| Summary: | 碩士 === 國立高雄第一科技大學 === 資訊管理所 === 92 === Since the opening of the Internet to business, many business models have been innovated. The phenomenon of network externalities brings in snowballing effects which attract more people to access the Internet, and make diverse electronic commerce services competitive in the Internet environment. The Internet Service Providers (ISPs), such as AOL, UUNet and HiNet, supporting the market of access the Internet for end users forms the infrastructure of EC. They unavoidably face a highly competitive environment and are forced to reduce the price in order to keep on the cutting edge; even obtain new customers at no cost. Therefore, it is of great importance for ISPs that how to improve customer’s loyalty and lower churn rate. In this paper we integrate technologies of data mining, OLAP and visualization technologies to discovery the so-called “heavy user discriminate rule” in the IP traffic data. The visualization of customer knowledge allows the ISP to identify groups of
high-value customers so that it can support the one-on-one customer care, and up-selling with seamless relationship of customers. We also develop a decision support system to support the decision-making for ISP in managing customer relationships.
|
|---|
