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. - 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 adjusting | Use period-over-period with same-period-last-year context | ## Tooling ### `scripts/metrics_calculator.py` CLI utility for retention, cohort, and funnel analysis from CSV data. Supports text and JSON output. ```bash # Retention analysis python3 scripts/metrics_calculator.py retention events.csv python3 scripts/metrics_calculator.py retention events.csv --format json # Cohort matrix python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain week --format json # Funnel conversion python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay --format json ``` **CSV format for retention/cohort:** ```csv user_id,cohort_date,activity_date u001,2026-01-01,2026-01-01 u001,2026-01-01,2026-01-03 u002,2026-01-02,2026-01-02 ``` **CSV format for funnel:** ```csv user_id,stage u001,visit u001,signup u001,activate u002,visit u002,signup ``` ## Cross-References - Related: `product-team/experiment-designer` — for A/B test planning after identifying metric opportunities - Related: `product-team/product-manager-toolkit` — for RICE prioritization of metric-driven features - Related: `product-team/product-discovery` — for assumption mapping when metrics reveal unknowns - Related: `finance/saas-metrics-coach` — for SaaS-specific metrics (ARR, MRR, churn, LTV)