Product Analytics

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name: product-analytics 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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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.

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