Restaurant Table Turn Time Revenue Analysis Builder

You are a hospitality data analyst helping a restaurant understand table turn time and dining room revenue. Analyze the data for:

Restaurant type: [casual dining, fine dining, cafe, tasting menu, bar, hotel restaurant, multi-unit]
Service period: [breakfast, lunch, dinner, brunch, event night, seasonal period]
Dataset available: [reservations, POS checks, table map, timestamps, party size, server section, waitlist, walk-ins]
Timestamp fields: [seated time, order time, entree fire, dessert, check drop, payment, table reset, next seating]
Revenue fields: [subtotal, drinks, dessert, tips, discounts, comps, taxes, check count]
Capacity context: [table count, seat count, patios, private dining, bar seats, turn targets]
Operating constraints: [kitchen capacity, staffing, bussing, host stand, reservation intervals, walk-in policy]
Customer experience goals: [no rushing, premium pacing, family-friendly, fast lunch, event turnover]
Known issues: [long waits, empty tables, bottlenecked kitchen, late reservations, large-party delays, low dessert attach]
Comparison needs: [weekday vs weekend, server section, party size, table size, reservation channel, weather]
Data quality concerns: [missing timestamps, split checks, merged tables, open checks, incorrect covers, outliers]
Decision question: [increase covers, adjust pacing, change reservation slots, add staff, change menu, protect guest experience]

Create:
1. Analysis plan with metrics, grain, filters, and data cleaning rules.
2. Definitions for table turn time, seated duration, reset time, revenue per available seat hour, and average check.
3. Data validation checklist for missing timestamps, merged tables, outliers, and incorrect party sizes.
4. Segment analysis by party size, table type, server section, daypart, channel, and menu category.
5. Bottleneck diagnosis for host stand, kitchen, bar, service, payment, and reset.
6. Scenario model for reservation intervals, table mix, staffing, menu pacing, and walk-in allocation.
7. Dashboard layout with trend charts, heatmaps, distribution plots, and exception tables.
8. Recommendations that protect guest experience while improving capacity.
9. Experiment plan for testing one operational change at a time.
10. Executive summary template with findings, confidence level, tradeoffs, and next data needs.

Do not assume that shorter dining time is always better. Separate revenue opportunity from guest experience, service quality, and brand positioning.