product-analytics

# 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)

{
  "format": "aiskill/v1",
  "skill": {
    "name": "product-analytics",
    "slug": "product-analytics",
    "version": "1.0.0",
    "description": "Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.",
    "category": "Business",
    "tags": [
      "product-business",
      "data-viz"
    ],
    "platforms": [
      "chatgpt",
      "claude-code",
      "cursor"
    ],
    "tested_environment": "Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and compatible agents",
    "license": "MIT"
  },
  "prompts": {
    "system": "# Product Analytics\r\n\r\nDefine, track, and interpret product metrics across discovery, growth, and mature product stages.\r\n\r\n## When To Use\r\n\r\nUse this skill for:\r\n- Metric framework selection (AARRR, North Star, HEART)\r\n- KPI definition by product stage (pre-PMF, growth, mature)\r\n- Dashboard design and metric hierarchy\r\n- Cohort and retention analysis\r\n- Feature adoption and funnel interpretation\r\n\r\n## Workflow\r\n\r\n1. Select metric framework\r\n- AARRR for growth loops and funnel visibility\r\n- North Star for cross-functional strategic alignment\r\n- HEART for UX quality and user experience measurement\r\n\r\n2. Define stage-appropriate KPIs\r\n- Pre-PMF: activation, early retention, qualitative success\r\n- Growth: acquisition efficiency, expansion, conversion velocity\r\n- Mature: retention depth, revenue quality, operational efficiency\r\n\r\n3. Design dashboard layers\r\n- Executive layer: 5-7 directional metrics\r\n- Product health layer: acquisition, activation, retention, engagement\r\n- Feature layer: adoption, depth, repeat usage, outcome correlation\r\n\r\n4. Run cohort + retention analysis\r\n- Segment by signup cohort or feature exposure cohort\r\n- Compare retention curves, not single-point snapshots\r\n- Identify inflection points around onboarding and first value moment\r\n\r\n5. Interpret and act\r\n- Connect metric movement to product changes and release timeline\r\n- Distinguish signal from noise using period-over-period context\r\n- Propose one clear product action per major metric risk/opportunity\r\n\r\n## KPI Guidance By Stage\r\n\r\n### Pre-PMF\r\n- Activation rate\r\n- Week-1 retention\r\n- Time-to-first-value\r\n- Problem-solution fit interview score\r\n\r\n### Growth\r\n- Funnel conversion by stage\r\n- Monthly retained users\r\n- Feature adoption among new cohorts\r\n- Expansion / upsell proxy metrics\r\n\r\n### Mature\r\n- Net revenue retention aligned product metrics\r\n- Power-user share and depth of use\r\n- Churn risk indicators by segment\r\n- Reliability and support-deflection product metrics\r\n\r\n## Dashboard Design Principles\r\n\r\n- Show trends, not isolated point estimates.\r\n- Keep one owner per KPI.\r\n- Pair each KPI with target, threshold, and decision rule.\r\n- Use cohort and segment filters by default.\r\n- Prefer comparable time windows (weekly vs weekly, monthly vs monthly).\r\n\r\nSee:\r\n- `references/metrics-frameworks.md`\r\n- `references/dashboard-templates.md`\r\n\r\n## Cohort Analysis Method\r\n\r\n1. Define cohort anchor event (signup, activation, first purchase).\r\n2. Define retained behavior (active day, key action, repeat session).\r\n3. Build retention matrix by cohort week/month and age period.\r\n4. Compare curve shape across cohorts.\r\n5. Flag early drop points and investigate journey friction.\r\n\r\n## Retention Curve Interpretation\r\n\r\n- Sharp early drop, low plateau: onboarding mismatch or weak initial value.\r\n- Moderate drop, stable plateau: healthy core audience with predictable churn.\r\n- Flattening at low level: product used occasionally, revisit value metric.\r\n- Improving newer cohorts: onboarding or positioning improvements are working.\r\n\r\n## Anti-Patterns\r\n\r\n| Anti-pattern | Fix |\r\n|---|---|\r\n| **Vanity metrics** — tracking pageviews or total signups without activation context | Always pair acquisition metrics with activation rate and retention |\r\n| **Single-point retention** — reporting \"30-day retention is 20%\" | Compare retention curves across cohorts, not isolated snapshots |\r\n| **Dashboard overload** — 30+ metrics on one screen | Executive layer: 5-7 metrics. Feature layer: per-feature only |\r\n| **No decision rule** — tracking a KPI with no threshold or action plan | Every KPI needs: target, threshold, owner, and \"if below X, then Y\" |\r\n| **Averaging across segments** — reporting blended metrics that hide segment differences | Always segment by cohort, plan tier, channel, or geography |\r\n| **Ignoring seasonality** — comparing this week to last week without adjusting | Use period-over-period with same-period-last-year context |\r\n\r\n## Tooling\r\n\r\n### `scripts/metrics_calculator.py`\r\n\r\nCLI utility for retention, cohort, and funnel analysis from CSV data. Supports text and JSON output.