I jumped straight into segmentation before even clarifying what 'usage' meant, which was a mistake.
Start by clarifying the metric definition and the scope of the drop (e.g., which usage metric, time period, user segments). Then systematically break down the problem using a structured framework like internal vs. external factors, segment analysis, and funnel analysis to identify root causes. Finally, prioritize hypotheses based on data and propose next steps for validation.
Pro tip: Demonstrate a hypothesis-driven approach by suggesting specific data cuts (e.g., new vs. existing users, platform, geography) and mentioning how you'd validate with A/B tests or user research. This shows you can move from diagnosis to action.
Define what 'usage' means (DAU, sessions, time spent) and confirm the 20% drop is real and not a data artifact. Identify the time frame, user segments, and platforms affected.
Break down the drop by user cohorts (new vs. existing, active vs. lapsed), demographics, geography, platform (iOS/Android/Web), and entry points. Look for disproportionate impacts.
Map the Groups usage funnel: discovery, joining, engagement, retention. Identify where the drop-off occurs and whether it's a supply (content) or demand (user) issue.
Check for recent product changes, bugs, algorithm updates, or competitive actions. Also consider seasonality, holidays, or macro trends that could affect usage.
Based on data, form hypotheses about root causes and prioritize by impact and likelihood. Outline how to validate (e.g., A/B tests, user surveys, deeper analytics).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.