Google BigQuery
Data warehouse
A number that only lives in a warehouse cannot inform a conversation happening on a call right now. Revenue Labs reads the tables a company builds in BigQuery from product, billing and revenue data, and joins them to the right account, so a rep works from one set of numbers.
Funnel Analysis·1 cohort traced, 1 test proposed this quarter
Reviewing this quarter’s funnel stage movement… ›
TriggerBigQuery warehouse refresh · stage, conversion and attribution events reconciled across the funnel
Deploy agentDeploying Funnel Analysis agent on this quarter’s stage movement
Use skillLoading skills · Attribution & Funnel Logic · Persona · Messaging
Reference memoryReading memory · Stage Movement · Wins & Losses · Decision Traces
ReasoningVP RevOps persona cohort converts well through engagement and meetings, then drops sharply at meeting-to-opportunity while every other persona holds - the evidence proposes a test at that stage
ActionCompose funnel diagnosis with affected cohort, cause and proposed test → surface in Marketing Weekly Review for RevOps and Leaders to validate
✓Traced in 6 seconds ›
Surfaces inMarketing Weekly ReviewviaSlackTeams
Revenue LabsAPP7:20 AM
LIVE
Marketing Weekly Review: Marketing Team - Week ending 16 Aug 2026
WHAT CHANGED
Stage movement: meeting-to-opportunity conversion held flat across the funnel this quarter, one persona cohort diverging
Win movement: opportunity-to-win rate unchanged this quarter, no funnel-wide cause found
EXCEPTIONS
FUNNEL ANALYSIS
VP RevOps persona cohort · converts well early, drops at meeting-to-opportunity
Proposed: stage-specific messaging test, this quarter
Open Weekly ReviewReview Upgrade QueueAsk Agent
The tool alone > AI bolted on > AI-native
What changes when Google BigQuery feeds a system that learns
Three jobs Google BigQuery already does, and what each becomes when its evidence joins live company, contact and deal state.
01 | The tool on its own
02 | With AI bolted on
03 | AI-native
Datasets and tables
Warehoused tables for product, revenue, and usage data.
BigQuery stores the tables a company builds from product and revenue data. A data owner still defines schemas, permissions, and joins.
Gemini helps find and explain data.
Gemini works from the datasets and schema it can access. Its answer stays in BigQuery and does not update the live account view.
Warehouse data becomes account state.
Relevant tables are associated with their company, contact, and deal without replacing the warehouse.
SQL queries
SQL turns large tables into a usable answer.
BigQuery lets teams query large datasets with SQL. An analyst still runs the query, checks the result, and decides whether it belongs in a commercial decision.
Gemini generates and explains SQL.
Query drafts remain a draft until an analyst checks and runs them. The draft does not carry the result into every capability or learn from the deal outcome.
A query result reaches the deal.
Usage change, revenue fact, or product event sits beside calls and stages where the next capability can use it.
Data canvas and analysis
Analysis can be queried, charted, and shared.
BigQuery data canvas helps teams find, query, and visualise data. An analyst still selects the tables and reviews the result.
Gemini creates charts and summaries.
Charts and summaries explain selected BigQuery data. They provide no company or deal join, and no test against renewal, expansion, or loss outcomes.
Warehouse signals follow outcomes.
Closed outcomes reveal which warehouse patterns preceded them. The system learns the useful pattern and supplies it to the next account decision.
Runs on it
Capabilities that act on connected evidence
Sales
Sales Analysis
Active
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The review opens with causes diagnosed.
Deals, decisions and outcomes are examined against sales process and qualification logic.
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The analysis pins rate changes to causes.
It links a shift to the stage, stakeholder gap or process change.
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Planning starts from what worked.
Each proposed process, qualification or coaching change has outcome evidence.
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RevOps sees which choices caused the shift.
Recorded deal decisions show which choices moved the result.
Humans validate causal interpretation and approve changes.
Sales
Forecasting
Active
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The forecast category stays current.
It maintains category, confidence and deal evidence between calls.
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The rep category is checked against evidence.
It names the gap between the submission and the deal evidence.
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The manager sees disagreements first.
Rep and system judgements open with their evidence and gap.
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The forecast explains movement and coverage.
Leaders see movement, gaps and roll-up implications with evidence.
Managers and leaders own final commitments and overrides.
BUILT FOR SECURITY, CONTROL AND SCALE
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The GTM teams that learn fastest will win.
Connect your stack to a system that learns.