Predicting Season Ticket Holder Retention Using Rich Behavioral Data

in Journal of Sport Management

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Adam Karg Swinburne University of Technology

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Ali Tamaddoni Deakin University

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Heath McDonald RMIT University

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Michael Ewing Deakin University

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DOI:
https://doi.org/10.1123/jsm.2020-0190
Keywords:
churn prediction; loyalty; machine learning; professional sport teams; season ticket holders
First Published Online:
26 Mar 2021
In Print:
Volume 35: Issue 5
Page Range:
426–439
Restricted access

Season ticket holders are a vital source of revenue for professional teams, but retention remains a perennial issue. Prior research has focused on broad variables, such as relationship tenure, game attendance frequency, and renewal intention, and has generally been limited to survey data with its attenuate problems. To advance this important research agenda, the present study analyzes team-supplied behavioral data to investigate and predict retention as a loyalty outcome for a single professional team over a 3-year period. Specifically, the authors embrace a broad range of loyalty measures and team performance to predict retention and employ novel data mining techniques to improve predictive accuracy.

Karg is with Swineburne University of Technology, Hawthorn, Victoria, Australia. Tamaddoni and Ewing are with  Deakin University, Burwood, Victoria, Australia. McDonald is with RMIT University, Melbourne, Victoria, Australia.

Karg (akarg@swin.edu.au) is corresponding author.
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