Capacity Allocation of Game Tickets Using Dynamic Pricing

Aniruddha Dutta
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Aniruddha Dutta: Haas School of Business, University of California, Berkeley, CA 94704, USA

Data, 2019, vol. 4, issue 4, 1-12

Abstract: This study examines a pricing approach that is applicable in the field of online ticket sales for game tickets. The mathematical principle of dynamic programing is combined with empirical data analysis to determine demand functions for university football game tickets. Based on the calculated demand functions, the application of DP strategies is found to generate more revenues than a fixed price strategy. The other important result is the capacity distribution of tickets according to the football game intensity. Prior studies have shown that it is sometimes more profitable or football clubs to allocate a share of tickets to a retailer and earn a commission based on the sales, rather than selling the entire capacity of tickets by itself. This paper finds that in a high intensity game, where the demand is generally high, it is optimal for the club to sell all tickets by itself. Whereas, for less popular games, where there is considerable fluctuation in demand, the capacity allocation problem for maximized revenues from ticket sales, becomes a harder optimization challenge for the club. According to DP optimization, when the demand for tickets is relatively low, it is optimal for the club to retain 20–40% of the tickets and the rest of the capacity should be sold to online retailers. In the real world, this pricing technique has been used by football clubs and thus the secondary market online retailers like Ticketmaster and Vivid Seats have become popular in the last decade.

Keywords: dynamic pricing; machine learning; big data; capacity allocation; online tickets; pricing strategy; game tickets; willingness to pay (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
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