Companies can use customer segmentation to group customers with similar characteristics together and identify the differences between groups to develop marketing strategies. This study investigates the problem of customer segmentation in relation to automotive customer relationship management and presents a real case study of an automobile dealer in Taiwan. Although several past studies have adopted different clustering techniques with which to group customer attributes, few have simultaneously considered customer transaction behaviour and customer satisfaction variables. In addition, most previous work has used only a single clustering method for customer segmentation, which results in unreliable results and leads to inadequate marketing decisions. Therefore, in this study, we consider two clustering techniques, k-means and expectation maximization, and compare their results for correctness. The experimental results show that four customer groups are identified with both clustering methods: loyal, potential, VIP and churn customer groups. Based on the segmentation results, several customized marketing strategies aimed at each of the four customer groups are suggested to improve the quality of services for effective customer relationship management.