Management Science, Forthcoming
47 Pages Posted: 20 Sep 2012 Last revised: 6 Mar 2014
Date Written: February 1, 2014
Motivated by the availability of real-time data on customer characteristics, we consider the problem of personalizing the assortment of products for each arriving customer. Using actual sales data from an online retailer, we demonstrate that personalization based on each customer's location can lead to over 10% improvements in revenue compared to a policy that treats all customers the same.
We propose a family of index-based policies that effectively coordinate the real-time assortment decisions with the backend supply chain constraints. We allow the demand process to be arbitrary and prove that our algorithms achieve an optimal competitive ratio. In addition, we show that our algorithms perform even better if the demand is known to be stationary. Our approach is also flexible and can be combined with existing methods in the literature, resulting in a hybrid algorithm that brings out the advantages of other methods while maintaining the worst-case performance guarantees.
Keywords: personalization, assortment optimization, choice models, online algorithms
Suggested Citation: Suggested Citation
Golrezaei, Negin and Nazerzadeh, Hamid and Rusmevichientong, Paat, Real-Time Optimization of Personalized Assortments (February 1, 2014). Management Science, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2149344 or http://dx.doi.org/10.2139/ssrn.2149344