Incorporating Experience Quality Data into CRM Models: The Impact of Gambler Outcomes on Casino Return Times
Enabled by modern interaction-logging technologies, managers increasingly have access to data on quality levels in customer interactions. We consider the direct marketing targeting problem in situations where 1) the customer's experience quality level varies randomly and independently from occasion to occasion, 2) the firm has measures of the quality levels 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 experience quality data to model a customer's evolving beliefs about the firm's quality 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 experience 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 learning models. 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 offer interesting findings on gambler learning and direct marketing responsiveness and suggest that casino profitability can increase substantially when marketing incorporates gamblers' beliefs and past outcome sequences into the targeting decision.
CRM, targeted marketing, service quality experience, discrete choice, Bayesian learning, Bayesian estimation
SMU Cox: Marketing (Topic)