Product Analytics

在定义产品 KPI、构建指标仪表板、运行群组或保留分析或解释跨产品阶段的功能采用趋势时使用。

## 概述（中文）

在定义产品 KPI、构建指标仪表板、运行群组或保留分析或解释跨产品阶段的功能采用趋势时使用。

## 原文

# 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.

## Tooling

### `scripts/metrics_calculator.py`

CLI utility for:
- Retention rate calculations by cohort age
- Cohort table generation
- Basic funnel conversion analysis

Examples:
```bash
python3 scripts/metrics_calculator.py retention events.csv
python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month
python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay
```