Diamonds in the Rough: Leveraging Click Data to Spotlight Underrated Products

• 成果类型: Article; Early Access
• 署名作者: Modaresi, Sajad; Emadi, Seyed Morteza; Deshpande, Vinayak
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478251350097
• 发表日期: 2025
• 关键词: Click Data; Consumer Search Model; structural estimation; Online Retailing

摘要

We study the click and purchase behavior of customers in an online retail setting by employing a structural estimation approach. In particular, we aim to understand the impact of the information available to the customer before and after the click on the customer's search and purchase behavior. We propose a sequential discrete choice framework to model the customer's search strategy, where the customer repeatedly decides between continuing her search by clicking on a product, or stopping her search and making a purchase/no-purchase decision. By combining the click and order data, our proposed structural framework allows us to disentangle and separately estimate the attractiveness of a product before and after the click. This, in turn, allows us to identify underrated products which we call diamonds in the rough: these are products that have low pre-click but high post-click attractiveness; thus, even though such products have a low chance of being clicked, they have a high chance of being purchased, if clicked. The proposed framework provides an online retailer with new tools and insights to better manage the product assortment based on customer click and purchase behavior. We estimate our model on a data set from the Chinese retailer JD.com. Through simulation studies, we illustrate how our model can be operationalized and used for improving assortment decisions by accounting for the customers' search behavior. In particular, we focus on a subset of 126 substitutable products as a representative sample of the data and find that the optimal assortments under our model significantly increase the expected revenue compared to the actual assortments displayed by JD.com, and two multinomial logit (MNL) benchmarks.