讲座：Promotion Design with Path Dependent Network Effects
题 目：Promotion Design with Path Dependent Network Effects
演讲人：毕晟 博士生 新加坡国立大学
主持人：李成璋 助理教授 上海交通大学安泰经济与管理学院
The limited-time “collect and win” games are often used to promote products and drive sales to the retail outlets by creating short term temporal changes to customers' purchasing behavior -- the desire to buy products on promotion and frequency of purchases both increase with the number of previous purchases. We study the promotion design problem for these games, to determine the set of eligible products and the duration of the promotion. The customers' purchasing behavior depends not only on the product attributes and features (static effect), but also on product eligibility for promotion and historical purchases (path dependent network effect). We model the dynamic choice processes using poissonization of the Polya Urn models, to capture the transient change in the frequency of purchases and purchase probability of each product on promotion. We use this approach to study the optimal promotion design problem under different “collect and win” game settings, by solving non-convex assortment optimization problems. We obtain an exact and/or approximation approach for these problems, and show that the revenue-ordered promotion set is already near-optimal in many of these games. The optimal duration depends on the promotion set chosen, and also on the targeted number of products sold before the game found a winner for the grand prize. Using a set of data provided by a fast-food company, we show the importance of carefully choosing the promotion set and promotion duration, both decisions that will affect the total revenues and profits generated by such promotion campaigns.
Sheng Bi is a fifth-year Ph.D. candidate in the Department of Analytics and Operations at National University of Singapore, advised by Professor Chung Piaw Teo and Long He. Prior to this, she received a Bachelor’s degree in Industrial Engineering from Nanjing University. Her research interests are in the area of data-driven optimization, customer choice modeling, revenue and supply chain management.