Funnel Analysis

When to use

• Conversion is low and the team needs to know where users are dropping off
• A product change may have affected a specific funnel step
• Comparing conversion rates across channels, devices, or user cohorts
• Designing an A/B test and needing a baseline to set a meaningful MDE
• Building a regular funnel monitoring report

Process

1. Define funnel steps and time window — list the ordered sequence of events or pages that constitute the funnel. Agree on how long a user has to complete the funnel (session, 24 hours, 7 days). Ambiguous definitions here will invalidate the analysis.
2. Build the user-level funnel dataset — for each user who reached step 1, record which subsequent steps they completed and when, within the time window. Use scripts/funnel_analyzer.py to compute this from an events log.
3. Calculate conversion rates — compute step-to-step conversion (users reaching step N ÷ users reaching step N−1) and overall conversion (step 1 to last step). Record absolute drop-off counts at each step.
4. Analyse time-to-convert — for users who completed each step, calculate median, P75, and P95 time between steps. Long gaps can signal friction even without high drop-off.
5. Segment the funnel — run the funnel separately by channel, device type, user cohort, or other dimensions. Rank segments by overall conversion rate and identify where the worst-performing segment diverges from the best. See references/funnel_design_guide.md.
6. Prioritise and report — rank drop-off points by absolute users lost × estimated revenue impact. Produce assets/funnel_report_template.md with the funnel table, segment comparison, and ranked recommendations.