Product analytics

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.

Anti-Patterns

Anti-pattern	Fix
Vanity metrics — tracking pageviews or total signups without activation context	Always pair acquisition metrics with activation rate and retention
Single-point retention — reporting "30-day retention is 20%"	Compare retention curves across cohorts, not isolated snapshots
Dashboard overload — 30+ metrics on one screen	Executive layer: 5-7 metrics. Feature layer: per-feature only
No decision rule — tracking a KPI with no threshold or action plan	Every KPI needs: target, threshold, owner, and "if below X, then Y"
Averaging across segments — reporting blended metrics that hide segment differences	Always segment by cohort, plan tier, channel, or geography
Ignoring seasonality — comparing this week to last week without adjusting	Use period-over-period with same-period-last-year context

Tooling

scripts/metrics_calculator.py

CLI utility for retention, cohort, and funnel analysis from CSV data. Supports text and JSON output.

# Retention analysis
python3 scripts/metrics_calculator.py retention events.csv
python3 scripts/metrics_calculator.py retention events.csv --format json

# Cohort matrix
python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month
python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain week --format json

# Funnel conversion
python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay
python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay --format json

CSV format for retention/cohort:

user_id,cohort_date,activity_date
u001,2026-01-01,2026-01-01
u001,2026-01-01,2026-01-03
u002,2026-01-02,2026-01-02

CSV format for funnel:

user_id,stage
u001,visit
u001,signup
u001,activate
u002,visit
u002,signup

Cross-References

• Related: product-team/experiment-designer — for A/B test planning after identifying metric opportunities
• Related: product-team/product-manager-toolkit — for RICE prioritization of metric-driven features
• Related: product-team/product-discovery — for assumption mapping when metrics reveal unknowns
• Related: finance/saas-metrics-coach — for SaaS-specific metrics (ARR, MRR, churn, LTV)