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Funnel Analysis

Funnel analysis visualizes and measures user progression through a multi-step flow toward a conversion goal. Each step represents a user action, and the analysis focuses on drop-off rates between steps to identify optimization opportunities.

Key Facts

  • Funnel = sequence of user steps toward conversion goal
  • Key metrics: step conversion (N to N+1), overall conversion, drop-off points, time between steps
  • Typical e-commerce funnel: Visitor -> Product Page -> Add to Cart -> Checkout Start -> Payment -> Order Complete
  • Typical mobile app funnel: app_open -> registration_start -> registration_complete -> onboarding_complete -> first_purchase
  • Funnel optimization targets the biggest drop-off step first
  • Time window matters: only count funnel completion if done within N days/hours

Patterns

Funnel Optimization Process

  1. Identify biggest drop-off step
  2. Hypothesize cause (confusing UX, too many steps, unclear value)
  3. Test improvement (A/B or sequential)
  4. Measure change in conversion

Critical User Path (Event Map)

The minimum sequence of events defining the core user journey: 1. app_open - app launched 2. registration_start - registration flow opened 3. registration_complete - account created 4. onboarding_complete - tutorial finished 5. first_purchase - first payment

Event Prioritization

  1. Business KPI events (purchase, subscription, activation)
  2. Funnel step events (key screens, key actions)
  3. Error/crash events (identify issues)
  4. Engagement events (feature usage depth)

Funnel Analysis in Amplitude

  • Define sequence of events: sign_up -> onboarding_complete -> first_purchase
  • Shows drop-off at each step
  • Segment by property: compare conversion for iOS vs Android
  • Time window: only count funnel if completed within N days/hours

SQL Funnel Pattern

SELECT
    COUNT(CASE WHEN event = 'page_view' THEN user_id END) as step1_views,
    COUNT(CASE WHEN event = 'add_to_cart' THEN user_id END) as step2_cart,
    COUNT(CASE WHEN event = 'checkout_start' THEN user_id END) as step3_checkout,
    COUNT(CASE WHEN event = 'purchase' THEN user_id END) as step4_purchase
FROM events
WHERE event_date = CURRENT_DATE;

Install Conversion Rate (App Stores)

Conversion rate = installs / product page views. Benchmark: 25-35% for well-optimized apps.

Elements affecting conversion: - Icon - first visual impression, recognizable at small size - Screenshots - first 2-3 visible without scrolling (most important) - Preview video - auto-plays on iOS, show core value in first 3 seconds - App name + subtitle - conveys value proposition instantly - Ratings - apps below 4.0 have significantly lower conversion - Reviews - recent positive reviews matter more than old ones

Gotchas

  • Funnel analysis without time windowing can be misleading - a user who starts today and purchases 90 days later is different from one who completes in 10 minutes
  • Counting events vs counting unique users gives very different funnel shapes
  • Non-linear funnels (users skip steps, go back) require session-based analysis, not simple step counting
  • Segmenting funnels by acquisition source often reveals that aggregate numbers hide dramatically different behavior patterns

See Also