A Study on Recommendation Systems in Retail Channel

• Main Authors: Shu-Chin Kuo, 郭書琴
• Other Authors: Li-Chung, Jen
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/92205552995005625492

碩士 === 國立臺灣大學 === 國際企業學研究所 === 102 === With the advent of technology, it is easier for corporations to collect customer data and to develop virtual channel or online stores, which changed tremendously the way people consume today. Therefore, with computing technology, database marketing could help corporations to conduct efficient marketing strategies, to predict future trend and customer behavior, and to actively contact with target customers. However, vigorous virtual channel and online stores tread the neck of physical channel or physical retailers, keeping them barely survive today. Therefore, it is critical for physical channel and retailers to implement database marketing against low cost virtual channel. Database marketing use historical customers’ consuming data to apply one-on-one marketing strategies, attempting to reinforce relationship with customers and customers’ loyalty. The most prevalent execution of database marketing today is the recommendation system. Recommendation system is a platform to suggest customers to buy the products and the products are computed by the system and categorized in highest rating and preference for individual customer. While customers are heterogeneous, via implementing recommendation system, physical retailers could exactly predict the need of customers, control the inventory accurately and gain more bargaining power with branding manufacturers. This thesis used customer data of domestic noted supermarket and applied Hierarchical Bayesian Probit Model to build up recommendation system model. In this system model, each customer has his or her own preference to different brand (in the similar product category). In this way, each customer will receive personal shop suggestion for the next buying. Theoretically, personal suggestions are better than identical ones. The objective of this thesis is try to figure out whether the success hit rate of recommendation system via individual HB Probit model is more higher than the rate of traditional aggregate recommendation model.