Customized Product Assortment and Marketing Effort A Robust Approach
时 间：2017年4月7日（周五）下午 12:00-13:30
地 点：体育外围平台APP紫金港校区行政楼1002会议室
主 讲 人：金庆伟 博士, Zhejiang University
摘 要:
With increasing availability of consumer data and improving technologies in analytics, companies like online retailers are gaining better understanding of individual customer’s shopping behavior and preferences. The retailers thus can provide customized product assortment and employ individual marketing to maximize their profit. In this paper, we study a revenue management model where the retailer decides the product assortment and marketing effort under the multinomial logit customer choice model. We apply a robust approach to find the marketing effort that maximizes the worst-case revenue for the retailer under customized assortment. With given marketing effort, we analyze how to identify the customer preference vector(s) that lead to the worst-case revenue under various structures of the preference set. We find that without cardinality constraint on the assortment, the minimax problem leads to the same expected revenue as the maximin problem whereas the former in general gives a higher expected revenue in the case with cardinality constraint. Then we examine two types of marketing effort: product-level promotion, and category-level promotion. Under product-level promotion, the retailer offers different price discounts to different products while under category-level promotion, it spends constant advertising effort across the products. We develop efficient algorithms to solve the optimal marketing effort. One interesting insight is that the retailer should offer deeper discounts when the cardinality constraint for the assortment becomes more restrictive; this monotonicity result, however, does not hold under the category-level promotion. Finally, our extensive numerical studies show that the optimal solutions under the robust approach provide a good performance guarantee for the retailer without losing much on the average revenue.
主讲人简介：
Dr. Qingwei JIN is an associate professor on Operations Management at School of Management, Zhejiang University. His research focuses on the development of theories and algorithms for solving supply chain management, operations management and revenue management problems through optimization methods. His research interests include mixed integer linear and nonlinear optimization and applications, supply chain optimization, operations optimization, and revenue management. He is the principal investigator of two NSFC projects and has published several papers in journals such as SIAM Journal on Optimization, European Journal of Operational Research, Journal of Global Optimization and so on. He has a doctoral degree on Industrial Engineering from North Carolina State University in U.S. and was a visiting professor of several universities in Hongkong.
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数据科学与管理工程学系
2017年4月6日
