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Product Analytics

Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.

thedixitjain 87d1868 4 files · 17.2 KB Updated Sep 9, 2026 2 repo stars

Data & Analytics

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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

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.

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)

Source: alirezarezvani/claude-skills → product-team/skills/product-analytics/SKILL.md

---name: product-analyticsdescription: Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.--- # 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. ```bash # 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:** ```csv 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:** ```csv 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) --- **Source:** [`alirezarezvani/claude-skills`](https://github.com/alirezarezvani/claude-skills) → `product-team/skills/product-analytics/SKILL.md`

thedixitjain/the-mega-skill-library/tree/main/library/product-and-pm/product-analytics commit 87d1868b3d

Frequently asked questions

npx skillmds add thedixitjain/product-analytics