I went straight to foot traffic and peak hours because that felt obvious, but I stalled a bit when trying to connect data sources to actual decisions.
Start by clarifying the business objective and constraints (e.g., store size, location, customer traffic patterns). Then outline a data collection plan that covers demand drivers, physical constraints, and operational goals, and finally explain how you would use that data to determine size, layout, and policies.
Pro tip: Frame your answer around the trade-off between customer experience and operational efficiency, and mention that you would validate assumptions with a pilot or A/B test before full implementation.
Ask questions to understand the store's goals (e.g., maximize customer throughput, minimize wait times) and any physical or budget constraints. This ensures your data collection is focused and relevant.
Break down data needs into three areas: demand (customer traffic, peak hours), supply (available space, budget), and operations (parking duration, turnover). This structured approach ensures comprehensive coverage.
For each data category, list specific sources (e.g., historical sales, traffic counters, surveys) and methods (e.g., manual counts, sensors, POS data). Explain how you would collect and validate the data.
Describe how you would use the data to determine parking lot size (e.g., peak demand analysis), layout (e.g., traffic flow simulation), and policies (e.g., time limits based on average shopping duration).
Propose a pilot or phased rollout to test assumptions and gather feedback, then adjust the design and policies based on real-world performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.