Product Metrics Reviewer
Product metrics review with trend analysis, anomaly detection, and scorecard generation
Your product dashboard has 40 metrics and you check the same 5 every morning. You missed the activation rate dropping 15% two weeks ago because it was buried in a tab you never open. Metrics reviews should surface what changed and what matters — not make you hunt.
Who it's for: product managers doing weekly or monthly metrics reviews, growth PMs monitoring acquisition and activation funnels, product leaders preparing data for exec reviews, data-informed PMs building metric accountability into their process, product teams establishing KPI tracking and review cadences
Example
"Review our product metrics for the past month" → Scorecard with 12 key metrics showing trend direction, 3 anomalies flagged (activation rate -15%, support tickets +40%, session duration -8%), root cause hypotheses for each anomaly, and recommended next actions
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
npx ccplaybooks add product-metrics-reviewer# Product Metrics Reviewer
Review and analyze product metrics, identify trends, and surface actionable insights.
## Workflow
### 1. Gather Metrics Data
Ask the user to provide:
- The metrics and their values (paste a table, screenshot, or describe)
- Comparison data (previous period, targets)
- Any context on recent changes (launches, incidents, seasonality)Get new playbooks like this one
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What This Does
Reviews and analyzes product metrics using a structured hierarchy — North Star metric, L1 health indicators (acquisition, activation, engagement, retention, revenue, satisfaction), and L2 diagnostic metrics. Produces scorecards, trend analysis, bright spots, areas of concern, and recommended actions.
Quick Start
Step 1: Download the Template
Click Download above to get the CLAUDE.md file.
Step 2: Set Up Your Project
Create a project folder and place the template inside:
product-metrics/
├── CLAUDE.md
├── data/ # Metrics exports (CSV, spreadsheets)
└── reviews/ # Generated metric reviews
Step 3: Start Working
claude
Say: "Review our product metrics for last month — here's the data..."
Metrics Hierarchy
| Level | What It Covers |
|---|---|
| North Star | Single metric capturing core value delivered to users |
| L1 Health | Acquisition, activation, engagement, retention, monetization, satisfaction |
| L2 Diagnostic | Funnel conversions, feature adoption, segment breakdowns, performance |
Review Cadences
- Weekly (15-30 min) — North Star, key L1 movements, experiment results, anomalies
- Monthly (30-60 min) — Full L1 scorecard, OKR progress, cohort analysis, feature adoption
- Quarterly (60-90 min) — OKR scoring, trend analysis, YoY comparisons, retrospective
Tips
- Always show comparisons — absolute numbers without context are useless
- Segment analysis often reveals that an aggregate metric masks important differences
- If a metric misses its target, recommend what to do about it, not just report the miss
- Focus attention on meaningful changes — small fluctuations are noise
Example Prompts
"Review our product metrics for last month"
"Why is our activation rate dropping? Here's the data..."
"Create a weekly metrics scorecard for our team"
"Which metrics should be our North Star?"