The Effect of Gambling Outcomes on Casino Return Times with Scalable DDC

Abstract Enabled by modern interaction-logging technologies, managers increasingly have access to outcome data from customer interactions. We consider the direct marketing targeting problem in situations where 1) the customer’s outcomes vary randomly and independently from occasion to occasion, 2) the firm has measures of the outcomes experienced by each customer on each occasion, and 3) the firm can customize marketing according to these measures and the customer’s behaviors. A primary contribution of this paper is a framework and methodology to use data on customer outcome data to model a customer’s evolving beliefs related to the firm and how these beliefs combine with marketing to influence purchase behavior. Thereby, this paper allows the manager to assess the marketing response of a customer with any specific outcome and behavior history, which in turn can be used to decide which customers to target for marketing. This research develops a novel, tractable way to estimate and introduce flexible heterogeneity distributions into Bayesian dynamic discrete choice learning models on large datasets. The model is estimated using data from the casino industry, an industry which generates more than $60 billion in U.S. revenues but has surprisingly little academic, econometric research. The counterfactuals suggest that casino profitability can increase substantially when marketing incorporates gamblers’ beliefs and past outcome sequences into the targeting decision.