Uncovering Consumer Decision Rules under Complex Dynamic Environments: The Case of Coalition Loyalty Programs

We propose a framework for measuring the impact of a coalition loyalty program on consumers network choice decisions. Unlike single-vendor loyalty programs, coalition loyalty programs generate a very complex dynamic environment in which future rewards from accumulated points influence customers current network choice. In this research, we explicitly model the potential forward-looking incentive of consumers in loyalty programs, while allowing for bounded rationality. Using individuallevel data from a European coalition loyalty program, we apply the Bayesian classification algorithm and estimate heterogeneity in consumer decision rules. Our preliminary results indicate that there is a segment of consumers whose behavior is consistent with the prediction of dynamic programming models, but many consumers do not seem to care about accumulating points until their accumulated points get closer to the threshold level. We provide managerial implications for target marketing designs under such consumer behavior. ∗Associate Professor of Marketing, Rotman School of Management, University of Toronto. Email: [email protected]. †Assistant Professor of Marketing, Stern School of Business, New York University. Email: [email protected].