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.
Tooling
scripts/metrics_calculator.py
CLI utility for:
- Retention rate calculations by cohort age
- Cohort table generation
- Basic funnel conversion analysis
Examples:
python3 scripts/metrics_calculator.py retention events.csv
python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month
python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay
1---2name: product-analytics3description: Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.4---5
6# Product Analytics
7
8Define, track, and interpret product metrics across discovery, growth, and mature product stages.
9
10## When To Use
11
12Use 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 hierarchy
16- Cohort and retention analysis
17- Feature adoption and funnel interpretation
18
19## Workflow
20
211. Select metric framework
22- AARRR for growth loops and funnel visibility
23- North Star for cross-functional strategic alignment
24- HEART for UX quality and user experience measurement
25
262. Define stage-appropriate KPIs
27- Pre-PMF: activation, early retention, qualitative success
28- Growth: acquisition efficiency, expansion, conversion velocity
29- Mature: retention depth, revenue quality, operational efficiency
30
313. Design dashboard layers
32- Executive layer: 5-7 directional metrics
33- Product health layer: acquisition, activation, retention, engagement
34- Feature layer: adoption, depth, repeat usage, outcome correlation
35
364. Run cohort + retention analysis
37- Segment by signup cohort or feature exposure cohort
38- Compare retention curves, not single-point snapshots
39- Identify inflection points around onboarding and first value moment
40
415. Interpret and act
42- Connect metric movement to product changes and release timeline
43- Distinguish signal from noise using period-over-period context
44- Propose one clear product action per major metric risk/opportunity
45
46## KPI Guidance By Stage
47
48### Pre-PMF
49- Activation rate
50- Week-1 retention
51- Time-to-first-value
52- Problem-solution fit interview score
53
54### Growth
55- Funnel conversion by stage
56- Monthly retained users
57- Feature adoption among new cohorts
58- Expansion / upsell proxy metrics
59
60### Mature
61- Net revenue retention aligned product metrics
62- Power-user share and depth of use
63- Churn risk indicators by segment
64- Reliability and support-deflection product metrics
65
66## Dashboard Design Principles
67
68- 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).
73
74See:
75- `references/metrics-frameworks.md`
76- `references/dashboard-templates.md`
77
78## Cohort Analysis Method
79
801. 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.
85
86## Retention Curve Interpretation
87
88- 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.
92
93## Tooling
94
95### `scripts/metrics_calculator.py`
96
97CLI utility for:
98- Retention rate calculations by cohort age
99- Cohort table generation
100- Basic funnel conversion analysis
101
102Examples:
103```bash
104python3 scripts/metrics_calculator.py retention events.csv
105python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month
106python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay
107```
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