I went straight to impact times effort and started ranking drop-off points by volume of users lost, which felt right but I skipped over asking clarifying questions about which part of the funnel they actually cared about.
Start by mapping the funnel stages and defining the key metric for each stage. Then use quantitative analysis to identify the largest drop-offs and qualitative research to understand why they occur. Finally, prioritize based on impact and effort, and propose design changes that directly address the root causes.
Pro tip: At Duolingo, tie every funnel drop-off to a specific user motivation or habit loop (e.g., streak, leaderboard) and propose changes that reinforce those loops, not just generic UX fixes.
Break down the user journey into distinct stages (e.g., install, sign-up, first lesson, streak, subscription) and assign a conversion metric to each.
Use analytics to calculate conversion rates at each stage and pinpoint the largest absolute and relative drop-offs.
Combine quantitative data (e.g., cohort analysis, session recordings) with qualitative insights (e.g., user interviews, surveys) to understand why users drop off.
Score each drop-off based on potential impact (e.g., number of users affected, revenue potential) and effort (e.g., development complexity) using a framework like RICE.
Suggest specific design interventions (e.g., onboarding simplification, streak reminders) and outline an A/B testing plan to validate their effectiveness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.