Research Article Open Access

DYNAMIC PRICING WITH UPDATED DEMAND FOR THE SPORTS AND ENTERTAINMENT TICKET INDUSTRY

Naragain Phumchusri1 and Julie L. Swann2
  • 1 Chulalongkorn University, Thailand
  • 2 , United States

Abstract

Revenue Management (RM) helped increase profitability for many travel industries. Selling perishable products with a fixed event date, the Sports and Entertainment (S&E) ticket industry can potentially benefit from RM ideas but has received less attention in the literature. In this study we develop dynamic pricing models for stochastic S&E demand in a discrete finite time setting, where demand depends not only on ticket prices but also on remaining times until the show dates. We assume the show popularity is uncertain to the seller, but this information can be learned via Bayesian updates as early sales are revealed. We present stochastic dynamic programs for Sports and Entertainment tickets pricing decisions. We test the models using real data obtained from a major performance venue in the U.S. to understand properties of the model solutions and performance under different scenarios. Our results show that demand learning is most beneficial when the initial estimates are incorrect. In addition, we found it is less necessary for the seller to vary price every period if demand variation is low and/or a large amount of demand arrives close to the show dates. Overall, we found that the benefits from having flexibility of price changes and demand learning can complement each other to achieve as much as 8.15% revenue increase on average, as compared to static pricing.

Journal of Computer Science
Volume 10 No. 11, 2014, 2240-2252

DOI: https://doi.org/10.3844/jcssp.2014.2240.2252

Submitted On: 6 March 2014 Published On: 19 December 2014

How to Cite: Phumchusri, N. & Swann, J. L. (2014). DYNAMIC PRICING WITH UPDATED DEMAND FOR THE SPORTS AND ENTERTAINMENT TICKET INDUSTRY. Journal of Computer Science, 10(11), 2240-2252. https://doi.org/10.3844/jcssp.2014.2240.2252

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Keywords

  • Dynamic Pricing
  • Demand Learning
  • Bayesian Updates
  • Sports and Entertainment Industry