| name | metrics-review |
| description | Review product metrics against prior periods and targets, diagnose meaningful changes, and recommend actions. Use for weekly, monthly, or quarterly reviews and investigations of spikes, drops, funnels, cohorts, or experiments. |
Metrics Review
Turn numbers into product decisions while preserving data and causal uncertainty.
Workflow
- Confirm each metric's definition, population, period, comparison, target, source, and known data-quality issues.
- Organize metrics as a North Star, product-health indicators, and diagnostic metrics. Use acquisition, activation, engagement, retention, monetization, and satisfaction only where relevant.
- Calculate absolute and relative changes correctly. Inspect trends, rate of change, anomalies, cohorts, and segments rather than relying on one aggregate snapshot.
- Connect changes to launches, incidents, campaigns, seasonality, or experiments, but label correlation and hypotheses separately from demonstrated causation.
- Prioritize a small set of investigations, experiments, investments, or alerts. Give each action an owner or next decision when context allows.
Output
## Metrics Review
### Summary
Overall health, most important change, and main caveat.
### Scorecard
| Metric | Current | Previous | Change | Target | Status |
|---|---:|---:|---:|---:|---|
### Findings
Material trends, segments, anomalies, and confidence.
### Bright Spots and Concerns
What to sustain and what needs attention.
### Hypotheses
Possible explanations, evidence for and against, and validation needed.
### Actions
Investigation, experiment, investment, or alert; expected decision unlocked.
### Data Caveats
Definition changes, missing data, bias, and comparability limits.
Quality Gates
- Never invent missing targets, baselines, or causal explanations; mark them
TBD.
- Avoid vanity metrics without a link to user value or a decision.
- Show denominators for rates and sample sizes when available.
- Keep the action list smaller than the finding list.
