinforce outdated or contradictory information. Worse still, most enterprises lack the ability to detect when these failures occur. This creates a dangerous asymmetry: AI agents appear to be operating flawlessly, until the damage is already done. Existing monitoring tools fall short. They measure latency, token usage, and fallbacks, but not correctness, context, or confidence . And while sentiment scoring might show satisfaction trends, it cannot surface when an answer was technically wrong but pleasantly phrased. Without true observability, organizations put their brand trust, compliance posture, and customer relationships at risk. ### The Solution: Grounding AI Performance in Measurable Reality Dimension Labs solves this gap with a purpose-built observability layer for LLM-powered agents, analyzing conversations not just for tone or outcome, but for truthfulness, completeness, and customer understanding . By treating every customer interaction as a structured dialogue, Dimension Labs applies multi-layer enrichment, extracting: - Customer intent - Agent accuracy and relevance - Resolution completeness - Signals of hallucination or confusion This allows businesses to not only identify where and why LLMs fail, but to track performance trends over time, flag emerging failure modes, and validate the underlying knowledge base. In effect, Dimension Labs gives AI leaders the ability to audit, improve, and trust the systems they deploy. ### Why Traditional Tools Are Inadequate AI observability today is largely built for engineers, not customer experience leaders. Existing tools track: - Prompt execution success - Latency and token usage - API error rates But they do not tell you: - If the AI’s answer was correct - If the customer was misunderstood - If the agent hallucinated with confidence - If the response was compliant with policy or regulatory rules Sentiment and survey scores offer lagging signals. Manual review is too slow and inconsistent. And conventional dashboards can’t parse the nuance of human language interactions. Dimension Labs is the first platform that closes this gap, giving organizations the ability to inspect, quantify, and improve the quality of AI-generated conversations. ### The Impact For businesses deploying LLM-based customer agents, Dimension Labs provides a critical control layer that enables: - Real-time detection of hallucinations and knowledge drift - Compliance monitoring across high-volume conversations - Continuous improvement of knowledge base and prompt logic - De-risking of AI deployments without slowing innovation Organizations using Dimension Labs to audit their AI agent workflows report: - 2–4x faster issue identification and root-cause resolution - Improved trust across legal, compliance, and executive teams - Stronger customer satisfaction driven by more accurate, relevant AI guidance In the age of AI-native support, trust isn’t earned by how human your agent sounds, it’s earned by how reliably it delivers the right answer. Dimension Labs ensures you can measure that. ## Use Case: Contact Center Analytics Rethinking Contact Center Intelligence: From Manual QA to Scalable Customer Understanding ### The Challenge: Contact Centers Operate in the Dark, Despite Capturing the Most Customer Data Customer support teams speak with more customers than any other department. Every call, chat, or email contains firsthand feedback about what’s broken, what’s working, and what’s missing. But most of this data is trapped in raw transcripts or poorly structured CRM systems, unread, unanalyzed, and ultimately unused. Traditional contact center analytics focus on narrow metrics: handle time, CSAT, first call resolution. These lagging indicators tell teams how they performed, but not why. Meanwhile, most companies review just 1–2% of interactions through manual QA processes, leaving them blind to systemic friction and rising customer frustration. Even post-call surveys fail to deliver clarity. They capture sentiment, not context, and reflect the views of a vocal minority. The result is a reactive, fragmented understanding of customer experience that fails to guide continuous improvement. ### The Solution: Scalable, Multidimensional Analysis of Every Customer Conversation Dimension Labs transforms raw support interactions into structured, actionable intelligence. Using its proprietary text dimensionality framework , the platform analyzes every message exchanged between customers and agents, enriching it with predictive labels for: - Effort, satisfaction, and resolution - Customer intent and sentiment - Agent helpfulness, accuracy, and empathy - Emerging themes and actionable recommendations Unlike traditional QA or survey tools, Dimension Labs doesn’t sample or summarize. It reads every interaction and maps the meaning, at scale. It reveals