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Cohort Analysis

Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.

By phuryn
25k2.6kUpdated 1 month agoMIT

Skill Content

# Cohort Analysis & Retention Explorer

## Purpose
Analyze user engagement and retention patterns by cohort to identify trends in user behavior, feature adoption, and long-term engagement. Combine quantitative insights with qualitative research recommendations.

## How It Works

### Step 1: Read and Validate Your Data
- Accept CSV, Excel, or JSON data files with user cohort information
- Verify data structure: cohort identifier, time periods, engagement metrics
- Check for missing values and data quality issues
- Summarize key statistics (cohort sizes, date ranges, metrics available)

### Step 2: Generate Quantitative Analysis
- Calculate cohort retention rates and engagement trends
- Identify retention curves, drop-off patterns, and anomalies
- Compute feature adoption rates across cohorts
- Calculate month-over-month or period-over-period changes
- Generate Python analysis scripts using pandas and numpy if requested

### Step 3: Create Visualizations
- Generate retention heatmaps (cohorts vs. time periods)
- Create line charts showing cohort progression
- Build comparison charts for feature adoption
- Visualize drop-off points and engagement trends
- Output as interactive charts or static images

### Step 4: Identify Insights & Patterns
- Spot one or more significant patterns:
  - Early churn in specific cohorts
  - Late-stage engagement changes
  - Feature adoption clusters
  - Seasonal or temporal trends
- Highlight surprising findings and deviations
- Compare cohort performance to establish baselines

### Step 5: Suggest Follow-Up Research
- Recommend qualitative research methods:
  - Targeted user interviews with churning users
  - Feature usage surveys with engaged cohorts
  - Session replays of key interaction patterns
  - Win/loss analysis for high vs. low retention cohorts
- Design follow-up quantitative studies
- Suggest A/B tests or feature experiments

## Usage Examples

**Example 1: Upload CSV Data**
```
Upload cohort_engagement.csv with columns: cohort_month, weeks_active,
user_id, feature_x_usage, engagement_score

Request: "Analyze retention patterns and identify why Q4 2025 cohorts
underperform compared to Q3"
```

**Example 2: Describe Data Format**
```
"I have monthly user cohorts from Jan-Dec 2025. Each row shows:
cohort date, user ID, purchase frequency, and support tickets.
Analyze which cohorts show best long-term retention."
```

**Example 3: Feature Adoption Analysis**
```
Upload feature_usage.xlsx with cohort adoption data.

Request: "Compare adoption curves for our new feature across cohorts.
Which cohorts adopted fastest? Any patterns?"
```

## Key Capabilities

- **Data Reading**: Import CSV, Excel, JSON, SQL query results
- **Retention Analysis**: Calculate and visualize retention rates over time
- **Cohort Comparison**: Compare metrics across cohort groups
- **Anomaly Detection**: Flag unusual patterns or drop-offs
- **Python Scripts**: Generate reusable analysis code for ongoing analysis
- **Visualizations**: Create heatmaps, charts, and interactive dashboards
- **Research Design**: Suggest targeted follow-up studies and interview approaches
- **Statistical Summary**: Provide quantitative metrics and correlation analysis

## Tips for Best Results

1. **Include time dimension**: Provide data across multiple time periods
2. **Define cohort clearly**: Make cohort grouping explicit (signup month, feature launch date, etc.)
3. **Provide context**: Explain product changes, launches, or events during the period
4. **Multiple metrics**: Include retention, engagement, feature usage, revenue, etc.
5. **Sufficient data**: At least 3-4 cohorts for meaningful pattern identification
6. **Request specific output**: Ask for visualizations, Python scripts, or research recommendations

## Output Format

You'll receive:
- **Data Summary**: Cohort overview and data quality assessment
- **Quantitative Findings**: Key metrics, retention rates, and trend analysis
- **Visualizations**: Charts showing retention curves, adoption patterns
- **Pattern Identification**: 2-3 significant insights from the data
- **Research Recommendations**: Specific qualitative and quantitative follow-ups
- **Analysis Scripts** (if requested): Python code for reproducible analysis
- **Next Steps**: Prioritized actions based on findings

---

### Further Reading

- [Cohort Analysis 101: How to Reduce Churn and Make Better Product Decisions](https://www.productcompass.pm/p/cohort-analysis)
- [The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs](https://www.productcompass.pm/p/the-product-analytics-playbook-aarrr)
- [Are You Tracking the Right Metrics?](https://www.productcompass.pm/p/are-you-tracking-the-right-metrics)

How to use

  1. Copy the skill content above
  2. Create a .claude/skills directory in your project
  3. Save as .claude/skills/pm-skills-cohort-analysis.md
  4. Use /pm-skills-cohort-analysis in Claude Code to invoke this skill

GitHub stars License: MIT PRs Welcome Tests Companion: pm-skills Companion: burnstop Companion: claude-usage

PM Skills Marketplace: The AI Operating System for Better Product Decisions

68 PM skills and 42 chained workflows across 9 plugins. Claude Code, Cowork, and more. From discovery to strategy, execution, launch, growth, and shipping AI-built code.

