Discovering Online Shopping Preference Structures in Large and Frequently Changing Store Assortments

Abstract

The authors develop an attribute-based mixed-membership model of consumers’ preference for stockkeeping units in store assortments. The model represents the underlying “topics of interest” that drive shopping behaviors as probability distributions over product attributes. It overcomes several limitations of latent Dirichlet allocation topic models and is particularly useful for making preference predictions in large and frequently changing store assortments. The authors apply the proposed model to investigate topics driving browsing and purchase activities in an online deal marketplace of fashion products and explore how preference structures evolve over time. They find commonalities and differences in the topics that drive the browsing and purchase stages of online shopping processes. In general, browsing covers a broader range of product attributes than purchases. Consumers tend to browse products of premium positioning and/or deep discounts in the deal marketplace, but when purchasing, they tend to gravitate toward lower-tiered products at their original prices and modest depths of discounts. The authors illustrate how insights from the proposed model can be utilized to profile consumers based on their price preferences and to improve personalized product recommendations. They show that the model's performance is particularly strong in predicting preferences for new products that are not in the existing assortment.