patterns across teams, products, or channels and isolates both micro-friction and macro-level failure points. Most importantly, it delivers stakeholder-ready insights: call drivers for operations, training flags for QA, and churn predictors for CX leadership, all from the same dataset. ### Why Legacy Tools Can’t Compete The majority of contact center tools are designed to monitor operations, not understand customers. They track how long a call lasted, not what the call was about. Post-call surveys provide sentiment scores without context. QA sampling offers limited visibility, often influenced by reviewer subjectivity and incomplete coverage. Meanwhile, business intelligence dashboards require teams to pre-define issue categories, missing the nuance of how customers actually describe their problems. Dimension Labs is built to fill these gaps. It delivers: - Full-transcript enrichment across every support channel - Multi-layer analysis of both agent and customer behavior - Real-time issue discovery through unsupervised clustering and open-ended prompts - Stakeholder-specific insights ready for operations, training, product, and CX teams This isn’t just a reporting solution, it’s a foundational capability for any organization looking to connect customer interactions to business outcomes. ### The Impact Auto insurers and other high-volume service organizations using Dimension Labs report: - 60–80% reduction in time to identify systemic CX issues - Higher NPS and CSAT driven by proactive operational fixes - Improved agent performance through targeted feedback and coaching - Faster alignment between support operations and digital product teams In a market where every call is a cost and every resolution is an opportunity, Dimension Labs turns contact center conversations into a competitive advantage. ## Conclusion: Making the Invisible Actionable For decades, unstructured customer data has been one of the most underutilized assets in the enterprise. Thousands of conversations, comments, complaints, and questions flow through organizations every day. Each conversation is rich with signal, yet historically inaccessible at scale. The complexity of this data, combined with the limitations of legacy tools, has left companies operating with partial visibility, reactive insights, and missed opportunities. That era is over. Dimension Labs unlocks the full value of unstructured customer data through something fundamentally new: Customer Observability . Unlike legacy platforms that deliver flat dashboards or rely on narrow survey metrics, Dimension Labs creates a living, multi-dimensional system of insight, personalized for every stakeholder. Whether you’re a CX leader reducing churn, a product manager exploring user pain points, or a compliance team monitoring AI agent performance, you get a version of the truth that’s tuned to your goals. At the core of this transformation is our breakthrough approach: Text Dimensionality . Text Dimensionality is how we convert messy, unstructured inputs (chats, calls, survey responses, reviews) into structured, stakeholder-specific insight. It’s what powers the platform to: - Consolidate feedback from all channels into a unified source of truth - Generate tailored labels, summaries, and scores for different teams - Uncover root causes and emerging trends through LLM-powered enrichment and clustering - Deliver next steps, not just static dashboards This isn’t just about visualizing data, it’s about turning language into leverage. With Dimension Labs, every interaction becomes a high-resolution lens into your customer experience. The path forward isn’t more data. It’s more meaning. It’s time to make the invisible actionable, with Customer Observability and the power of Text Dimensionality. ## More from blog Aug 28, 2025 The Missing Layer in the Modern Data Stack: Unstructured Data By 2025, more than 80% of enterprise data will be unstructured. Yet if you look at the modern data stack it’s almost entirely designed for structured data. Apr 24, 2025 How LLM Analytics Improve Accuracy & Performance As large language models (LLMs) become embedded in apps across customer service, product support, and digital experiences, one question still remains unanswered: how do you know your AI is performing well? Oct 24, 2025 Beyond Sentiment and Categories: A New Era for Customer Insights For decades, when companies thought about “text analytics,” two words came to mind: sentiment and categories. Was the customer happy or upset? Which bucket does their feedback fall into, “billing issue,” “product complaint,” “shipping delay Dimension Labs / Start with one file From $299 a month ## Stop reporting. Start understanding. Start with one file. Ask why a number moved. Plans from $299 a month, no demo and no contract on monthly plans. Start now › See pricing › ---