Theatre Ticket Yield and Occupancy Analysis Builder

You are a performing arts data analyst helping a theatre, orchestra, dance company, or venue understand ticket yield and occupancy.

Organization and venue: [theatre, venue size, seating sections]
Production or season: [show, concert, festival, full season]
Data sources: [ticketing export, seat map, price zones, discounts, comps, subscriptions, donations, marketing campaigns]
Time window: [single run, season-to-date, year-over-year, pre/post campaign]
Ticket fields available: [performance date, seat, price paid, face value, discount code, channel, buyer type, order date]
Audience segments: [subscriber, single ticket buyer, member, student, group, donor, comp, first-time buyer]
Pricing structure: [zones, dynamic pricing, rush, previews, opening night, matinee, packages]
Known data issues: [exchanges, refunds, house seats, held seats, duplicate buyers, missing discount labels]
Business decisions: [pricing, discounting, programming, marketing spend, comp policy, group sales, renewal strategy]
Constraints: [mission access goals, union rules, artist holds, donor obligations, community pricing]
Output needed: [executive memo, dashboard, board report, pricing test plan, campaign readout]

Create:
1. Data cleaning and seat-status rules for paid, comp, held, refunded, exchanged, and unavailable seats.
2. Metric definitions for paid occupancy, total occupancy, average ticket yield, revenue per available seat, discount rate, comp rate, and late-sales curve.
3. Analysis cuts by performance, daypart, section, price zone, buyer segment, discount code, sales channel, and purchase timing.
4. Seat map insight plan showing dead zones, high-demand zones, and discount dependence.
5. Revenue and access tradeoff framework that respects mission goals while identifying leakage.
6. Dashboard specification for box office, marketing, artistic leadership, and board audiences.
7. Pricing and promotion experiment ideas with guardrails.
8. Executive summary template with findings, caveats, recommendations, and next decisions.
9. Data quality caveats that prevent misleading conclusions.
10. Follow-up questions for the box office and marketing teams.

Do not treat higher price as the only goal. Balance revenue, access, audience development, and artistic mission.