I went straight to segmentation before even clarifying the metric, which I think was a mistake.
Start by clarifying the metric definition and scope of the drop (e.g., which songs, time period, user segments). Then systematically rule out data/measurement issues before exploring internal and external drivers, using a structured root-cause framework like segmentation and cohort analysis.
Pro tip: Always validate the data first—many apparent drops are due to tracking bugs, logging changes, or definition shifts. Also, consider whether the drop is isolated to specific regions, platforms, or user cohorts, as that can quickly narrow down the cause.
Define what 'song listens' means (e.g., streams, unique listeners, completed plays) and the exact time frame and comparison baseline. Determine if the drop is across all songs or specific ones, and which user segments are affected.
Investigate potential tracking errors, logging changes, or pipeline issues that could cause a false drop. Verify data integrity by cross-checking with other sources or internal dashboards.
Break down the metric by dimensions such as platform (iOS, Android, web), region, user type (free vs. premium), and content type (genre, artist). Identify which segments are driving the decline.
Consider recent product changes (e.g., UI updates, algorithm changes), marketing campaigns, or external events (e.g., competitor launches, holidays, news). Correlate timelines to identify potential causes.
Prioritize the most likely causes based on segmentation and timeline, then propose further analysis or experiments to confirm. Suggest actionable next steps to mitigate if the cause is identified.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.