PM Skills marketplace: skills, commands, and all 9 plugins at a glance

Designed for Claude Code and Cowork. Skills compatible with other AI assistants.

Start Here

New idea? → /discover
Need strategic clarity? → /strategy
Writing a PRD? → /write-prd
Planning a launch? → /plan-launch
Defining metrics? → /north-star

If this project helps you, ⭐ the repo.

Why PM Skills Marketplace?

Generic AI gives you text. PM Skills Marketplace gives you structure.

Each skill encodes a proven PM framework — discovery, assumption mapping, prioritization, strategy — and walks you through it step by step. You get the rigor of Teresa Torres, Marty Cagan, and Alberto Savoia built into your daily workflow, not sitting on a bookshelf.

The result: better product decisions, not just faster documents.

How It Works (Skills, Commands, Plugins)

Example prompts: a skill and two commands (/write-prd, /ship-check) in action

Skills are the building blocks of the marketplace. Each skill gives Claude domain knowledge, analytical frameworks, or a guided workflow for a specific PM task. Some skills also work as reusable foundations that multiple commands share.

Skills are loaded automatically when relevant to the conversation — no explicit invocation needed. If needed (e.g., prioritizing skills over general knowledge), you can force loading skills with /plugin-name:skill-name or /skill-name (Claude will add the prefix).

Commands are user-triggered workflows invoked with /command-name. They chain one or more skills into an end-to-end process. For example, /discover chains four skills together: brainstorm-ideas → identify-assumptions → prioritize-assumptions → brainstorm-experiments.

Plugins group related skills and commands into installable packages. Each plugin covers a PM domain — discovery, strategy, execution, and so on. Installing the marketplace gives you all 9 plugins at once.

Commands use skills. Some skills serve multiple commands. Some skills (like prioritization-frameworks or opportunity-solution-tree) are standalone references that Claude draws on whenever relevant — no command needed.

Commands are designed to flow into each other, matching the PM workflow. After any command completes, it suggests relevant next commands — just follow the prompts.

Installation

Claude Cowork (recommended for non-developers)

  1. Open Customize (bottom-left)
  2. Go to Browse pluginsPersonal+
  3. Select Add marketplace from GitHub
  4. Enter: phuryn/pm-skills

All 9 plugins install automatically. You get both commands (/discover, /strategy, etc.) and skills.

Installing PM Skills in Claude Cowork

Claude Code (CLI)

# Step 1: Add the marketplace
claude plugin marketplace add phuryn/pm-skills

# Step 2: Install individual plugins
claude plugin install pm-toolkit@pm-skills
claude plugin install pm-product-strategy@pm-skills
claude plugin install pm-product-discovery@pm-skills 
claude plugin install pm-market-research@pm-skills 
claude plugin install pm-data-analytics@pm-skills
claude plugin install pm-marketing-growth@pm-skills
claude plugin install pm-go-to-market@pm-skills
claude plugin install pm-execution@pm-skills
claude plugin install pm-ai-shipping@pm-skills

Codex CLI (OpenAI)

Codex reads the same plugin marketplace file as Claude Code, so you can install PM Skills natively — no conversion or file-copying needed:

# Step 1: Add the marketplace
codex plugin marketplace add phuryn/pm-skills

# Step 2: Install the plugins you want
codex plugin add pm-toolkit@pm-skills
codex plugin add pm-product-strategy@pm-skills
codex plugin add pm-product-discovery@pm-skills
codex plugin add pm-market-research@pm-skills
codex plugin add pm-data-analytics@pm-skills
codex plugin add pm-marketing-growth@pm-skills
codex plugin add pm-go-to-market@pm-skills
codex plugin add pm-execution@pm-skills
codex plugin add pm-ai-shipping@pm-skills

What you get: every skill (the PM frameworks), available to Codex and invocable by name. Install whole plugins rather than cherry-picking individual skills — a workflow usually relies on several skills that ship together.

What's different from Claude Code: the /slash commands (/discover, /write-prd, …) install but don't run as Codex slash commands — Codex plugins don't expose commands. To run a workflow, just describe the steps in plain language, for example:

Run product discovery on [your idea]: brainstorm options, map assumptions, prioritize the risky ones, then design experiments — pause between each step.

Optional — let Codex turn the workflows into skills. Because the command files ship inside each installed plugin, you can ask Codex to convert the ones you use most:

Read the command files in the pm-execution plugin and create equivalent Codex skills for the workflows I use most often.

This is a best-effort, model-driven conversion (some Claude-specific command syntax won't translate), but it's a quick way to get the guided workflows on Codex without leaving the CLI.

Other AI assistants (skills only)

The skills/*/SKILL.md files follow the universal skill format and work with any tool that reads it. Commands (/slash-commands) are Claude-specific.

