Dimension Labs

Industry

Lead conversation analytics for real estate

Read every lead text, call and chat. Find which signals predict a showing and a closing, and which follow-up patterns lose buyers.

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Ask Which signals predict a closing?

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

[01] The problem

Lead scores rank by clicks. The buying signal is in the conversation.

Real estate platforms handle millions of lead conversations. Pre-approval, timing and agent switching are all said plainly, and almost never captured as data.

Right now

01Lead scores are built on web activity
02Buying signals in texts aren't captured
03Agent follow-up quality isn't measured
04Lost leads have no recorded reason
05Nurture sequences aren't tested against outcomes

Buyers tell you their budget, timeline and frustrations in the first three messages.

[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 real estate.

enrichment · one record

Raw text

We're pre-approved up to 650 and need to be in before school starts in August. Is the one on Maple still available? Our current agent stopped responding.

SMS thread · inbound lead · CRM joined

Dimensions written to the record

Buying Signalpre-approved
Dollar Amount Referenced$650K
Urgency & Deadlinebefore August
Customer Intentshowing request
Incumbent / Current Setuphas an agent
Competitive Contextswitch opportunity

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 lead texts, calls, chats and emails, or connect every channel. Every record comes back with typed Dimensions like buying signal, urgency and deadline, customer intent, incumbent setup and dollar amount referenced, written the same way every run, then joined to lead source, agent, showing and closing data.

Read every record, not a sample
Join each record to lead source, agent, showing and closing 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 showings and closings, with an effect size and a confidence interval. Findings that fail the robustness checks are never reported as causal.

Which signals predict a closing
Find the follow-up patterns that lose buyers
Rank leads by what they said, not what they clicked
Measure which nurture sequences work

dimension labs · ask

Example finding

Which signals predict a closing?

Buying signals ranked by their effect on showings and closings, with the conversations behind each one.

Every lead conversation · CRM joined

3.3 · Deliver

Ask for the report. Get the report.

Ask for the readout your brokerage and platform 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