I jumped straight into segmenting by surface area (feed vs stories vs explore) which felt right but I skipped the obvious first step of checking whether the data itself was even reliable.
Start by clarifying the metric definition and scope—what exactly is 'impressions' and where is the 10% drop observed (platform, region, user segment, time period). Then systematically rule out data/measurement issues before diving into product, user behavior, and external factors, using a structured root-cause framework.
Pro tip: Always validate the data pipeline first—many 'metric drops' are actually logging or tracking bugs. Mentioning this shows you think like a seasoned PM who avoids wild goose chases.
Define 'impressions' precisely (e.g., feed impressions, ad impressions, story views) and confirm the drop's dimensions: time period, platform (iOS/Android/web), geography, user cohort, and surface. This prevents solving the wrong problem.
Check for instrumentation issues, logging errors, ETL pipeline failures, or recent changes in tracking. Compare with other related metrics (e.g., sessions, DAU) to see if the drop is isolated or systemic.
Break down impressions by user segments (new vs. existing, heavy vs. light), content types, and surfaces to identify if the drop is concentrated in a specific area. Use cohort analysis to see if it's a sudden or gradual decline.
Investigate internal factors (recent product changes, algorithm updates, bugs, ranking changes) and external factors (seasonality, holidays, competitor launches, platform policy changes). Correlate with release timelines and A/B tests.
Based on findings, prioritize the most likely root cause(s) and propose immediate mitigation (e.g., rollback, fix) and long-term monitoring. Suggest further experiments or data pulls if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.