چکیده (انگلیسی):

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.