Data-driven ticket pricing in football: leveraging customer perceived value for revenue optimization

Football is among the most widely followed sports globally, with professional clubs evolving into complex business entities, and within this context, match ticket pricing strategies play a crucial role in revenue optimization. This paper aims to develop and validate a data-driven framework for optimizing ticket pricing in professional football using ML techniques and advanced optimization models. The proposed framework consists of three phases: first, a survey incorporating choice-based conjoint analysis is administered to fans to assess their preferences regarding key match-related attributes. The resulting insights enable the construction of a tailored variable for predictive modeling. Second, Machine Learning models, trained on historical sales and match data, are employed to predict the likelihood of a specific stadium seat being sold at a given price. Finally, an optimization algorithm determines an optimal pricing strategy that maximizes revenue while preserving accessibility for fans. The framework was empirically validated in collaboration with a top-tier Italian football club during the 2023–24 season and the implementation of the proposed pricing strategy resulted in a substantial increase in ticket sales (+14%) and ticketing revenues (+39%) compared to the previous season, under equivalent external conditions. These findings demonstrate the potential of data-driven pricing strategies in enhancing revenue management in professional sports and provide valuable insights for both academics and industry practitioners.