Real-Time Optimization of Personalized Assortments
First floor of Chemistry department, Jaber ibn Hayyan Hall
Wednesday, 28 December 2016
09:15 - 10:15
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
Hamid Nazerzadeh is an associate professor in the Data Sciences and Operations department at Marshall School of Business, and (by courtesy), Department of Computer Science, University of Southern California. He obtained his Ph.D. in Operations Research from Stanford University and his B.Sc. from Sharif University of Technology and has worked at Microsoft, Yahoo!, and Google research labs. His research focuses on mechanism design and optimization algorithms and their applications in operations and monetization of online markets. He is the recipient of Yahoo! Ph.D. Student Fellowship Award (2007), Honorable Mention in George Dantzig Dissertation Awards (2009), Google Faculty Research Award (2013), Marshall Dean's Award for Research Excellence (2014), and INFORMS Revenue Management and Pricing Section Prize (2014).