I started with the usual breakdown: is it all groups or specific types, all regions or one, did something change in the product or ranking algorithm recently.
Start by clarifying the metric definition and scope of the drop (e.g., which engagement metric, time period, user segments). Then systematically rule out data issues, external factors, and product changes before diving into user behavior and algorithmic causes.
Pro tip: Always validate the data first—many 'drops' are instrumentation bugs or logging changes. Also, segment by platform (iOS/Android/Web) and user cohorts to localize the issue quickly.
Define what 'engagement' means (e.g., posts, comments, likes, DAU) and confirm the 25% drop is real and not a data artifact. Identify the time frame, affected user segments, and platforms.
Verify data pipeline integrity, logging changes, and metric definitions. Compare with other sources (e.g., internal dashboards, external analytics) to rule out false alarms.
Look for recent product releases, algorithm updates, marketing campaigns, or external events (e.g., holidays, competitor launches) that could impact engagement.
Break down the metric by dimensions like platform, geography, user demographics, and group types to pinpoint where the drop is concentrated.
Based on segmentation, generate hypotheses (e.g., ranking change, notification bug, UI change) and validate with A/B tests, user surveys, or log analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.