An Empirical Study of Customer Spillover Learning About Service Quality

Abstract

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.

Surge Pricing on A Service Platform under Spatial Spillovers: Evidence from Uber

“…Cachon et al (2017) presents an analytic model with dynamic prices and wages under self-scheduling capacity (independent agents), while Taylor (2018) considers platforms that commit to prices and wages in advance; they study the effect of agent independence and customer-delay sensitivity on the optimal price and wage. As an exception to spot pricing work, some studies have gone on to examine information spillovers from consumer learning about the quality of a service from past experiences (Musalem et al 2017) or they explore provider capacity when one service zone spills over to another service zone to meet unfilled demand. Bimpikis et al (2016) identify possible spillovers by considering ride-sharing platforms that price discriminate based on location to study the network effect of service demand patterns on the platform's pricing policy, profits, and consumer surplus under a stationary environment.…”