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
- 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.mdreferences/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.
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)
1---2name: product-analytics3description: Define, track, and interpret product metrics across discovery, growth, and mature product stages using frameworks like AARRR, North Star, and HEART.4---56# Product Analytics78Define, track, and interpret product metrics across discovery, growth, and mature product stages.910## When To Use1112Use this skill for:13- Metric framework selection (AARRR, North Star, HEART)14- KPI definition by product stage (pre-PMF, growth, mature)15- Dashboard design and metric hierarchy16- Cohort and retention analysis17- Feature adoption and funnel interpretation1819## Workflow20211. Select metric framework22- AARRR for growth loops and funnel visibility23- North Star for cross-functional strategic alignment24- HEART for UX quality and user experience measurement25262. Define stage-appropriate KPIs27- Pre-PMF: activation, early retention, qualitative success28- Growth: acquisition efficiency, expansion, conversion velocity29- Mature: retention depth, revenue quality, operational efficiency30313. Design dashboard layers32- Executive layer: 5-7 directional metrics33- Product health layer: acquisition, activation, retention, engagement34- Feature layer: adoption, depth, repeat usage, outcome correlation35364. Run cohort + retention analysis37- Segment by signup cohort or feature exposure cohort38- Compare retention curves, not single-point snapshots39- Identify inflection points around onboarding and first value moment40415. Interpret and act42- Connect metric movement to product changes and release timeline43- Distinguish signal from noise using period-over-period context44- Propose one clear product action per major metric risk/opportunity4546## KPI Guidance By Stage4748### Pre-PMF49- Activation rate50- Week-1 retention51- Time-to-first-value52- Problem-solution fit interview score5354### Growth55- Funnel conversion by stage56- Monthly retained users57- Feature adoption among new cohorts58- Expansion / upsell proxy metrics5960### Mature61- Net revenue retention aligned product metrics62- Power-user share and depth of use63- Churn risk indicators by segment64- Reliability and support-deflection product metrics6566## Dashboard Design Principles6768- Show trends, not isolated point estimates.69- Keep one owner per KPI.70- Pair each KPI with target, threshold, and decision rule.71- Use cohort and segment filters by default.72- Prefer comparable time windows (weekly vs weekly, monthly vs monthly).7374See:75- `references/metrics-frameworks.md`76- `references/dashboard-templates.md`7778## Cohort Analysis Method79801. Define cohort anchor event (signup, activation, first purchase).812. Define retained behavior (active day, key action, repeat session).823. Build retention matrix by cohort week/month and age period.834. Compare curve shape across cohorts.845. Flag early drop points and investigate journey friction.8586## Retention Curve Interpretation8788- Sharp early drop, low plateau: onboarding mismatch or weak initial value.89- Moderate drop, stable plateau: healthy core audience with predictable churn.90- Flattening at low level: product used occasionally, revisit value metric.91- Improving newer cohorts: onboarding or positioning improvements are working.9293## Anti-Patterns9495| Anti-pattern | Fix |96|---|---|97| **Vanity metrics** — tracking pageviews or total signups without activation context | Always pair acquisition metrics with activation rate and retention |98| **Single-point retention** — reporting "30-day retention is 20%" | Compare retention curves across cohorts, not isolated snapshots |99| **Dashboard overload** — 30+ metrics on one screen | Executive layer: 5-7 metrics. Feature layer: per-feature only |100| **No decision rule** — tracking a KPI with no threshold or action plan | Every KPI needs: target, threshold, owner, and "if below X, then Y" |101| **Averaging across segments** — reporting blended metrics that hide segment differences | Always segment by cohort, plan tier, channel, or geography |102| **Ignoring seasonality** — comparing this week to last week without adjusting | Use period-over-period with same-period-last-year context |103104## Tooling105106### `scripts/metrics_calculator.py`107108CLI utility for retention, cohort, and funnel analysis from CSV data. Supports text and JSON output.109110```bash111# Retention analysis112python3 scripts/metrics_calculator.py retention events.csv113python3 scripts/metrics_calculator.py retention events.csv --format json114115# Cohort matrix116python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month117python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain week --format json118119# Funnel conversion120python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay121python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay --format json122```123124**CSV format for retention/cohort:**125```csv126user_id,cohort_date,activity_date127u001,2026-01-01,2026-01-01128u001,2026-01-01,2026-01-03129u002,2026-01-02,2026-01-02130```131132**CSV format for funnel:**133```csv134user_id,stage135u001,visit136u001,signup137u001,activate138u002,visit139u002,signup140```141142## Cross-References143144- Related: `product-team/experiment-designer` — for A/B test planning after identifying metric opportunities145- Related: `product-team/product-manager-toolkit` — for RICE prioritization of metric-driven features146- Related: `product-team/product-discovery` — for assumption mapping when metrics reveal unknowns147- Related: `finance/saas-metrics-coach` — for SaaS-specific metrics (ARR, MRR, churn, LTV)
Alireza Rezvani