108 lines
3.5 KiB
Markdown
108 lines
3.5 KiB
Markdown
---
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name: product-analytics
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description: Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
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---
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# Product Analytics
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Define, track, and interpret product metrics across discovery, growth, and mature product stages.
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## When To Use
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Use this skill for:
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- Metric framework selection (AARRR, North Star, HEART)
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- KPI definition by product stage (pre-PMF, growth, mature)
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- Dashboard design and metric hierarchy
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- Cohort and retention analysis
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- Feature adoption and funnel interpretation
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## Workflow
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1. Select metric framework
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- AARRR for growth loops and funnel visibility
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- North Star for cross-functional strategic alignment
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- HEART for UX quality and user experience measurement
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2. Define stage-appropriate KPIs
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- Pre-PMF: activation, early retention, qualitative success
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- Growth: acquisition efficiency, expansion, conversion velocity
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- Mature: retention depth, revenue quality, operational efficiency
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3. Design dashboard layers
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- Executive layer: 5-7 directional metrics
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- Product health layer: acquisition, activation, retention, engagement
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- Feature layer: adoption, depth, repeat usage, outcome correlation
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4. Run cohort + retention analysis
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- Segment by signup cohort or feature exposure cohort
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- Compare retention curves, not single-point snapshots
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- Identify inflection points around onboarding and first value moment
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5. Interpret and act
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- Connect metric movement to product changes and release timeline
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- Distinguish signal from noise using period-over-period context
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- Propose one clear product action per major metric risk/opportunity
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## KPI Guidance By Stage
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### Pre-PMF
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- Activation rate
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- Week-1 retention
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- Time-to-first-value
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- Problem-solution fit interview score
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### Growth
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- Funnel conversion by stage
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- Monthly retained users
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- Feature adoption among new cohorts
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- Expansion / upsell proxy metrics
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### Mature
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- Net revenue retention aligned product metrics
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- Power-user share and depth of use
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- Churn risk indicators by segment
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- Reliability and support-deflection product metrics
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## Dashboard Design Principles
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- Show trends, not isolated point estimates.
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- Keep one owner per KPI.
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- Pair each KPI with target, threshold, and decision rule.
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- Use cohort and segment filters by default.
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- Prefer comparable time windows (weekly vs weekly, monthly vs monthly).
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See:
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- `references/metrics-frameworks.md`
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- `references/dashboard-templates.md`
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## Cohort Analysis Method
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1. Define cohort anchor event (signup, activation, first purchase).
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2. Define retained behavior (active day, key action, repeat session).
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3. Build retention matrix by cohort week/month and age period.
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4. Compare curve shape across cohorts.
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5. Flag early drop points and investigate journey friction.
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## Retention Curve Interpretation
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- Sharp early drop, low plateau: onboarding mismatch or weak initial value.
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- Moderate drop, stable plateau: healthy core audience with predictable churn.
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- Flattening at low level: product used occasionally, revisit value metric.
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- Improving newer cohorts: onboarding or positioning improvements are working.
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## Tooling
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### `scripts/metrics_calculator.py`
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CLI utility for:
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- Retention rate calculations by cohort age
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- Cohort table generation
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- Basic funnel conversion analysis
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Examples:
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```bash
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python3 scripts/metrics_calculator.py retention events.csv
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python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month
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python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay
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```
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