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.
Highlights
With improving computing processing power, companies increasingly use data analytics for decision making [1] rather than relying on a leader’s or manager’s “gut instinct”
Sports demand has been researched in detail in the field of sports economics [5,6,7] where previous research has shown that ticket price has a negative effect on stadium attendance
Taking into consideration the timing of the ticket sales and the performance of the team, Dynamic Pricing (DP) method has been used empirically to determine the prices which the sellers should implement to maximize their revenues based on capacity allocation and specific game strategies
Summary
With improving computing processing power, companies increasingly use data analytics for decision making [1] rather than relying on a leader’s or manager’s “gut instinct”. Taking into consideration the timing of the ticket sales and the performance of the team (ranking in the league), DP method has been used empirically to determine the prices which the sellers should implement to maximize their revenues based on capacity allocation and specific game strategies. Our model assumes that there exists a fixed initial inventory of C over a limited period of time T and a seller tries to optimize profits by selling tickets for a specific game in a monopolist market. Before evaluating revenues for every time period, I must estimate the demand function from our distribution functions of the price points for two games in the 2016 season using an ordinary least data.
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