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The majority of recent empirical papers in operations management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as observational data lacks the benefit of random treatment assignment, estimating causal effects poses challenges. In the specific scenario where one can reasonably assume that all confounding factors are observed—referred to as selection on observables—matching methods and synthetic controls can assist researchers to replicate a randomized experiment, the most desirable setting for drawing causal inferences. In this paper, we first present an overview of matching methods and their utilization in the OM literature. Subsequently, we establish the framework and provide pragmatic guidance for propensity score matching and coarsened exact matching, which have garnered considerable attention in recent OM studies. Following this, we conduct a comprehensive simulation study that compares diverse matching algorithms across various scenarios, providing practical insights derived from our findings. Finally, we discuss synthetic controls, a method that offers unique advantages over matching techniques in specific scenarios and is expected to become even more popular in the OM field in the near future. We hope that this paper will serve as a catalyst for promoting a more rigorous application of matching and synthetic control methodologies. [ABSTRACT FROM AUTHOR]

Although variable pricing enables sports teams to respond to changing demand across games, reports point to somewhat limited implementation. In this paper, we study the implications of a switch to variable pricing on the primary and resale ticket markets using quasi-experimental data from the National Football League. Applying a difference-in-differences technique with propensity score weighting, we first show that the adoption of variable pricing increases primary market ticket sales by 1.59% per game. We then explore the mechanism behind this positive impact. We find that lower prices for the less attractive games are well received by customers, and customers do not have an unfavorable response to higher prices for the more attractive games. Our analysis of the customers' activities in the resale market shows that the minimum resale market prices increase for the more attractive games following the switch to variable pricing. This analysis provides evidence for the increase in the option value created by the resale market and explains the positive response to higher prices for the more attractive games. Finally, we investigate when variable pricing is likely to be the most effective. We find that variable pricing has greater success in team hometowns with lower income levels and higher income diversity, and creating more game tiers in variable pricing leads to higher sales. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4588. [ABSTRACT FROM AUTHOR]

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 in 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 valuations 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) computes 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%. [ABSTRACT FROM AUTHOR]