Thu, January 2, 1:30 PM
45 MINUTES
Personalized Assortment Optimization for Online Retailer Considering Risk of Customers Churning

In this work, we consider an e-tailer with heterogeneous customers both on their preference and loyalty. Customer attrition plagues e-tailer and they should find a wayto address this issue since it has been studied that attracting new customers is more costly rather than keeping the current one. However keeping unhappy customers is not an easy task and sometime approaching them make the situation worst. Thus animplicit approach in saving the unhappy and at risk customer is chosen in this paper. We proposed a heuristic personalized assortment planning model by reserving items with low inventory levels or high demand for at risk customers who may have a stronger preference for those items. The survival model has been used to predict at risk customers and their lifetime. The output of the survival model has been used in the proposed dynamic programming model for the personalized assortment planning.

Morteza Saberi

Assistant Professor at the School of Information, Systems and Modelling, UTS, Sydney

Morteza is currently a Lecturer (Assistant Professor) at the School of Information, Systems and Modelling, UTS, Sydney. Prior to joining UTS, He was a Lecturer at the UNSW, Business school. He has an outstanding research record and significant capabilities in the area of business intelligence, data mining and applied machine learning. He has published more than 180 papers in reputable academic journals and conference proceedings such as Future Generation Computer Systems, Knowledge-Based Systems, Computers & Industrial Engineering, Fuzzy Sets and systems, IEEE-ACCESS, Applied Soft Computing, Enterprise Information Systems, Business Process Management Journal, Journal of Manufacturing Systems, Journal of Loss Prevention in the Process Industries, Technological Forecasting and Social Change, Process Safety and Environmental Protection, Neurocomputing, Safety science, Quality & Quantity, Energy Policy, Applied Energy, Energy, AAA-I, WSDM & IJCAI.