Applying Data Analytical Technique to Customer Lose and Customer Value

• Main Authors: Wei-Chien Lien, 連惟謙
• Other Authors: Shih-Ming Pi
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/bg6x8t

碩士 === 中原大學 === 資訊管理研究所 === 92 === The competition element in nowadays industry has transferred from promoting inner core competition to satisfy customers’ needs. Only providing intimate service can seize the hearts of customers. Customer management is very important to an enterprise.According to Ravi Kalakota and Marcia Robinson（1999）, CRM must be implemented in three stages in order to manage customer cycle properly: gain new customers, strengthen the profitability of the exiting customers and maintain the lifelong value of the existing customers. Generally speaking, most enterprises focus more on developing new customers than maintaining long term relationship of existing customers. According to Peppers,Don and Rogers,Martha the cost of developing a new customer is five times more than retaining an existing one. Besides, 25% average customers have been lost every year. The companies would have 100% profit growth if the customer lose rate is decreased by 5%. Therefore, investigating customer lose and customer value are one of the major motives in this research. This research is focused on A company, which is an industry combining sport, leisure and entertainment. This industry has crucial relationship with customers and is the model in domestic industry. This research is to understand how to apply members database to analyze customer lose. It adopts four classification techniques: discriminant analysis, logistic analysis, neural networks analysis and decision trees analysis to guide mathematics model in customer lose. Then, the main forecasting aviations will be conducted gradually and be compared to differentiate rate in order to establish higher differentiate rate of mathematics model. On the other hand, it adopts RFM customer value analysis of Arthur Hughes (1994) to analyze related transaction information from existing and lost members. In addition, it also applies customer purchase period to implement MLE and WMLE customer value trend analysis. The result of this research will provide A company the related reference guide in analyzing customer lose, customer value and value trend. Key works: Data Analysis、Multiple Classification、DataBase Marketing、Customer Relationship Management、Customer Lose、Customer Value.