The Research of applying Data Mining Techniques on the Prediction Model of Customer Churn – Using an example of a certain Security Broker

• Main Authors: Chih-Chi Wang, 王志吉
• Other Authors: Yung-Sun Lee
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/92332163542332954752

碩士 === 銘傳大學 === 資訊管理學系碩士在職專班 === 100 === The merges and acquisitions (M&A) actions in securities industry has become a tendency for years. It toward to the prospects of enterprise getting bigger gradually. Securities industry offers products and services with high homogeneity. Therefore, the innovative financial products and better quality of customer service have been required to improved sharply on the market to avoid customer churn. This study uses of 4 dimensions, the business cycle on the market, macroeconomics index, service quality, and consumption characteristics of customer, to explore the influence factors that caused customer churn. We used a certain security broker’s customer data as samples, according to the complete procedures of data mining to mine knowledge. First, we eliminating the incomplete data, doing appropriate coding, using the attribute prediction evaluation model to identify the key prediction variables, to explore the critical factors that affects customer churn and provide the results of this research as reference to security brokers for formulating their marketing strategies. The results showed that frequency and monetary; the critical factors that affected the prediction of customer churn are recency, frequency and monetary; the composite index of leading business cycle indicators is the most leading and effective factor to sense the change of business cycle, also has significant impact on the customer churn.