posthog-analytics

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?

1. 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

1. Save context: Write discovered knowledge to .claude/product-context.md

Context File Structure

```markdown

# Product Context

[What the product does, target users]

| Event | Meaning | Importance |

|-------|---------|------------|

| $pageview | Page visit | Navigation tracking |

| signup_completed | User registered | Core conversion |

| [custom events discovered] | | |

• user_tier: free/pro/enterprise
• [other key properties]

• Primary: [e.g., Weekly Active Users, Conversion Rate]
• Secondary: [e.g., Feature Adoption, Retention]

• Activation: signup → onboarding_complete → first_value_action
• [other key funnels]

```

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

1. Funnel Health

- query-run: Core activation funnel

- query-run: Conversion funnel (trial → paid if SaaS)

- Look for: Drop-off points, conversion changes

1. Retention Check

- query-run: Cohort retention (week-over-week)

- Look for: Retention curve shape, changes over time

1. Feature Adoption

- query-run: Feature usage by user segment

- Look for: Underused features, power user patterns

1. Error Impact

- list-errors: Top errors by occurrence

- error-details: Impact on user journeys

```

Insight Presentation Format:

```

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](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

1. Identify highest-value segments
2. Recommend targeting strategies

```

TrendsQuery (counts over time)

```json

{

"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)

```json

{

"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:

```json

"breakdownFilter": {

"breakdown": "property_name",

"breakdown_type": "event" // or "person"

}

```

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 |

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