Start by validating the data and defining the metrics precisely, then segment the user base to identify where the gap between downloads and DAU occurs. Formulate hypotheses about potential causes, prioritize them, and design experiments to test the most impactful ones.
Pro tip: Focus on the quality of downloads and early user experience—many apps see high installs but low retention due to onboarding friction or mismatched expectations. Highlight the importance of cohort analysis and retention curves to uncover the real story.
Ensure downloads and DAU are measured consistently and accurately. Clarify what constitutes a 'download' (e.g., new installs vs. re-installs) and an 'active user' (e.g., opens app, performs key action).
Break down users by acquisition channel, geography, device, and time. Analyze retention curves and cohort behavior to see if certain groups have low engagement or churn quickly.
Generate hypotheses for why DAU isn't growing despite high downloads, such as poor onboarding, lack of compelling features, or seasonal effects. Prioritize based on potential impact and ease of testing.
Test hypotheses through A/B tests or multivariate experiments. For example, test onboarding improvements, push notification strategies, or feature enhancements to see impact on retention and DAU.
Continuously track key metrics post-experiment, learn from results, and iterate. Use findings to inform product roadmap and marketing strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.