My first instinct was to jump straight to funnel analysis, which wasn't wrong but I skipped a step: clarifying what 25% even means relative to baseline.
Start by clarifying the scope and defining what 'drop-off' means (e.g., at which step, for which user segment). Then propose a structured investigation: validate the data, segment users, analyze the funnel, and form hypotheses about technical, UX, and external factors. Prioritize hypotheses by impact and ease of testing, and suggest experiments or instrumentation to confirm root causes.
Pro tip: Emphasize that you would first check whether the drop-off is real and not a data instrumentation issue, as premature optimization can waste resources. Also, consider both client-side and server-side factors, and think about how the sign-up flow might differ across platforms or regions.
Ask clarifying questions to understand the sign-up flow, the definition of drop-off (e.g., from which step to which), and the target user segments. Confirm the 25% figure and its source.
Ensure the drop-off is not due to logging errors, missing events, or sampling issues. Check data pipeline integrity and compare with other metrics.
Break down the funnel by steps, user demographics, device types, OS versions, geographies, and acquisition channels to identify where and for whom the drop-off is most severe.
Generate hypotheses across technical (e.g., API failures, latency), UX (e.g., confusing UI, too many steps), and external (e.g., competitor launch) factors. Prioritize by potential impact and ease of validation.
Design experiments (A/B tests, user studies) or deep-dive analyses (session replays, error logs) to confirm or refute hypotheses. Implement fixes and monitor.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.