product-analytics

# Product Analytics

Define, track, and interpret product metrics across discovery, growth, and mature product stages.

## When To Use

Use this skill for:
- Metric framework selection (AARRR, North Star, HEART)
- KPI definition by product stage (pre-PMF, growth, mature)
- Dashboard design and metric hierarchy
- Cohort and retention analysis
- Feature adoption and funnel interpretation

## Workflow

1. Select metric framework
- AARRR for growth loops and funnel visibility
- North Star for cross-functional strategic alignment
- HEART for UX quality and user experience measurement

2. Define stage-appropriate KPIs
- Pre-PMF: activation, early retention, qualitative success
- Growth: acquisition efficiency, expansion, conversion velocity
- Mature: retention depth, revenue quality, operational efficiency

3. Design dashboard layers
- Executive layer: 5-7 directional metrics
- Product health layer: acquisition, activation, retention, engagement
- Feature layer: adoption, depth, repeat usage, outcome correlation

4. Run cohort + retention analysis
- Segment by signup cohort or feature exposure cohort
- Compare retention curves, not single-point snapshots
- Identify inflection points around onboarding and first value moment

5. Interpret and act
- Connect metric movement to product changes and release timeline
- Distinguish signal from noise using period-over-period context
- Propose one clear product action per major metric risk/opportunity

## KPI Guidance By Stage

### Pre-PMF
- Activation rate
- Week-1 retention
- Time-to-first-value
- Problem-solution fit interview score

### Growth
- Funnel conversion by stage
- Monthly retained users
- Feature adoption among new cohorts
- Expansion / upsell proxy metrics

### Mature
- Net revenue retention aligned product metrics
- Power-user share and depth of use
- Churn risk indicators by segment
- Reliability and support-deflection product metrics

## Dashboard Design Principles

- Show trends, not isolated point estimates.
- Ke...