Bridging Models and Business: Understanding heterogeneity in hidden drivers of customer purchase behavior

Abstract

Recent years have seen many advances in quantitative models in the marketing literature. Even though these advances enable model building for a better understanding of customer purchase behavior and customer heterogeneity such that firms develop optimal targeting and pricing strategies, it has been observed that not many of the advanced models have found their way into business practice. This thesis aims to bridge the gap between advanced models and their business applications by systematically extending the use of models. We first focus on probabilistic customer base analysis models that deal with understanding customer heterogeneity and predicting customer behavior. These models specify a customer's transaction and defection processes under a non-contractual setting. Through this study, we show that the timing of the next purchase for each customer can be predicted using these models. We also extend them by modeling customer heterogeneity in a more flexible and insightful way. As a result, managers can obtain a refined segmentation. Based on the customer heterogeneity insights, we then focus on pricing strategies for online retailers who derive their revenues from delivery fees and sales. In order to come up with optimal pricing strategies for delivery fees, we use ideas from the two-part tariff literature. Given the time and costs associated with implementing advanced models/theories in managerial practice, the marketing executives need to be convinced by clearly demonstrating the contributions of such models. Our study serves as a step toward bridging advanced models and business practice by empirically demonstrating their extended contributions.