I started with the usual segmentation angle: device, geography, query type, time of day.
Start by clarifying the metric definition and the scope of the drop (e.g., which ads, devices, geographies, and time period). Then systematically rule out data/measurement issues, external factors, and internal changes, using segmentation and hypothesis testing to isolate the root cause.
Pro tip: Emphasize that you would first verify the data pipeline and metric definition before diving into product changes—many apparent drops are due to logging or attribution issues. Also, quantify the impact and prioritize hypotheses by likelihood and ease of testing.
Define CTR precisely (e.g., clicks/impressions) and confirm the drop is real and not a data artifact. Identify the time frame, affected segments (device, geo, ad format), and whether it's isolated to Google Shopping.
Verify that impression and click tracking are functioning correctly, and that there are no logging errors, pipeline delays, or changes in attribution. Compare with other metrics like conversion rate to see if the drop is consistent.
Break down CTR by dimensions such as device, browser, geography, ad position, product category, and time. Identify which segments are driving the overall drop and whether it's uniform or concentrated.
List internal changes (e.g., ranking algorithm updates, UI changes, new ad formats) and external factors (e.g., seasonality, competitor actions, market trends). Correlate the timing of the drop with deployments or events.
Form hypotheses and test them using A/B tests, holdback groups, or causal inference methods. If a cause is confirmed, propose a fix or mitigation, and monitor the metric post-change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.