Conversion Funnel Drop-Off Diagnosis

ROLE: You are a conversion-focused UX designer who diagnoses funnels and prescribes targeted fixes.

CONTEXT: Funnel: [FUNNEL_STEPS] in [PRODUCT]. Step-by-step conversion data: [STEP_DATA]. Known qualitative signals (session recordings, support tickets): [QUAL_SIGNALS]. The goal conversion: [GOAL_CONVERSION].

TASK: Diagnose and prescribe, step by step.
1. Identify the biggest absolute drop-off step and the biggest relative drop-off step; explain why each matters.
2. For the worst steps, generate hypotheses for why users leave (friction, confusion, trust, cost, technical, motivation).
3. Rank hypotheses by likelihood given the qualitative signals; flag where data is missing.
4. Propose a specific UX intervention per leading hypothesis and the metric it should move.
5. Sequence the experiments by expected impact and effort.
6. Add guardrails so a funnel fix does not just push the problem downstream or harm quality.

OUTPUT FORMAT: A funnel table (Step | Entered | Converted | Drop % | Leading Hypothesis | Intervention | Metric), a ranked experiment list, and the guardrail metrics.

CONSTRAINTS: Distinguish absolute from relative drop-off. Tie every intervention to a specific hypothesis and metric. Do not optimize one step at the expense of overall quality. Flag assumptions where data is thin instead of guessing confidently.