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CSE 4395 Senior Design

CSE 4395 Senior Design. A Model to Forecast No Shows for The Dallas Symphony Orchestra. Keely Casady and Marc Sanderson. The Dallas Symphony Orchestra. Non-Profit Organization Meyerson Symphony Hall Classical and Pops Series August through May Thursday through Sunday

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CSE 4395 Senior Design

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  1. CSE 4395Senior Design A Model to Forecast No Shows for The Dallas Symphony Orchestra Keely Casady and Marc Sanderson

  2. The Dallas Symphony Orchestra Non-Profit Organization Meyerson Symphony Hall Classical and Pops Series August through May Thursday through Sunday Nine Consecutive Sold-Out Seasons

  3. The Problem The Problem: Despite having a large number of tickets sold, a large number of no shows can always be expected. • Ticket Sales are Steady • Decline in Subscription Tickets • Increase in Box Office Sales • Increase in Walk Ups • Flexible Exchange Policy • No Shows

  4. Classical Series Totals

  5. Pops Series Totals

  6. The Goal The Goal: To provide the Dallas Symphony Orchestra staff with a model to predict the no shows for a given upcoming performance. Reduce the Number of Empty Seats Improve Audience / Performer Morale Help Marketing Department Determine Wait List Capacity Increase Revenue via Reselling

  7. Analysis of Situation • Establish contacts at DSO • Collect historical data • Performance sheet information • Drop count records • Identify variables of interest • quantitative or subjective

  8. Technical Description • Multivariate Regression • Y = 0 + 1x1 + 2x2 + . . . + kxk • coefficients 0, 1, 2,…, k • k independent variables x1, x2, …, xk • dependent variable -- no show • Statistical Analysis System (SAS) • Normal Regression Procedure • Stepwise Regression Procedure

  9. Classical Series Regression Models • Normal Regression Procedure • Y = -42.487289 + 58.844256(THUR) + 40.122351(FRI) + 23.223861(SAT) - 42.906939(COND) - 21.228757(ART) + 21.580583(CHORUS) + 35.180223(WEATHER) + 0.613395(BOXOFF) + 0.310128(SUBTIX) + 0.763227(EXCHANGE) • Stepwise Regression Procedure • Y = 198.52778626 - 37.72674646(SUN) - 43.53072694(COND) - 25.17187851(ART) + 0.61406004(BOXOFF) + 0.21429378(SUBTIX) + 0.75318909(EXCHANGE)

  10. Pops Series Regression Models • Normal Regression Procedure • Y = 1180.780760 + 75.781180(THUR) + 55.083079(FRI) + 112.432408(SAT) + 35.386645(WEATHER) - 0.458060(BOXOFF) + 0.345219(SUBTIX) - 0.143412(EXCHANGE) • Stepwise Regression Procedure • Y = 486.89090909 + 60.23409091(SAT)

  11. Visual Basic No-Show Forecaster

  12. Interpretation R2, coefficient of multiple determination CL Normal … R2 = 0.2832 CL Stepwise … R2 = 0.2703 PP Normal … R2 = 0.1205 PP Stepwise … R2 = 0.0488 High R2 value is desired Interpreted as R2 x 100% (percentage variation)

  13. Conclusions • Set out to help DSO with no show problem • Develop a forecasting model • Formulate a user friendly program • Models are limited • Quantitative focus • Subjective rankings • Other key factors which must play a role • Valuable information discovered

  14. Future Recommendations • Inform current season ticket holders • Develop a customer profile • Change the subscription ticket packages • Implement a stricter exchange policy • Create a waiting list • Track ticket sales and exchanges on a daily basis • Determine the other key success factors

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