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.

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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

01OEM surveys stop at the point of sale
02Delivery and title issues surface as reviews
03Rooftops are compared by star average
04Service and sales feedback live in different systems
05Online and in-store buyers are measured differently

Every buyer leaves a detailed account of the process. Most of it is public.

[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 automotive.

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 Typecheck engine · at delivery
Expectation Gapcondition on delivery
Urgency & Deadlineregistration · 60 days
Effort Levelhigh · no single owner
Recommendation Signaldetractor
Predicted NPS Banddetractor

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 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