Product Analytics

What This Skill Does

• The Product Analytics skill helps define, track, and interpret product metrics across different product stages.
• It assists in selecting metric frameworks, defining KPIs, designing dashboards, and conducting cohort analysis.
• This skill is useful for product managers, analysts, and data scientists aiming to improve product performance through data-driven insights.

When to Use

• Selecting metric frameworks (AARRR, North Star, HEART).
• Defining KPIs for pre-PMF, growth, and mature stages.
• Designing dashboard layers for different audiences.
• Running cohort and retention analysis.
• Interpreting metric movements and proposing actions.
• Tracking feature adoption and funnel performance.

Key Features

• Provides KPI guidance by product stage.
• Offers dashboard design principles.
• Supports AARRR, North Star, and HEART frameworks.
• Helps identify churn risk indicators.
• Analyzes feature adoption among new cohorts.
• Connects metric movement to product changes.

Manual Installation

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.

Tooling

scripts/metrics_calculator.py

CLI utility for: Retention rate calculations by cohort age Cohort table generation Basic funnel conversion analysis

Examples: