I went straight to root cause analysis and started listing hypotheses: series-activated users might binge fast and churn once the show ends, or maybe the recommendation engine fails them right after.
Start by clarifying the metric and segmenting the activated users to understand the drop-off pattern. Then hypothesize root causes across the user journey—content, onboarding, and habit formation—and prioritize solutions based on impact and effort. Finally, propose a test-and-learn plan to validate and iterate.
Pro tip: Acknowledge that 'activated through watching a series' may attract binge-oriented users with different retention drivers than those who discover content more gradually. Focus on building habits, not just initial engagement.
Define what 'low retention' means (e.g., 30-day retention) and segment users by series type, viewing behavior, and demographics to identify patterns.
Analyze the end-to-end experience from activation to retention, considering content exhaustion, lack of personalized recommendations, and weak habit loops.
Use data to rank hypotheses by impact and confidence, focusing on the most significant drivers of churn.
Propose interventions such as improved post-series recommendations, onboarding to other content, or habit-building features, and outline A/B tests to measure impact.
Establish clear metrics (e.g., retention lift, engagement) and a process for continuous learning and optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.