Product Analytics
FreeDefine and track essential product metrics effectively.
Free · Opens the source repo
What Product Analytics does
The Product Analytics skill provides a structured approach to defining, tracking, and interpreting product metrics at various stages of a product's lifecycle. It is particularly useful for product managers and analysts who need to establish key performance indicators (KPIs) that align with their product's current phase—whether in discovery, growth, or maturity. The skill supports the selection of appropriate metric frameworks, such as AARRR, North Star, and HEART, allowing users to choose the best fit based on their specific needs.
With this skill, users can define KPIs tailored to each product stage. For instance, during the pre-product-market fit (PMF) phase, the focus is on metrics like activation rates and early retention, while growth stages emphasize acquisition efficiency and conversion rates. Mature products require a different set of metrics, such as net revenue retention and churn risk indicators. This adaptability ensures that users can monitor the right metrics at the right time.
The skill also includes guidance on designing effective dashboards that present data in a meaningful way. Users can create layers that highlight executive metrics, product health metrics, and detailed feature performance metrics. This layered approach helps stakeholders quickly grasp the product's status and make informed decisions based on clear, actionable insights. Additionally, the skill facilitates cohort and retention analysis, allowing users to segment data effectively and identify trends over time.
Finally, the Product Analytics skill emphasizes the importance of interpreting metrics in context. By connecting metric movements to product changes and release timelines, users can distinguish between significant trends and noise, leading to more informed product decisions. Overall, this skill is essential for anyone involved in product management or analytics who seeks to enhance their understanding of product performance and drive data-informed strategies.
When to use it
Use this skill when you need to define product KPIs, create metric dashboards, or analyze user retention and feature adoption.
When not to use it
This skill may not be suitable for projects that require real-time data processing or complex machine learning analytics.
What you can build with it
Defining KPIs for a New Product
When launching a new product, use this skill to establish relevant KPIs that focus on activation and early retention metrics.
Analyzing User Retention Trends
Utilize the cohort analysis capabilities to track user retention over time and identify areas for improvement in user onboarding.
Creating Executive Dashboards
Design dashboards that highlight key metrics for stakeholders, ensuring clarity and focus on the most critical product performance indicators.
How to install Product Analytics
View source1. Install with the skills CLI
npx skills add alirezarezvani/claude-skills/product-analytics --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by alirezarezvaniProduct 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)
Frequently asked questions about Product Analytics
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