A Study on the Applications of Data Mining Techniques to Enhance Customer Lifetime Value

摘要

In today's competitive environment, a successful company must provide better customized services, that are not only acceptable to customers but satisfy their needs as well; in order to survive and succeed in gaining an advantage against competition. It has been proven by many studies that it is more costly to acquire new customers than to retain old ones. Consequently, evaluating current customers in order to enhance their lifetime value becomes a critical factor to decide the success or failure of a business. This study applies data from customer and transaction databases of a department store, based on the RFM model, and does clustering analysis to recognize high value customer groups for cross-selling promotions. Study findings show that clustering analysis can locate high value customers, and the company can then apply appropriate target marketing to enhance their lifetime value effectively. The implication for the marketer is that leveraging techniques of data mining can make the most from data of customers and transactions databases and thus create sustainable competitive advantages.