# 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