The Application of Data Mining to Customer Value Analysis in the Food Industry

• Main Authors: Huang,Hsin-Hsin, 黃心心
• Other Authors: Ching-Chih Huang
• Format: Others
• Language: zh-TW
• Published: 101
• Online Access: http://ndltd.ncl.edu.tw/handle/84846008402548975555

碩士 === 南台科技大學 === 企業管理系 === 100 === Take control of customer’s consumption pattern and improvement of company’s service and marketing efficiency are important key factors affecting enterprise profit. With the transformation of business environment and increasing competition, the rule of thumb in operation model can no longer completely reflect the real requirement of customers. In recent years, the rapid development of information technology has significantly improved the functions of information system, such as large data processing and high-speed computing. Data mining technique is the comprehensive application of excellent data processing and computing abilities to business field for predicting the future trend of consumers and market, as well as creating new business opportunities. In this thesis, application of data mining to the consumer membership database and historical transaction records to put use customer value analysis in the food company. RFM (recenecy, frequency, monetary) model to put use analyze customer value, and SOM(self-organizing map) clustering was applied to divide customers into eight clusters. This study analyzed the characteristics of consumer behavior of various consumer groups, explained the importance of meanings of customer value and customer loyalty to enterprises, and identified important customers and potentially important customers of high value. Finally, through the graphics and charts of descriptive statistics analysis, this study proposed suggestions on marketing strategies and more efficient marketing strategies, as references for maintenance of good customer relationships and operating management.