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

• Check for existing context: Read .claude/product-context.md if it exists

• 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?

• 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

• 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

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:

• dashboard-create with descriptive name

• Build insights with query-run → insight-create-from-query

• Add to dashboard with add-insight-to-dashboard

• Organize with dashboard-reorder-tiles

4. Experiment Design

When setting up A/B tests:

• Clarify hypothesis: What change, expected impact, and why

• Find existing flags: feature-flag-get-all (reuse if appropriate)

• Choose metrics: Use event-definitions-list to find trackable events

• 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