I went straight to bucketing possible causes: internal changes versus external factors, then started narrowing by platform, region, user segment.
Start by clarifying the metric definition and scope (e.g., which search surface, time period, and whether it's queries or sessions). Then systematically break down the funnel and segment by dimensions like platform, geography, query type, and user cohort to isolate the root cause, considering both internal changes and external factors.
Pro tip: Always validate the data pipeline first—35% drops are often instrumentation or logging issues, not real user behavior changes. Then, compare against a control metric (e.g., overall Google traffic) to see if the drop is search-specific.
Define exactly what 'Google searches' means (e.g., total queries, unique users, sessions) and confirm the data source is reliable. Check for logging errors, pipeline breaks, or recent instrumentation changes.
Break down the drop by dimensions: platform (mobile/desktop), geography, browser, query type (navigational, transactional, informational), and user cohorts (new vs. returning). Identify which segments are affected.
Review recent product changes, algorithm updates, UI modifications, or outages that could impact search volume. Check if any experiments or rollouts coincide with the drop.
Evaluate external events: competitor launches, market trends, seasonality, holidays, or macroeconomic shifts. Compare with industry benchmarks if available.
Prioritize likely causes based on data, then design quick tests or queries to confirm or rule out each hypothesis. Iterate until root cause is identified.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.