product-analytics

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: product-analytics
description: Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
---

# 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.
- Keep one owner per KPI.
- Pair each KPI with target, threshold, and decision rule.
- Use cohort and segment filters by default.
- Prefer comparable time windows (weekly vs weekly, monthly vs monthly).

See:
- `references/metrics-frameworks.md`
- `references/dashboard-templates.md`

## Cohort Analysis Method

1. Define cohort anchor event (signup, activation, first purchase).
2. Define retained behavior (active day, key action, repeat session).
3. Build retention matrix by cohort week/month and age period.
4. Compare curve shape across cohorts.
5. Flag early drop points and investigate journey friction.

## Retention Curve Interpretation

- Sharp early drop, low plateau: onboarding mismatch or weak initial value.
- Moderate drop, stable plateau: healthy core audience with predictable churn.
- Flattening at low level: product used occasionally, revisit value metric.
- Improving newer cohorts: onboarding or positioning improvements are working.

## Anti-Patterns

| Anti-pattern | Fix |
|---|---|
| **Vanity metrics** — tracking pageviews or total signups without activation context | Always pair acquisition metrics with activation rate and retention |
| **Single-point retention** — reporting "30-day retention is 20%" | Compare retention curves across cohorts, not isolated snapshots |
| **Dashboard overload** — 30+ metrics on one screen | Executive layer: 5-7 metrics. Feature layer: per-feature only |
| **No decision rule** — tracking a KPI with no threshold or action plan | Every KPI needs: target, threshold, owner, and "if below X, then Y" |
| **Averaging across segments** — reporting blended metrics that hide segment differences | Always segment by cohort, plan tier, channel, or geography |
| **Ignoring seasonality** — comparing this week to last week without adjustin