Dimension Labs

Industry

Policyholder experience analytics for insurance

Read every call, claim note and complaint. Find which experience actually drives lapse and complaints, joined to policy and claims data.

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Ask Which experience drives policy lapse?

We work with enterprises such as NBA, Travelers, Mars and Concentrix.

[01] The problem

Policyholders tell you they're shopping before the renewal date.

Premium increases, slow claims and unexplained decisions show up in calls and complaints months before a policy lapses. Speech analytics flags keywords. It doesn't measure which one costs you the policy.

Right now

01Lapse models score risk without a reason
02Claims experience is reviewed one file at a time
03Complaint themes are counted, not measured
04Premium change conversations aren't analyzed at all
05Chatbot and agent channels are measured separately

The reason a policyholder leaves is usually in the last three conversations.

[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 insurance.

enrichment · one record

Raw text

My premium went up 22% with no claim on the policy and nobody explained why. I've already got a quote from another carrier, so tell me why I should stay.

Call transcript · auto policy · policy joined

Dimensions written to the record

Fee & Premium Change+22% · no claim
Dollar Amount Referenced22%
Trust & Honesty Signallow · no explanation
Competitor Mentionedcompeting quote
Churn & Cancellation Riskhigh
Objectionprice, needs a reason to stay

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 call transcripts, claim notes, complaints and chat logs, or connect every channel. Every record comes back with typed Dimensions like fee and premium change, trust and honesty signal, competitor mentioned, resolution status and regulatory exposure, written the same way every run, then joined to policy, premium, claims and tenure data.

Read every record, not a sample
Join each record to policy, premium, claims and tenure 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 lapse, complaint and claims satisfaction, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal.

Which experience drives policy lapse
Measure the effect of premium change conversations
Find claims experiences that create complaints
Flag regulatory exposure across every channel

dimension labs · ask

Example finding

Which experience drives policy lapse?

See the Rhino causal brief: 26 Dimensions across public Trustpilot and BBB reviews.

Published artifact · public reviews

3.3 · Deliver

Ask for the report. Get the report.

Ask for the readout your retention and claims 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