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

1. Define stage-appropriate KPIs
• Pre-PMF: activation, early retention, qualitative success
• Growth: acquisition efficiency, expansion, conversion velocity
• Mature: retention depth, revenue quality, operational efficiency

1. Design dashboard layers
• Executive layer: 5-7 directional metrics
• Product health layer: acquisition, activation, retention, engagement
• Feature layer: adoption, depth, repeat usage, outcome correlation

1. 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

1. 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.

Tooling

scripts/metrics_calculator.py

CLI utility for:

• Retention rate calculations by cohort age
• Cohort table generation
• Basic funnel conversion analysis

Examples:

python3 scripts/metrics_calculator.py retention events.csv
python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month
python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay