I started with data integrity because I've been burned before by jumping to product explanations when it was actually a logging bug.
Start by clarifying the metric definition and validating the data pipeline to rule out instrumentation or logging issues. Then segment the drop by dimensions like platform, region, user cohort, and time to localize the cause, and finally correlate with recent changes or external events to identify the root cause.
Pro tip: Always check if the drop is real before diving deep—many 'metric drops' are actually data pipeline failures or logging bugs. Also, consider that a 10% drop might be intentional (e.g., due to a product change) or a shift in user behavior, so align with product and engineering teams early.
Confirm the exact definition of 'daily post views' and check if the drop is real by verifying data pipelines, logging, and aggregation logic. Look for anomalies in data freshness or completeness.
Break down the metric by dimensions such as platform (iOS, Android, web), region, user demographics, and time of day to see if the drop is concentrated in a specific segment.
Check recent code deployments, A/B tests, feature launches, or infrastructure changes that could impact post views. Use dashboards and version control to identify potential culprits.
Evaluate if external events (e.g., holidays, competitor launches, news events) or seasonal trends could explain the drop. Compare with historical data for similar periods.
Based on segmentation and correlations, form hypotheses about the root cause and validate them with further analysis or experiments. Communicate findings and recommended actions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.