Integration of data mining technologies to analyze customer value for the automotive maintenance industry

Customer value refers to the potential contribution of customers to an enterprise during specific periods. When enterprises understand the value of customers, enterprises that understand customer value can provide customized service to different customers and thus achieve effective customer relationship management. This study focuses on the current automotive maintenance industry in Taiwan and systematically integrates numerous data mining technologies to analyze customer value and thus promote customer value. This investigation first applies the K-means and SOM methods to establish a customer value analysis model for analyzing customer value. By the results of the two methods, the customers are divided into high, middle and low value groups. Moreover, further analysis is conducted for clustering variables using the LSD and Turkey HSD tests. Subsequently, decision tree theory is utilized to mine the characteristics of each customer segment. Third, this study develops different strategies for customers with different values and thus promoted customer value. The analytical results in this study can provide a valuable reference with regard to customer relationship management for managers in the automotive maintenance industry.