Online Shopping Customers Segmentation on Multiple Relations using Approximation of Variational Method

• Main Authors: Po-hsuan Chou, 周珀萱
• Other Authors: Rung-shiun Wu
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/03412305387780698489

碩士 === 國立中正大學 === 資訊管理所 === 96 === In recent years, internet and electronic commerce become popular as consumers getting tendency toward online shopping. The new economic has been developed into the internet virtual space. A new economic era is born. Enterprises often set objectives on acquiring profit-maximizing in their businesses. To achieve at profit-maximizing, enterprises must enhance customer relationship management and adjust marketing strategy. It is necessary for enterprises to engage in process of customer relationship management analysis on their daily business. For online shopping market, useful information can be extracted from customer database which is beneficial from shopping website. Our approach involves customer segmentation on customers’attributes which stored in multiple relations. By using latent Dirichlet allocation to segment customers, we can distinguish high loyal customers, potential customers, or losing customers. From customer segmentation, we explore customer value for e-company and online shopping channels. This information offers decision makers to improve their customer relationship management and adjust marketing strategy. We employ latent Dirichlet allocation model into our approach. In LDA model, we present approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. Variational approximate provides an effective computing method to segment customers into segmentation. According to customer-centric business tendency, we make suggestions for handling customers’ direction of purchase and pandering to consumer behavior or consumer needs. This allows an enterprise to achieve its objective by building up profitable relationships with customers and enhancing customer satisfaction and loyalty.