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 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 Define stage-appropriate KPIs Pre-PMF: activation, early retention, qualitative success Growth: acquisition efficiency, expansion, conversion velocity Mature: retention depth, revenue quality, operational efficiency Design dashboard layers Executive layer: 5-7 directional metrics Product health layer: acquisition, activation, retention, engagement Feature layer: adoption, depth, repeat usage, outcome correlation 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 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 Define cohort anchor event (signup, activation, first purchase). Define retained behavior (active day, key action, repeat session). Build retention matrix by cohort week/month and age period. Compare curve shape across cohorts. 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. # 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: 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: 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)
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