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.

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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

01The roadmap gets argued from anecdote
02Requests get counted, never weighted by what they cost
03Bug reports and churn data sit in systems that never meet
04Shipping a fix proves nothing

Prioritization is a causal problem. Every tool you own treats it as a ranking one.

[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 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 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