UCL School of Management is delighted to welcome Fernando Bernstein, Duke University, to host a research seminar discussing ‘A dynamic clustering approach to data-driven assortment personalization.’
We consider a retailer facing heterogeneous customers with initially unknown product preferences. Customers are characterized by a diverse set of demographic and transactional attributes. The retailer can personalize the assortment offerings based on the available customers’ profile information to maximize cumulative revenue. To that end, the retailer must estimate customer preferences by observing transaction data. This, however, may require a considerable amount of information given the broad range of customer profiles and large number of products available. At the same time, the retailer can aggregate (pool) purchasing information among customers with similar product preferences. For a simplified version of the problem, we analytically characterize settings in which pooling transaction information is beneficial for the retailer. We also show that there are economies of scale in learning in the sense that there are diminishing marginal returns to pooling information from an increasing number of customers. We next propose a dynamic clusteringpolicy that adaptively adjusts customer segments (clusters of customers with similar preferences) and estimates customer preferences as more transaction information becomes available. We conduct an extensive numerical study to examine the benefit of pooling transaction data and personalizing assortment offerings by adopting the dynamic clustering policy. The study suggests that the benefits of dynamic clustering – over an “oblivious” policy that ignores profile information and treats all customers the same or over a “data-intensive” policy that treats customers independently – can be substantial.