Data-Driven Sports Ticket Pricing for Multiple Sales Channels with Heterogeneous Customers

35 Pages Posted: 7 Sep 2019 Last revised: 3 Nov 2020

See all articles by Hayri Alper Arslan

Hayri Alper Arslan

University of Texas at San Antonio - College of Business - Department of Economics

Robert F. Easley

University of Notre Dame

Ruxian Wang

Johns Hopkins University - Carey Business School

Ovunc Yilmaz

Leeds School of Business, University of Colorado Boulder

Date Written: September 3, 2019

Abstract

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 multiplesales 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 optimizationtools 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

Suggested Citation

Arslan, Hayri Alper and Easley, Robert F. and Wang, Ruxian and Yilmaz, Ovunc, Data-Driven Sports Ticket Pricing for Multiple Sales Channels with Heterogeneous Customers (September 3, 2019). Available at SSRN: https://ssrn.com/abstract=3447206 or http://dx.doi.org/10.2139/ssrn.3447206

Hayri Alper Arslan

University of Texas at San Antonio - College of Business - Department of Economics ( email )

6900 North Loop 1604 West
San Antonio, TX 78249
United States

Robert F. Easley

University of Notre Dame ( email )

Information Technology, Analytics, and Operations
Mendoza College of Business
Notre Dame, IN 46556
United States
219-631-6077 (Phone)

Ruxian Wang

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

Ovunc Yilmaz (Contact Author)

Leeds School of Business, University of Colorado Boulder ( email )

Boulder, CO 80309-0419
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
358
Abstract Views
1,143
rank
100,573
PlumX Metrics