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

01Health scores flag accounts without a reason
02Feature requests are counted, not weighted by revenue
03Support and success data never meet
04Roadmap debates are settled by the loudest account
05Churn 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] 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 software and saas.

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 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 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