ToolHow to useWhat works
Gemini CLICopy skill folders to .gemini/skills/Skills only
OpenCodeCopy skill folders to .opencode/skills/Skills only
CursorCopy skill folders to .cursor/skills/Skills only
KiroCopy skill folders to .kiro/skills/Skills only
# Example: copy all skills for OpenCode (project-level)
for plugin in pm-*/; do
  mkdir -p .opencode/skills/
  cp -r "$plugin/skills/"* .opencode/skills/ 2>/dev/null
done

# Example: copy all skills for Gemini CLI (global)
for plugin in pm-*/; do
  cp -r "$plugin/skills/"* ~/.gemini/skills/ 2>/dev/null
done

Available Plugins

<details> <summary><strong>1. pm-product-discovery</strong> — Ideation, experiments, assumption testing, OSTs, interviews (13 skills, 5 commands)</summary>

Skills (13):

  • brainstorm-ideas-existing — Multi-perspective ideation for existing products (PM, Designer, Engineer)
  • brainstorm-ideas-new — Ideation for new products in initial discovery
  • brainstorm-experiments-existing — Design experiments to test assumptions for existing products
  • brainstorm-experiments-new — Design lean startup pretotypes for new products (Alberto Savoia)
  • identify-assumptions-existing — Identify risky assumptions across Value, Usability, Viability, and Feasibility
  • identify-assumptions-new — Identify risky assumptions across 8 risk categories including Go-to-Market, Strategy, and Team
  • prioritize-assumptions — Prioritize assumptions using an Impact × Risk matrix with experiment suggestions
  • prioritize-features — Prioritize a feature backlog based on impact, effort, risk, and strategic alignment
  • analyze-feature-requests — Analyze and categorize customer feature requests by theme and strategic fit
  • opportunity-solution-tree — Build an Opportunity Solution Tree (Teresa Torres) — outcome → opportunities → solutions → experiments
  • interview-script — Create a structured customer interview script with JTBD probing questions
  • summarize-interview — Summarize an interview transcript into JTBD, satisfaction signals, and action items
  • metrics-dashboard — Design a product metrics dashboard with North Star, input metrics, and alert thresholds

Commands (5):

  • /discover — Full discovery cycle: ideation → assumption mapping → prioritization → experiment design
  • /brainstorm — Multi-perspective ideation (ideas|experiments × existing|new)
  • /triage-requests — Analyze and prioritize a batch of feature requests
  • /interview — Prepare an interview script or summarize a transcript (prep|summarize)
  • /setup-metrics — Design a product metrics dashboard

Examples:

Skills:

  • What are the riskiest assumptions for our AI writing assistant idea?
  • Help me build an Opportunity Solution Tree for improving user activation
  • Prioritize these 12 feature requests from our enterprise customers [attach CSV]

Commands:

  • /discover AI-powered meeting summarizer for remote teams
  • /brainstorm experiments existing — We need to reduce churn in our onboarding flow
  • /interview prep — We're interviewing enterprise buyers about their procurement workflow
</details> <details> <summary><strong>2. pm-product-strategy</strong> — Vision, business models, pricing, competitive landscape (12 skills, 5 commands)</summary>

Product strategy, vision, business models, pricing, and macro environment analysis. Covers the full strategic toolkit from vision crafting through competitive landscape scanning.

Skills (12):

  • product-strategy — Comprehensive 9-section Product Strategy Canvas (vision → defensibility)
  • startup-canvas — Startup Canvas combining Product Strategy (9 sections) + Business Model — an alternative to BMC and Lean Canvas for new products
  • product-vision — Craft an inspiring, achievable, and emotional product vision
  • value-proposition — 6-part JTBD value proposition (Who, Why, What before, How, What after, Alternatives)
  • lean-canvas — Lean Canvas business model for startups and new products
  • business-model — Business Model Canvas with all 9 building blocks
  • monetization-strategy — Brainstorm 3–5 monetization strategies with validation experiments
  • pricing-strategy — Pricing models, competitive analysis, willingness-to-pay, and price elasticity
  • swot-analysis — SWOT analysis with actionable recommendations
  • pestle-analysis — Macro environment: Political, Economic, Social, Technological, Legal, Environmental
  • porters-five-forces — Competitive forces analysis (rivalry, suppliers, buyers, substitutes, new entrants)
  • ansoff-matrix — Growth strategy mapping across markets and products

Commands (5):

  • /strategy — Create a complete 9-section Product Strategy Canvas
  • /business-model — Explore business models (lean|full|startup|value-prop|all)
  • /value-proposition — Design a value proposition using the 6-part JTBD template
  • /market-scan — Macro environment analysis combining SWOT + PESTLE + Porter's + Ansoff
  • /pricing — Design a pricing strategy with competitive analysis and experiments

Examples:

Skills:

  • Compare Lean Canvas vs Business Model Canvas vs Startup Canvas for my marketplace startup
  • Design a value proposition for our AI writing assistant targeting non-native English speakers
  • Run a Porter's Five Forces analysis for the project management SaaS market

Commands:

  • `/strategy B2B project manage

View source on GitHub