Incorporating Experience Quality Data into CRM Models: The Impact of Gambler Outcomes on Casino Return Times

I consider the direct marketing targeting problem in situations where 1) the customer's experience quality level varies from occasion to occasion, 2) the rm has measures of these quality levels, and 3) the rm can customize marketing according to these measures and the customer's behaviors. A primary contribution of this paper is a framework and methodology that allows the manager to assess the marketing response of a forward-looking customer with any speci c experience and behavior history, which in turn can be used to decide which customers to target for marketing. This research introduces a novel, tractable way to estimate and introduce exible heterogeneity distributions into Bayesian learning models with forward looking agents. 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 o er ndings on gambler learning and direct marketing responsiveness and show that casino pro tability increases substantially when marketing incorporates gamblers' beliefs and past outcome sequences into the targeting decision.