Dimension Labs

Use case

Contact center and agent performance

Read every call, chat and ticket your agents handle. Find which behaviors and process failures move resolution, repeat contacts and CSAT.

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Ask Which agent behaviors actually move CSAT?

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

[01] The problem

QA scores two percent of calls. The other ninety-eight decide your CSAT.

Sampled QA tells you whether an agent followed the script. It can't tell you which part of the conversation made the customer call back.

Right now

01QA reviews a small sample by hand
02Scorecards grade script adherence, not outcomes
03Repeat contacts have no named cause
04Coaching is based on anecdotes
05Process failures get blamed on agents

Every conversation is already recorded. The answer is in there, unread.

[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

This is my fourth call about the same bill. The last agent said a credit would post in 48 hours and it never did. You're the first one who actually listened, thank you, can you note that on the account?

Call transcript · billing queue · agent joined

Dimensions written to the record

First-Time vs. Repeatrepeat · 4th contact
Billing Disputecredit not posted
Expectation Gappromised credit missing
Staff Interaction Qualitypositive · this agent
Named Employeecurrent agent
Resolution Statuspending · note added

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 calls, chats and tickets, or connect every channel. Every record comes back with typed Dimensions like effort, resolution status, repeat contact, staff interaction quality and escalation risk, written the same way every run, then joined to agent, queue, handle time and resolution data.

Read every record, not a sample
Join each record to agent, queue, handle time and resolution 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 resolution, repeat contact and CSAT, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal.

Which agent behaviors actually move CSAT
Find the process failures that cause repeat contacts
See what your top agents do that others don't
Score 100% of conversations, not a sample

dimension labs · ask

Example finding

Which agent behaviors actually move CSAT?

Coaching targets ranked by measured effect on repeat contact, with the calls behind each one.

Every call, chat and ticket · agent and queue joined

3.3 · Deliver

Ask for the report. Get the report.

Ask for the readout your operations lead 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