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Zhongwei Li
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commands/analyze_user_behavior.md
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120
commands/analyze_user_behavior.md
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---
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description: Analyze user behavior patterns and cohorts using PostHog
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category: analytics
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tools: Task, TodoWrite
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model: inherit
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version: 1.0.0
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---
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# Analyze User Behavior
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Query PostHog to understand user behavior patterns, cohorts, and product usage.
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## Prerequisites
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- PostHog MCP must be enabled (this plugin should be enabled)
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- `POSTHOG_AUTH_HEADER` environment variable configured
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- Access to PostHog project
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## Usage
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```bash
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/analyze-user-behavior <query>
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Examples:
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/analyze-user-behavior "checkout abandonment last 30 days"
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/analyze-user-behavior "feature adoption for new dashboard"
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/analyze-user-behavior "user retention cohorts by signup month"
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```
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## What This Command Does
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Uses PostHog MCP tools to:
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1. Query user events and properties
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2. Analyze cohorts and segments
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3. Calculate conversion metrics
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4. Identify behavior patterns
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5. Generate insights with charts/data
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## Available PostHog Capabilities
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When this plugin is enabled, you have access to ~43 PostHog tools:
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**User Analysis**:
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- Query user properties and events
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- Segment users by behavior
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- Track user journeys
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- Analyze cohort retention
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**Product Metrics**:
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- Feature usage tracking
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- Conversion funnel analysis
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- A/B test results
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- Session replay analysis
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**Trends & Insights**:
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- Event trends over time
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- User engagement metrics
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- Feature adoption rates
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- Custom dashboard queries
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## Example Queries
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### Conversion Analysis
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```bash
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/analyze-user-behavior "Show conversion rate from signup to first purchase, broken down by traffic source"
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```
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### Feature Adoption
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```bash
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/analyze-user-behavior "How many users adopted the new search feature in the last week?"
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```
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### Retention Cohorts
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```bash
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/analyze-user-behavior "Show weekly retention for users who signed up in December 2024"
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```
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### User Journey
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```bash
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/analyze-user-behavior "What's the typical path users take before upgrading to paid plan?"
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```
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## Output Format
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The command will:
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1. Translate your natural language query to PostHog API calls
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2. Fetch relevant data
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3. Present findings with:
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- Key metrics and numbers
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- Trends and patterns
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- Visualizations (when possible)
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- Actionable insights
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## Tips
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- Be specific about time ranges ("last 30 days", "this quarter")
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- Mention specific events or features by name
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- Ask for comparisons ("vs last month", "broken down by...")
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- Request segmentation ("by country", "by plan type")
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## Context Cost
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**This plugin adds ~40,645 tokens** to your context window. Disable when not analyzing metrics:
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```bash
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/plugin disable catalyst-analytics
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```
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---
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**See also**: `/product-metrics`, `/segment-analysis`
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commands/product_metrics.md
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commands/product_metrics.md
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---
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description: View key product metrics, KPIs, and conversion rates from PostHog
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category: analytics
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tools: Task, TodoWrite
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model: inherit
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version: 1.0.0
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---
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# Product Metrics Dashboard
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Query PostHog for key product metrics, KPIs, and performance indicators.
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## Usage
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```bash
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/product-metrics [metric-type] [time-range]
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Examples:
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/product-metrics "overall KPIs last 30 days"
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/product-metrics "conversion rates this quarter"
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/product-metrics "feature usage breakdown this week"
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```
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## Available Metrics
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### Conversion Metrics
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- Signup conversion rate
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- Trial to paid conversion
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- Checkout completion rate
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- Feature activation rate
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### Engagement Metrics
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- Daily/Weekly/Monthly Active Users (DAU/WAU/MAU)
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- Session duration
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- Feature usage frequency
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- User retention rates
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### Business Metrics
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- Revenue per user
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- Customer acquisition cost
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- Lifetime value
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- Churn rate
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### Feature Metrics
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- Feature adoption rate
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- Time to first use
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- Feature retention
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- Power user identification
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## Example Queries
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### Overall Dashboard
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```bash
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/product-metrics "Show me our key metrics for last month: MAU, conversion rates, and top features"
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```
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### Conversion Funnel
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```bash
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/product-metrics "Breakdown of our signup to paid funnel with drop-off rates at each step"
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```
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### Feature Performance
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```bash
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/product-metrics "Compare usage of our top 5 features over the last quarter"
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```
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### Cohort Performance
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```bash
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/product-metrics "How do our December signups compare to November in terms of activation and retention?"
