  "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.
