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MetricWhy

Get hypotheses, not hunches.
When your metrics change, we surface the most likely explanations from your meeting decisions—instantly.
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Click on any metric drop to see AI-generated causal hypotheses
First Pass Yield
Click to analyze the drop on Nov 22
Revenue
Click to analyze the drop on Nov 15

Analysis Results

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How It Works: Real Meeting Data Across Departments

MetricWhy analyzes decisions from all your meetings—production, sales, marketing, customer success—to surface what actually drives your metrics.

Example: Recent Meetings Analyzed 47 meetings from Nov 2025 containing 156 decisions
📊
Sales Leadership Weekly – Nov 1

Key decisions: 3 major deals ($890K) pushed to Q1 due to budget freezes. Created $299 "Essentials" tier after SMB churn citing "too expensive."

📈
Marketing Strategy Sync – Nov 5

Key decisions: Competitor launched at $299/mo (vs our $399). Lead volume down 22%. Paused underperforming campaigns, redirected 40% budget to content.

🏭
Production Ops Review – Nov 8

Key decisions: Implemented manual MES confirmation step. Tagged temperature probe for review. Adjusted automation setup time from 5 to 7 minutes.

💬
Customer Success Review – Oct 25

Key decisions: Lost 12 accounts in Q3 ($340K impact). NPS dropped from 42 to 31. Revamping onboarding to show value faster.

The Power of Cross-Functional Analysis: When revenue drops on Nov 15, 2025, MetricWhy extracts structured decisions from meeting text, maps temporal relationships between decisions and metric changes, and applies causal reasoning (incorporating future Bayesian Network models) to generate ranked hypotheses—showing not just correlation, but plausible causal mechanisms across departments.

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Surface the most likely explanations for your metric changes—instantly.