\r\n\r\n```bash\r\n# Retention analysis\r\npython3 scripts/metrics_calculator.py retention events.csv\r\npython3 scripts/metrics_calculator.py retention events.csv --format json\r\n\r\n# Cohort matrix\r\npython3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month\r\npython3 scripts/metrics_calculator.py cohort events.csv --cohort-grain week --format json\r\n\r\n# Funnel conversion\r\npython3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay\r\npython3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay --format json\r\n```\r\n\r\n**CSV format for retention/cohort:**\r\n```csv\r\nuser_id,cohort_date,activity_date\r\nu001,2026-01-01,2026-01-01\r\nu001,2026-01-01,2026-01-03\r\nu002,2026-01-02,2026-01-02\r\n```\r\n\r\n**CSV format for funnel:**\r\n```csv\r\nuser_id,stage\r\nu001,visit\r\nu001,signup\r\nu001,activate\r\nu002,visit\r\nu002,signup\r\n```\r\n\r\n## Cross-References\r\n\r\n- Related: `product-team/experiment-designer` — for A/B test planning after identifying metric opportunities\r\n- Related: `product-team/product-manager-toolkit` — for RICE prioritization of metric-driven features\r\n- Related: `product-team/product-discovery` — for assumption mapping when metrics reveal unknowns\r\n- Related: `finance/saas-metrics-coach` — for SaaS-specific metrics (ARR, MRR, churn, LTV)",
    "user_template": ""
  },
  "variables": [],
  "install": {
    "steps": [
      {
        "title": "Download & unzip",
        "detail": "Download the .zip package from SkillVault and unzip it. The product-analytics/ folder contains SKILL.md plus any companion scripts, references and assets."
      },
      {
        "title": "Copy into your agent skills directory",
        "detail": "Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session."
      },
      {
        "title": "Use it",
        "detail": "Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description."
      }
    ]
  },
  "usage": {
    "steps": []
  },
  "metadata": {
    "author": "SkillVault",
    "source": "https://skillvault.top/skills/product-analytics",
    "exported_at": "2026-11-03T07:42:09.282Z"
  }
}

format: aiskill/v1
skill:
  name: product-analytics
  slug: product-analytics
  version: 1.0.0
  description: >-
    Use when defining product KPIs, building metric dashboards, running cohort or retention
    analysis, or interpreting feature adoption trends across product stages.
  category: Business
  tags:
    - product-business
    - data-viz
  platforms:
    - chatgpt
    - claude-code
    - cursor
  tested_environment: >-
    Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and
    compatible agents
  license: MIT
prompts:
  system: "# Product Analytics\r\n\r\nDefine, track, and interpret product metrics across discovery, growth, and mature product stages.\r\n\r\n## When To Use\r\n\r\nUse this skill for:\r\n- Metric framework selection (AARRR, North Star, HEART)\r\n- KPI definition by product stage (pre-PMF, growth, mature)\r\n- Dashboard design and metric hierarchy\r\n- Cohort and retention analysis\r\n- Feature adoption and funnel interpretation\r\n\r\n## Workflow\r\n\r\n1. Select metric framework\r\n- AARRR for growth loops and funnel visibility\r\n- North Star for cross-functional strategic alignment\r\n- HEART for UX quality and user experience measurement\r\n\r\n2. Define stage-appropriate KPIs\r\n- Pre-PMF: activation, early retention, qualitative success\r\n- Growth: acquisition efficiency, expansion, conversion velocity\r\n- Mature: retention depth, revenue quality, operational efficiency\r\n\r\n3. Design dashboard layers\r\n- Executive layer: 5-7 directional metrics\r\n- Product health layer: acquisition, activation, retention, engagement\r\n- Feature layer: adoption, depth, repeat usage, outcome correlation\r\n\r\n4. Run cohort + retention analysis\r\n- Segment by signup cohort or feature exposure cohort\r\n- Compare retention curves, not single-point snapshots\r\n- Identify inflection points around onboarding and first value moment\r\n\r\n5. Interpret and act\r\n- Connect metric movement to product changes and release timeline\r\n- Distinguish signal from noise using period-over-period context\r\n- Propose one clear product action per major metric risk/opportunity\r\n\r\n## KPI Guidance By Stage\r\n\r\n### Pre-PMF\r\n- Activation rate\r\n- Week-1 retention\r\n- Time-to-first-value\r\n- Problem-solution fit interview score\r\n\r\n### Growth\r\n- Funnel conversion by stage\r\n- Monthly retained users\r\n- Feature adoption among new cohorts\r\n- Expansion / upsell proxy metrics\r\n\r\n### Mature\r\n- Net revenue retention aligned product metrics\r\n- Power-user share and depth of use\r\n- Churn risk indicators by segment\r\n- Reliability and support-deflection product metrics\r\n\r\n## Dashboard Design Principles\r\n\r\n- Show trends, not isolated point estimates.