I went straight into hypotheses without segmenting the user base first, which I regretted about two minutes in.
Start by clarifying the metric and segmenting the 1M drop-off by user cohorts, content preferences, and engagement patterns to identify root causes. Then prioritize hypotheses based on impact and feasibility, and propose data-driven solutions with clear success metrics.
Pro tip: Acknowledge that correlation isn't causation—propose A/B tests or holdout groups to validate root causes before scaling solutions. This shows rigor and avoids premature conclusions.
Define what 'dropping off' means (e.g., cancellation, inactivity) and segment the 1M users by demographics, content watched, device, plan type, and engagement metrics to spot patterns.
Brainstorm potential reasons: content exhaustion, price sensitivity, poor recommendations, technical issues, or competitive alternatives. Map each to available data.
Use cohort analysis, funnel analysis, and regression to test which factors correlate with drop-off. Look for leading indicators like declining watch time or search failures.
Rank root causes by impact and effort. Propose interventions such as improved onboarding, personalized content refreshes, or pricing tweaks, with clear success metrics.
Recommend A/B tests or pilot programs to validate solutions, measure impact on retention, and iterate based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.