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.

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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

01Churn scores flag accounts, never explain them
02The reason they left is in a transcript nobody queries
03Revenue at risk cannot be traced to a specific failure
04Retention programs get built on assumption
05Save plays run without evidence that they work

Your warehouse has the numbers. Your conversations have the reason. Nothing you own joins them.

[02] EnrichOne 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.

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 Mentionedyes · evaluation underway
Churn & Cancellation Riskhigh
Lifecycle Stagerenewal
Urgency & Deadline60 days
Expectation Gaproadmap promise missed
Feature RequestSSO
Resolution Statusopen · 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 worksRuns 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

ZendeskIntercomSalesforceLiveChatDriftSlackQualtricsMedalliaSurveyMonkeyQuestionProGenesysAmazon ConnectAvaya Cloud8x8TwilioSMSAdaKore.aiYellow.aiCognigyRasaMicrosoft Bot FrameworkAmazon LexAmazon AlexaOracle Digital AssistantBotcopyCapacityFacebook MessengerKikX (Twitter)OpenAIEmailCall recordingsApp store reviewsGoogle reviewsOpen-ended surveysCommunity forums

Business + world data

SnowflakeDatabricksBigQueryAmazon S3Salesforce CRMCSV + ExcelREST APIRevenueOrdersRenewalsUsage eventsLoyaltyStore + location dataWeatherSeasonalityMarket events

Dimension Labs pipeline · Runs daily

Ingest + normalizeEvery channel and language into one schema

EnrichDozens of Dimensions written onto every record

Map + governValidated for drift, versioned, the same fields every run

Analyze + proveJoined to outcomes and tested for cause

Where it goes

Dimension Labs agentAsk, report, schedule

Your warehouseSnowflake, Databricks, BigQuery

BI toolsTableau, Power BI, Hex

Claude and ChatGPTvia MCP · coming soon

[05] ArtifactsBuilt by the agent