Joint Assortment Optimization and Customization Under a Mixture of Multinomial Logit Models: On the Value of Personalized Assortments

Abstract

Assortment Personalization in E-commerce In the age of big data, online businesses have access to a tremendous amount of browsing and purchasing data from their customers. Consequently, there has been great effort in exploiting this data to offer personalized product assortments to each customer based on what is known about her or his preferences. A personalized assortment can potentially allow enhancing the customer experience as well as improving the revenue of the retailer. In “Joint Assortment Optimization And Customization Under A Mixture Of Multinomial Logit Models: On The Value Of Personalized Assortments,” Omar El Housni and Huseyin Topaloglu consider an optimization model that jointly optimizes the assortment of products carried by an online retailer as well as the personalized assortments offered to each customer. They study the value of personalization and develop algorithms with provable theoretical guarantees to solve the optimization problem.