顧客關係管理(Customer Relationship Management；CRM)近幾年在企業管理領域上造成相當大的話題，由於顧客消費意識的抬頭，使得企業與顧客之間的關係有所改變。以往「產品導向」之經營思維，逐漸不符合現今整體環境的需求，因而逐漸被更重視客戶感受之「顧客導向」的經營思維所取代。企業之行銷方式也由以往之「單一化、大眾化」，轉變為「個人化、客製化」之行銷方向。 基於如此的改變，企業需要透過分析以了解客戶的價值及需求。了解客戶的價值後，對於企業具高價值的客戶，其應該採取適當之保留策略以避免客戶流失；對於低價值的客戶，則須衡量所耗費之成本，避免浪費不必要之企業資源。 因此在本篇論文中，主要利用資料探勘中的叢集演算法對顧客做客戶區隔的動作。對於不同群集之使用者，分析其使用行為特徵，以利企業決策者制定適當之行銷策略以保留高忠誠度之客戶，並發掘潛在客戶群。 除此之外，針對具較高流失比例之叢集，本論文利用決策樹演算法分析其使用特徵。根據分析產生之結果，提供企業做為擬定防止顧客流失策略之參考 In recent years, Customer Relationship Management (CRM) has gained much attention on business administration. The relationship Between customers and corporations has changed because of the increasement of consumer awareness and knowledge. Customers now are facing huge amount of options from which to choose. Thus, more customers frequently change from one service provider to another in search of better service, which is called "churn". In such competitive business environment, CRM plays an important role in enterprises. Many corporations have adopted Customer-oriented policy rather than product-oriented strategy nowadays. The marketing strategy also shifts from simplex and popular marketing to customize and personalize marketing. In this paper, we applied customer segmentation by using clustering techniques in data mining. Customer segmentation involves identification of groups of customers with similar characteristics. According to the result, it could help enterprise to develop advisable business strategy and optimize the efficient use of resources throughout the organization. Besides, we also applied decision tree analysis to the clusters with high churn rates. Thus help enterprise preventing customer from leaving.