Funnel Drop-Off Diagnosis

Find out why users drop off at specific steps — then fix the right thing.

How to use

• /funnel-drop-off-diagnosis Apply funnel diagnosis constraints to this conversation.
• /funnel-drop-off-diagnosis <funnel data> Diagnose a specific funnel with the provided conversion data.

Constraints

Funnel Definition

Before diagnosing, MUST ensure the funnel is:

• Sequential: each step logically follows the previous
• Measurable: exact user counts at each step
• Complete: no invisible steps between measured ones
• Time-bounded: define a reasonable window (same session? 7 days?)

Diagnostic Checklist

For the problem step, work through causes in this order:

1. Technical issues: page loading? Broken elements? Cross-device/browser? Intermittent failures? Check here first — most common, easiest to fix.
2. Clarity problems: does the user know what to do? Is the CTA obvious? Too many choices? Mismatch between promise and reality?
3. Friction problems: too many inputs? Information user doesn't have ready? Unnecessary verification?
4. Trust problems: asking for sensitive data without trust signals? Design inconsistency? No help available?
5. Motivation problems: user doesn't see the value of completing this step? Effort exceeds perceived reward?
6. Expectation mismatch: previous step set wrong expectations? Marketing promised something different?

• MUST work through in order. Don't jump to motivation problems before ruling out bugs.

Segment the Drop-Off

• MUST break down conversion by: acquisition channel, device type, geography, user segment, time
• If one segment converts dramatically differently, the fix is specific to that segment's context
• NEVER diagnose a funnel problem without checking segments first

Impact Estimation

For each diagnosis, estimate:

• Confidence: how sure is this the cause? (High/Medium/Low)
• Impact: if fixed, how much would conversion improve?
• Effort: how hard is this to fix?
• MUST prioritize: high confidence + high impact + low effort first

The Funnel Paradox

• Optimizing one step can hurt a later step
• Making signup easier may bring less-qualified users who churn faster
• MUST measure downstream impact, not just the step being fixed

Anti-Patterns

• Guessing causes without checking data or segments
• Fixing the page where drop-off happens when the problem started earlier
• Optimizing a step that already works well while ignoring the real bottleneck
• A/B testing without a diagnosis — you're guessing with more steps