product-analytics

CategoryData
AuthorAlireza Rezvani
LicenseMIT
Rating4.20/5
Uses13.3K

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)
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