Data-Driven Sports Ticket Pricing for Multiple Sales Channels with Heterogeneous Customers
Hayri A. Arslan , Robert F. Easley , Ruxian Wang , Övünç Yılmaz (2021) Data-Driven Sports Ticket Pricing for Multiple Sales Channels with Heterogeneous Customers. Manufacturing & Service Operations Management
35 Pages Posted: 7 Sep 2019 Last revised: 5 Aug 2021
Date Written: September 3, 2019
Problem Definition: We develop a framework to study purchase behavior from distinct segments of heterogeneous customers, and to optimize prices for different policies in a sports ticket market with multiple sales channels.
Academic/Practical Relevance: Sports teams face challenges maintaining or increasing ticket sales levels. With the growth of analytics, they aim to implement data-driven pricing techniques to improve gate revenues; however they do not have state-of-the-art demand estimation and price optimization tools that take into account the range of valuation across different seat sections and opponent match-ups.
Methodology: Partnering with a college football team, we develop a data-driven pricing tool which: (1) segments customers in two sales channels using transaction-level data and anonymous customer profiles; (2) explores the decision-making process of different customers within these segments using the Multinomial Logit and Mixed-Multinomial Logit frameworks; and (3) decides optimal or near-optimal prices subject to some business constraints enforced by the team management. In addition, our method takes the sequential arrivals of customers and the capacity constraints of seat categories into account.
Results: Our estimation results show that customers differ significantly in their sensitivities to price and distance to the field within each segment, in addition to the differences across segments. We also observe that customers become less likely to choose a seat category as its remaining inventory falls below a certain point.
Managerial Implications: By analyzing different policies, we show that price optimization could increase revenue by as much as 7.6%. In addition, better categorization of games and further refinement of seat category differentiation and related pricing may help further boost this figure up to 11.9%
Keywords: Event Pricing; Discrete Choice Models; Analytics; College Football; Sports Tickets
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