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"Spillover" learning is defined as customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. In this paper, we propose a novel, parsimonious and general Bayesian hierarchical learning framework for estimating customers' spillover learning. We apply our model to a one-year shipping/sales historical data provided by a world-leading third party logistics company and study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. Our empirical results are consistent with information spillovers driving customer choices. Customers also display an asymmetric response such that they are more sensitive to delays than early deliveries. In addition, we find that customers are risk averse being more sensitive to their uncertainty about the mean service quality than to the intrinsic variability of the service. Finally, we develop policy simulation studies to show the importance of accounting for customer learning when a firm considers service quality improvement decisions.

An Empirical Study of Customer Spillover Learning About Service Quality