This is the kind of question where you can go a dozen directions and none of them feel obviously wrong, which is the problem.
Start by clarifying the product, target market, and retention metric definition, then segment the user base to identify where retention drops. Use funnel analysis and cohort analysis to pinpoint the stage of drop-off, and generate hypotheses about causes (e.g., usability, content, competition) that you can validate with data or experiments.
Pro tip: Don't just focus on the 'what' but also the 'why'—combine quantitative data with qualitative insights (e.g., user surveys, app store reviews) to uncover underlying reasons. Also, consider external factors like market-specific challenges (e.g., device limitations, data costs).
Ask clarifying questions to understand what 'retention' means (e.g., D1, D7, D30), the target market, and the product's core value proposition. Ensure alignment on the goal.
Break down retention by user segments (e.g., demographics, acquisition channel, device type, geography) and cohorts to identify patterns and outliers. Compare with benchmarks or other markets.
Map the user journey and analyze where users drop off. Identify critical steps (e.g., onboarding, first post, friend connections) that correlate with long-term retention.
Based on data, list potential causes (e.g., poor onboarding, lack of content, performance issues, competition). Prioritize by impact and ease of testing.
Design A/B tests or multivariate experiments to test hypotheses. Supplement with user interviews, surveys, or usability studies to understand the 'why' behind behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.