\r\n- Keep one owner per KPI.\r\n- Pair each KPI with target, threshold, and decision rule.\r\n- Use cohort and segment filters by default.\r\n- Prefer comparable time windows (weekly vs weekly, monthly vs monthly).\r\n\r\nSee:\r\n- `references/metrics-frameworks.md`\r\n- `references/dashboard-templates.md`\r\n\r\n## Cohort Analysis Method\r\n\r\n1. Define cohort anchor event (signup, activation, first purchase).\r\n2. Define retained behavior (active day, key action, repeat session).\r\n3. Build retention matrix by cohort week/month and age period.\r\n4. Compare curve shape across cohorts.\r\n5. Flag early drop points and investigate journey friction.\r\n\r\n## Retention Curve Interpretation\r\n\r\n- Sharp early drop, low plateau: onboarding mismatch or weak initial value.\r\n- Moderate drop, stable plateau: healthy core audience with predictable churn.\r\n- Flattening at low level: product used occasionally, revisit value metric.\r\n- Improving newer cohorts: onboarding or positioning improvements are working.\r\n\r\n## Anti-Patterns\r\n\r\n| Anti-pattern | Fix |\r\n|---|---|\r\n| **Vanity metrics** — tracking pageviews or total signups without activation context | Always pair acquisition metrics with activation rate and retention |\r\n| **Single-point retention** — reporting \"30-day retention is 20%\" | Compare retention curves across cohorts, not isolated snapshots |\r\n| **Dashboard overload** — 30+ metrics on one screen | Executive layer: 5-7 metrics. Feature layer: per-feature only |\r\n| **No decision rule** — tracking a KPI with no threshold or action plan | Every KPI needs: target, threshold, owner, and \"if below X, then Y\" |\r\n| **Averaging across segments** — reporting blended metrics that hide segment differences | Always segment by cohort, plan tier, channel, or geography |\r\n| **Ignoring seasonality** — comparing this week to last week without adjusting | Use period-over-period with same-period-last-year context |\r\n\r\n## Tooling\r\n\r\n### `scripts/metrics_calculator.py`\r\n\r\nCLI utility for retention, cohort, and funnel analysis from CSV data. Supports text and JSON output.\r\n\r\n```bash\r\n# Retention analysis\r\npython3 scripts/metrics_calculator.py retention events.csv\r\npython3 scripts/metrics_calculator.py retention events.csv --format json\r\n\r\n# Cohort matrix\r\npython3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month\r\npython3 scripts/metrics_calculator.py cohort events.csv --cohort-grain week --format json\r\n\r\n# Funnel conversion\r\npython3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay\r\npython3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay --format json\r\n```\r\n\r\n**CSV format for retention/cohort:**\r\n```csv\r\nuser_id,cohort_date,activity_date\r\nu001,2026-01-01,2026-01-01\r\nu001,2026-01-01,2026-01-03\r\nu002,2026-01-02,2026-01-02\r\n```\r\n\r\n**CSV format for funnel:**\r\n```csv\r\nuser_id,stage\r\nu001,visit\r\nu001,signup\r\nu001,activate\r\nu002,visit\r\nu002,signup\r\n```\r\n\r\n## Cross-References\r\n\r\n- Related: `product-team/experiment-designer` — for A/B test planning after identifying metric opportunities\r\n- Related: `product-team/product-manager-toolkit` — for RICE prioritization of metric-driven features\r\n- Related: `product-team/product-discovery` — for assumption mapping when metrics reveal unknowns\r\n- Related: `finance/saas-metrics-coach` — for SaaS-specific metrics (ARR, MRR, churn, LTV)"
  user_template: ""
variables: []
install:
  steps:
    - title: Download & unzip
      detail: >-
        Download the .zip package from SkillVault and unzip it. The product-analytics/ folder
        contains SKILL.md plus any companion scripts, references and assets.
    - title: Copy into your agent skills directory
      detail: >-
        Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code),
        .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or
        reload the session.
    - title: Use it
      detail: >-
        Describe the task in natural language; the agent loads the skill automatically when the
        request matches the skill description.
usage:
  steps: []
metadata:
  author: SkillVault
  source: https://skillvault.top/skills/product-analytics
  exported_at: "2026-11-03T07:42:09.282Z"