product-analytics

Solid

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

AI & Automation 16,642 stars 2295 forks Updated yesterday MIT

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Skill Content

# 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 - ...

Details

Author
alirezarezvani
Repository
alirezarezvani/claude-skills
Created
7 months ago
Last Updated
yesterday
Language
Python
License
MIT

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