product-analytics

---
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.
category: Product
version: 1.0.0
tools: []
---

# Product Analytics

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

## Core Workflow

1. **Metric Framework Selection**
   - AARRR (Acquisition, Activation, Retention, Revenue, Referral) for growth loops
   - North Star for cross-functional alignment
   - HEART (Happiness, Engagement, Adoption, Retention, Task Success) for UX quality

2. **Stage-Appropriate KPIs**
   - Pre-PMF: activation, week-1 retention, time-to-first-value, problem-solution fit
   - Growth: funnel conversion, monthly retained users, feature adoption, expansion proxies
   - Mature: net revenue retention, power-user share, churn risk by segment, support deflection

3. **Dashboard Design** — Executive layer (5-7 directional metrics); Health layer (acquisition, activation, retention, engagement); Feature layer (adoption, depth, repeat usage, outcome correlation).

4. **Cohort + Retention Analysis**
   - Segment by anchor event (signup, activation, first purchase)
   - Compare retention curves across cohorts (not isolated snapshots)
   - Identify early drop points as friction signals

5. **Retention Curve Interpretation**
   - Sharp early drop + low plateau → onboarding mismatch
   - Moderate drop + stable plateau → healthy core with predictable churn
   - Flattening at low level → occasional use; revisit value metric
   - Improving newer cohorts → onboarding/positioning working

6. **Anti-Pattern Remediation**
   - Vanity metrics: pair acquisition with activation + retention
   - Single-point retention: compare curves, not isolated snapshots
   - Dashboard overload: exec layer (5-7), feature layer only per-feature
   - No decision rule: every KPI needs target, threshold, owner, action
   - Averaged segments: always segment by cohort, tier, channel, geography
   - Seasonality blindness: use period-over-period with same-period-last-year context

## Technical Standards

- Attribution: full window (30-90 days B2B, not same-session)
- Cost data: platform APIs, never manual entry
- Retention: compare curves, not point snapshots
- Cohort metrics: segment by signup month and behavior
- Dashboard refresh: daily for operational metrics, weekly for trends
- Every KPI: target, threshold, owner, and if/then decision rule