I started with age verification as the obvious culprit and the interviewer just kind of nodded and waited, so I kept going.
Start by hypothesizing potential causes across the funnel—from browsing to delivery—considering regulatory, logistical, and user behavior factors. Then outline a data-driven investigation plan to validate each hypothesis, and finally propose targeted recommendations with measurable impact.
Pro tip: Emphasize the importance of segmenting the analysis by factors like location, time, and user demographics, as alcohol regulations and delivery logistics vary significantly. Also, mention the need to check for data quality issues before drawing conclusions.
Clarify what 'order completion rate' means (e.g., from order placement to delivery) and identify the key stages in the alcohol delivery funnel. Segment the data to see if the issue is widespread or concentrated in specific regions, user groups, or times.
Brainstorm potential causes: regulatory restrictions (e.g., delivery hours, ID verification), logistical challenges (e.g., special handling, limited couriers), user behavior (e.g., impulse purchases, cart abandonment), and product factors (e.g., pricing, availability).
Analyze funnel metrics to pinpoint where drop-offs occur. Compare alcohol vs. non-alcohol orders at each stage. Use cohort analysis, A/B tests, and qualitative methods (surveys, user interviews) to validate hypotheses.
Based on investigation, prioritize the most impactful causes. Propose solutions such as improving ID verification UX, partnering with more couriers, adjusting delivery fees, or implementing targeted promotions. Suggest A/B tests to measure effectiveness.
Define success metrics and set up monitoring to track improvements. Continuously iterate based on feedback and data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.