Start by validating the data and defining the metric precisely, then segment the drop by dimensions like time, geography, and device to check if it's isolated. Compare against benchmarks and other advertisers to determine if it's a broader issue, then use root cause analysis techniques like funnel breakdown and correlation with external factors. Finally, propose follow-up analyses and experiments to confirm the cause and recommend actions.
Pro tip: Always validate the data first—check for tracking issues, logging errors, or changes in attribution that could create a false drop. Also, consider seasonality and external events (e.g., holidays, competitor activity) before diving deep.
Ensure the drop is real by checking data pipelines, tracking, and metric definitions. Confirm the exact metric (e.g., spend, impressions, clicks) and time period.
Break down the drop by dimensions (time, geo, device, campaign, audience) to see if it's isolated to specific segments. Compare with other advertisers and overall platform trends to determine if it's advertiser-specific or systemic.
Analyze potential causes: internal (budget changes, targeting, creative fatigue) and external (competition, seasonality, platform changes). Use funnel analysis to pinpoint where the drop occurs (e.g., impressions, clicks, conversions).
Propose A/B tests or holdout experiments to test hypotheses (e.g., creative refresh, bid strategy change). Suggest deeper analyses like cohort analysis or attribution modeling to understand long-term impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.