Diamonds in the Rough: Leveraging Click Data to Spotlight Underrated Products
通过结构估计方法分析在线零售中客户的点击和购买行为，提出一个序贯离散选择模型来识别那些点击率低但购买转化率高的被低估产品，并基于京东数据验证该模型能显著提升预期收入。
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.