My first instinct was to jump straight to metrics like activation rate and drop-off points, but I realized mid-answer I hadn't said anything about what 'good' even looks like for this onboarding.
Start by clarifying the goals and success metrics of the onboarding experience, then outline a structured analysis plan that includes defining metrics, setting up tracking, analyzing data, and iterating. Emphasize a data-driven approach with A/B testing and root cause analysis to identify improvements.
Pro tip: Propose a guardrail metric to ensure that improvements in onboarding don't negatively impact other key metrics like long-term retention or engagement. This shows you think holistically about product health.
Identify key performance indicators (KPIs) such as completion rate, time to complete onboarding, user satisfaction, and downstream metrics like retention and engagement. Align these with business goals.
Verify that proper event tracking and logging are in place to capture user interactions at each step of the onboarding flow. This includes funnel events, errors, and timing data.
Use funnel analysis to identify drop-off points, segment users by cohort or acquisition channel, and compare against previous onboarding or control groups. Look for statistically significant changes.
For any drop-offs or issues, dig deeper using qualitative data (user feedback, session recordings) and quantitative methods (hypothesis testing, correlation analysis) to understand why users are struggling.
Propose improvements based on findings and test them via A/B experiments. Measure impact on primary and guardrail metrics, and roll out successful changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.