Start by clarifying the business objective and constraints, then outline the data collection plan covering demand, supply, and operational factors. Use a combination of historical data, surveys, and simulations to model parking demand and recommend capacity that balances customer satisfaction with cost efficiency.
Pro tip: Emphasize the importance of peak demand and turnover rates, and suggest a phased approach to capacity planning to avoid overbuilding. Mention that you would validate assumptions with A/B testing or pilot programs.
Understand the retailer's goals (e.g., minimize customer wait times, maximize lot utilization) and constraints (budget, space, regulations).
List internal data (sales, foot traffic, loyalty programs) and external data (demographics, competitor parking, traffic patterns) to collect.
Use historical data and surveys to estimate arrival rates, dwell times, and peak periods; build a simulation or queuing model to predict required capacity.
Analyze cost-benefit trade-offs of different capacity levels, considering customer satisfaction and ROI; recommend an optimal capacity with a buffer for variability.
Propose a pilot or phased implementation to test the recommendation and refine the model based on real-world feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.