Product analytics

Skill OpenSIN-AI/OpenSIN-Skills/product-team/product-analytics

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

Install

npx -y skills add OpenSIN-AI/OpenSIN-Skills --skill product-analytics

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SKILL.md

5.4 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

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)

What ships with it: 3 files

11.5 KB alongside SKILL.md, 1 of them executable

references/

dashboard-templates.md2.0 KB
metrics-frameworks.md2.4 KB

scripts/

metrics_calculator.pyruns7.2 KB

Gives 0 of the 12 instructions most analytics metrics skills give in ~1.2k tokens

Counted across 333 of the 342 authors here whose files we hold, read 2026-09-06

Read product marketing context before asking questionsin 37 of 333, across 16 files
Test one variable at a timein 26 of 333, across 11 files
Pre-determine sample size before launchin 24 of 333, across 16 files
Verify tracking and QA variants before launchin 17 of 333, across 8 files
Monitor for technical issues during the testin 14 of 333, across 6 files
Match each save offer to the cancel reasonin 14 of 333, across 5 files
Start every test with a specific hypothesisin 14 of 333, across 7 files
Keep the continue-cancelling option visiblein 13 of 333, across 4 files
Document every test with hypothesis, variants, results, and learningsin 13 of 333, across 6 files
Gather churn, billing, product, usage, and constraint context firstin 12 of 333, across 3 files
Build a health score from weighted signalsin 12 of 333, across 3 files
Retry soft declines 3-5 times over 7-10 daysin 12 of 333, across 3 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.