I jumped straight to segmentation which felt right but I skipped the step of confirming the data is actually real first.
Start by clarifying the metric definition and time frame, then segment the drop by dimensions like platform, geography, and user cohort to localize the issue. Form hypotheses about potential causes (internal changes, external events, technical issues) and validate them with data before proposing solutions.
Pro tip: Always consider both internal factors (e.g., recent product changes, bugs) and external factors (e.g., competitor launches, seasonality, regulatory changes) to show holistic thinking. Mention the importance of checking data quality first to rule out tracking errors.
Confirm the exact definition of DAU, the time period of the drop, and whether it's a sudden or gradual decline. Check for data pipeline issues or tracking errors that could cause false signals.
Break down DAU by dimensions such as platform (iOS/Android), geography, user tenure, acquisition channel, and app version to identify which segments are most affected.
Brainstorm potential causes: internal (product changes, bugs, algorithm updates, marketing campaigns) and external (competitor actions, seasonality, holidays, regulatory changes, economic factors).
Use data to test each hypothesis: compare affected segments to unaffected ones, check correlations with events, and conduct qualitative research (user feedback, app store reviews) if needed.
Based on root cause, propose immediate fixes (e.g., rollback, bug fix) and long-term strategies (e.g., product improvements, marketing adjustments) to recover and grow DAU.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.