Funnel Analysis

Quantitative analysis of user progression through a multi-step conversion process, identifying where users abandon and which steps need the most optimization effort.

Process

Key Fields

Question it answers	Where do users abandon the funnel? What is the conversion rate at each step? Are rates different by user segment?
Participants & timing	Continuous from analytics data · minimum 200-500 completions per funnel · 1-2 weeks per optimization cycle
AI compatibility	AI identifies statistically significant drop-offs, segments by user type, and predicts impact of optimizations.
Output	Funnel visualization with conversion rates, drop-off comparison across segments, prioritized optimization targets, impact measurements

Use when

A multi-step conversion process exists (signup, purchase, feature adoption)
Optimizing conversion is a stated priority
The biggest drop-off steps need to be identified before investing in fixes

Skip when

Funnel is single-step or very short
Sample size is too small (fewer than 100 completions per step)

Common Mistakes

Optimizing a low-traffic step

Improving step 1 (1,000 users) has more impact than improving step 5 (50 users). Focus on high-traffic, high-drop-off steps first.

Not investigating why people drop

Seeing 30% drop at checkout without knowing if it is shipping cost shock, payment method issues, or trust concerns. Research the step before redesigning it.

Ignoring downstream effects

Checkout went from 3 pages to 1 page and completion rate rose 20%, but average order value dropped 15% because users added fewer items. Measure downstream impact, not just immediate conversion.