Prompt: 14-Day Plan to Decide Which Snack Shop Will Be More Profitable Next Quarter

Context: Two snack shops operate simultaneously at a school gate. You have 14 calendar days to collect data and produce a defensible recommendation on which shop will deliver higher profit over the next quarter.

Design a measurement and analysis plan that yields a robust decision under real-world constraints.

Deliverables

1. Primary metric
   • Specify the exact primary metric (e.g., profit per passerby per open hour) and explain why it is decision-aligned.
   • Define in-scope revenues and costs, and how you will normalize for traffic and hours.
2. Minimum data to collect
   • List granular data you will collect, at least: hourly foot traffic, transactions and AOV, item-level margins, labor hours, rent/utilities allocation, weather, school calendar, competitor promotions and price changes.
3. Identification strategy
   • Propose either an experiment (e.g., randomized flyer distribution or alternating queueing) or a quasi-experiment (e.g., hour-level difference-in-differences with fixed effects and weather controls).
   • Ensure the approach remains valid under: weekday/weekend and exam-week spikes, Store B extending hours, Store A weekend discounts, and a price change by one shop on day 9.
4. Model and uncertainty
   • Write the DID regression you would fit: define outcome, treatment, fixed effects, and controls.
   • Explain how you would compute a 95% confidence interval for the profit delta.
   • Provide a minimal power check to show that 14 days is sufficient (order-of-magnitude inputs are fine).
5. Guardrails and decision rule
   • Define guardrails to detect stockouts and cannibalization.
   • State the explicit decision rule (e.g., recommend Shop X if estimated Δprofit > $Y/day and CI lower bound >$ Z).