Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group


First Advisor

Ryan R. Dew


I explore the application of Bayesian statistical modelling, and in particular Bayesian nonparametric methods in marketing research. I apply Bayesian nonparametric methods in both chapters of my dissertation to model two types of customer dynamics. In the first chapter, I investigate the impact of implementing a free cancellation program on customer behavior and firm profits in a hostel booking setting. While many firms have recently introduced free cancellation programs, the impact of such programs on customer behavior and firm profits remains unclear. I investigate this question empirically, using data from a hostel booking platform that recently introduced a free cancellation program. To understand the program’s impact on a myriad of aspects of customer behavior, including booking timing, spend amount, and propensity to cancel, while also accounting for latent attrition and customer heterogeneity, I build a hierarchical, Bayesian nonparametric model of behavior, leveraging Gaussian process change points to capture the effect of the free cancellation program on booking dynamics, and a Dirichlet process mixture specification for customer heterogeneity. These nonparametric components of the model allow us to make minimal assumptions about important aspects of booking behavior, while uncovering rich insights about the time-varying impact of the program, and the heterogeneity of customers. Our results suggest that the free cancellation program led customers to book more frequently, book earlier, spend more, and cancel more of their trips. Crucially, the increase in bookings generally outweighed the increase in cancellations in long term, resulting in an increase in average customer lifetime value. In the second chapter, I apply Bayesian nonparametric methods, in particular, Multi-output Gaussian Process, to model the cross-category dynamics of customers’ preference parameters in brand choice models. I show that the proposed model allows us to transfer information about customers’ preference parameters within and across categories, and that modelling the cross-category dynamics of customers’ preference parameters improves model fit and prediction accuracy. Moreover, leveraging information across categories gives us more reliable estimates of price elasticities. Together, these two chapters illustrate the power of Bayesian methods to gain deep insights into dynamic marketing problems.