My first instinct was to jump straight to hypotheses, which was wrong.
Start by clarifying the scope and impact of the drop, then systematically rule out data issues, external factors, and internal changes. Prioritize quick wins and communicate transparently with stakeholders while investigating deeper root causes.
Pro tip: Always check if the drop is real and not a data pipeline issue before diving into product changes—many 'crises' are just logging errors. Also, segment the data by platform, region, and user cohort to localize the problem quickly.
Confirm the 10% drop is real by checking data pipelines, logging, and dashboards for errors or delays. Compare with other metrics like server logs or user reports.
Break down the drop by dimensions such as platform (iOS/Android/web), geography, user cohort, and time. Identify if it's isolated to a specific segment or global.
Look for external events (holidays, outages, competitor launches) and internal changes (releases, experiments, config changes) that could explain the drop.
Prioritize likely causes based on data, then test them by querying logs, running experiments, or rolling back changes. Use a process of elimination.
If a bug or bad release is found, roll back or hotfix immediately. Communicate findings and actions to stakeholders, and set up monitoring to prevent recurrence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.