posthog-analytics

PostHog Analytics Expert

Transform PostHog data into actionable product insights. This skill combines product analytics expertise with the PostHog MCP server to help discover patterns, surface opportunities, and build a data-informed product strategy.

Product Context Management

Before diving into analysis, establish product context. Store discovered knowledge in .claude/product-context.md for persistence across sessions.

First Session: Discovery

1. Check for existing context: Read .claude/product-context.md if it exists
2. Interview the user (if context is missing or incomplete):
   • What does the product do? Who are the users?
   • What are the key user actions/conversions?
   • What business metrics matter most?
3. Explore PostHog data:
   • event-definitions-list - Discover tracked events
   • properties-list - Understand available properties
   • insights-get-all - See existing insights
   • dashboards-get-all - Review current dashboards
4. Save context: Write discovered knowledge to .claude/product-context.md

Context File Structure

# Product Context

## Product Overview
[What the product does, target users]

## Key Events
| Event | Meaning | Importance |
|-------|---------|------------|
| $pageview | Page visit | Navigation tracking |
| signup_completed | User registered | Core conversion |
| [custom events discovered] | | |

## Important Properties
- user_tier: free/pro/enterprise
- [other key properties]

## Key Metrics
- Primary: [e.g., Weekly Active Users, Conversion Rate]
- Secondary: [e.g., Feature Adoption, Retention]

## Funnels
- Activation: signup → onboarding_complete → first_value_action
- [other key funnels]

## Last Updated: [date]

Core Capabilities

1. Proactive Insight Discovery

When asked to "find insights" or "what's interesting", run this discovery workflow:

1. Trends Analysis
   - query-run: Total events over 30 days (spot volume changes)
   - query-run: DAU/WAU/MAU trends (engagement health)
   - query-run: Key conversion events over time

2. Funnel Health
   - query-run: Core activation funnel
   - query-run: Conversion funnel (trial → paid if SaaS)
   - Look for: Drop-off points, conversion changes

3. Retention Check
   - query-run: Cohort retention (week-over-week)
   - Look for: Retention curve shape, changes over time

4. Feature Adoption
   - query-run: Feature usage by user segment
   - Look for: Underused features, power user patterns

5. Error Impact
   - list-errors: Top errors by occurrence
   - error-details: Impact on user journeys

Insight Presentation Format:

## [Insight Title]
**Finding**: [One sentence summary]
**Evidence**: [Specific numbers/data]
**Impact**: [Why this matters]
**Recommended Action**: [What to do about it]

2. Answering Analytics Questions

Map common questions to PostHog queries:

Question Pattern	Approach
"How many users..."	query-run with TrendsQuery, math: "dau" or "total"
"What % convert..."	query-run with FunnelsQuery
"Where do users drop off..."	FunnelsQuery → analyze step-by-step conversion
"Which feature is most used..."	TrendsQuery with breakdown by feature/event
"How is X changing over time..."	TrendsQuery with interval: "day" or "week"
"Who are our power users..."	TrendsQuery with breakdown by user property
"What's causing errors..."	list-errors → error-details for top issues

3. Dashboard Creation

When building dashboards, follow this structure:

Executive Dashboard (high-level health):

• Active users (DAU/WAU/MAU)
• Core conversion rate
• Retention (week 1, week 4)
• Revenue metrics (if applicable)

Product Dashboard (feature-level):

• Feature adoption rates
• Feature engagement depth
• User journey completion
• Error rates by feature

Growth Dashboard (acquisition/activation):

• Signup funnel
• Activation funnel
• Traffic sources (if tracked)
• Onboarding completion

Workflow:

1. dashboard-create with descriptive name
2. Build insights with query-run → insight-create-from-query
3. Add to dashboard with add-insight-to-dashboard
4. Organize with dashboard-reorder-tiles

4. Experiment Design

When setting up A/B tests:

1. Clarify hypothesis: What change, expected impact, and why
2. Find existing flags: feature-flag-get-all (reuse if appropriate)
3. Choose metrics: Use event-definitions-list to find trackable events
4. Set up experiment: experiment-create with:
   • Clear name and description
   • Primary metric (what you're optimizing)
   • Secondary metrics (guardrails)
   • Appropriate sample size (MDE guidance)

See references/experiments.md for detailed experiment patterns.

5. Cohort & Segment Analysis

For understanding user segments:

1. Define cohort criteria (user properties, behaviors)
2. Compare cohorts on key metrics:
   - query-run with breakdownFilter by cohort property
   - Conversion rates per segment
   - Retention per segment
3. Identify highest-value segments
4. Recommend targeting strategies

Query Patterns

TrendsQuery (counts over time)

{
  "kind": "InsightVizNode",
  "source": {
    "kind": "TrendsQuery",
    "dateRange": {"date_from": "-30d"},
    "interval": "day",
    "series": [{
      "kind": "EventsNode",
      "event": "event_name",
      "custom_name": "Display Name",
      "math": "total"
    }]
  }
}

Math options: total, dau, weekly_active, monthly_active, unique_session, avg, sum, min, max

FunnelsQuery (conversion analysis)

{
  "kind": "InsightVizNode",
  "source": {
    "kind": "FunnelsQuery",
    "dateRange": {"date_from": "-30d"},
    "series": [
      {"kind": "EventsNode", "event": "step_1", "custom_name": "Step 1"},
      {"kind": "EventsNode", "event": "step_2", "custom_name": "Step 2"},
      {"kind": "EventsNode", "event": "step_3", "custom_name": "Step 3"}
    ],
    "funnelsFilter": {
      "funnelWindowInterval": 7,
      "funnelWindowIntervalUnit": "day"
    }
  }
}

Breakdown Analysis

Add to any query:

"breakdownFilter": {
  "breakdown": "property_name",
  "breakdown_type": "event"  // or "person"
}

SaaS Metrics Framework

For SaaS products, prioritize these metrics:

Metric	Query Approach	Why It Matters
Activation Rate	Funnel: signup → key_action	Validates onboarding
DAU/MAU Ratio	Trends: DAU ÷ MAU	Engagement stickiness
Feature Adoption	Trends: feature_used by user	Product-market fit signals
Retention (D7, D30)	Cohort retention query	Long-term value predictor
Conversion (Trial→Paid)	Funnel: trial_start → subscription	Revenue health
Expansion Revenue	Trends: upgrade events	Growth efficiency
Churn Indicators	Declining usage patterns	Early warning system

Resources

• references/experiments.md - Detailed experiment design patterns
• references/saas-playbook.md - SaaS-specific analytics strategies