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```
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## Output Format
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Results typically include:
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- **Metric values** with trend indicators (↑↓)
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- **Comparisons** to previous periods
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- **Breakdowns** by segment when relevant
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- **Top performers** and bottom performers
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- **Recommendations** based on data
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## Time Range Options
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- `today`, `yesterday`
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- `last 7 days`, `last 30 days`, `last 90 days`
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- `this week`, `last week`
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- `this month`, `last month`, `this quarter`
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- Custom: `2024-01-01 to 2024-03-31`
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## Segmentation
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Add segmentation to any query:
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```bash
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/product-metrics "MAU by country"
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/product-metrics "conversion rates by traffic source"
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/product-metrics "feature usage by plan type"
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```
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## Context Management
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This plugin consumes ~40k tokens. Disable after viewing metrics:
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```bash
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/plugin disable catalyst-analytics
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```
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---
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**See also**: `/analyze-user-behavior`, `/segment-analysis`
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138
commands/segment_analysis.md
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138
commands/segment_analysis.md
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@@ -0,0 +1,138 @@
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---
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description: Analyze user segments and cohorts for targeted insights
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category: analytics
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tools: Task, TodoWrite
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model: inherit
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version: 1.0.0
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---
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# Segment Analysis
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Deep-dive into specific user segments, cohorts, or customer groups using PostHog data.
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## Usage
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```bash
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/segment-analysis <segment-description>
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Examples:
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/segment-analysis "users from paid plans vs free plans"
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/segment-analysis "power users who use feature X daily"
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/segment-analysis "users who churned in last 30 days"
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/segment-analysis "cohort: signed up in Q4 2024"
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```
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## What This Analyzes
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### User Segments
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- By plan type (free, pro, enterprise)
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- By geography (country, region)
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- By acquisition source (organic, paid, referral)
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- By behavior (power users, casual users, at-risk)
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### Cohort Analysis
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- By signup date (monthly, weekly cohorts)
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- By first feature used
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- By activation milestone reached
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- By engagement level
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### Comparison Analysis
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- Segment A vs Segment B
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- Before/after feature launch
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- Treatment vs control (A/B tests)
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- Time period comparisons
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## Example Analyses
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### Plan Comparison
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```bash
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/segment-analysis "Compare engagement patterns between free and paid users: session frequency, feature usage, retention"
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```
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### Power User Identification
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```bash
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/segment-analysis "Identify our power users: who are they, what features do they use, what's their profile?"
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```
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### Churn Analysis
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```bash
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/segment-analysis "Analyze users who churned: what were their last actions, which features didn't they use?"
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```
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### Geographic Performance
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```bash
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/segment-analysis "Compare conversion rates and engagement across our top 5 countries"
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```
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### Cohort Retention
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```bash
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/segment-analysis "Show retention curves for each monthly signup cohort in 2024"
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```
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## Output Format
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Analysis typically includes:
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- **Segment characteristics** (size, demographics, behavior)
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- **Key metrics** for each segment
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- **Comparative insights** between segments
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- **Behavior patterns** unique to segment
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- **Recommendations** for targeting or improvement
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## Segmentation Criteria
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You can segment by:
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- **Demographics**: Country, language, device type
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- **Behavior**: Feature usage, session frequency, engagement score
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- **Business**: Plan type, payment history, LTV
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- **Temporal**: Signup date, last active, tenure
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- **Custom**: Any event or property in PostHog
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## Advanced Analysis
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### Multi-dimensional Segmentation
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```bash
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/segment-analysis "Power users (5+ sessions/week) from enterprise plans who use feature X"
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```
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### Funnel by Segment
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```bash
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/segment-analysis "Compare signup to activation funnel for organic vs paid traffic"
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```
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### Retention by Segment
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```bash
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/segment-analysis "30-day retention by initial feature used"
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```
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## Tips for Better Analysis
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1. **Be specific** - Define your segment clearly
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2. **Ask for comparisons** - "vs" between segments reveals insights
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3. **Look for patterns** - What makes segments different?
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4. **Consider time** - Trends over time matter
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5. **Combine criteria** - Multi-dimensional segments can be revealing
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## Context Cost
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Plugin uses ~40k tokens. Disable when analysis is complete:
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```bash
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/plugin disable catalyst-analytics
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```
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---
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**See also**: `/analyze-user-behavior`, `/product-metrics`
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Reference in New Issue
Block a user