Attempts were made to identify the prioritized determinants of season ticket holders’ (STHs) renewal behavior. Also, grounded in the lens of luxury fever, we explored how the determinants are differently weighed and processed across two types of STHs, including regular (R-STHs) vs. premium seat holders (P-STHs). Through a partnership with a Big Ten athletic department, the attendance data were obtained and analyzed. This study adapted a decision tree modeling approach to predict renewal/churning behavior by setting learning decision rules in the CHAID algorithm, a supervised learning algorithm family. Results of this study indicate that discounted tickets, affordability, and entertainment were important determinants for R-STHs. By contrast, tenure as a ticket holder, game outcomes, and business use were significantly weighed for P-STHs. Recent attendance and exclusive benefits were important branches for both decision trees. Th is research expands the understanding of the subscription-based services industry by applying a fresh data mining approach.