I started by trying to define 'inactive' which felt like the right move but I probably spent too long on it.
Start by clarifying what 'inactive' means and how it's measured, then systematically segment the user base to identify patterns and potential causes. Propose a data-driven investigation plan that combines quantitative analysis with qualitative insights, and prioritize hypotheses based on impact and ease of testing.
Pro tip: Demonstrate product thinking by linking inactivity to business metrics like retention and revenue, and suggest a cross-functional approach involving data science, product, and engineering teams.
Clarify the definition of 'inactive' (e.g., no login in 30 days, no streaming activity) and verify the data source and calculation method. Ensure the 10% figure is accurate and consistent across reports.
Break down the inactive group by dimensions such as demographics, device type, subscription plan, tenure, and geographic region to identify patterns. Look for correlations with recent product changes or external events.
Generate potential causes (e.g., UI changes, content gaps, technical issues, seasonal trends) and prioritize them based on impact and likelihood. Use data to quickly validate or eliminate hypotheses.
For top hypotheses, perform detailed analysis: cohort analysis, funnel analysis, A/B test results, and user surveys. Engage with customer support and social media to gather qualitative feedback.
Propose actionable solutions (e.g., re-engagement campaigns, product fixes) and define success metrics. Set up monitoring to track changes and iterate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.