長期租賃車業者之市場開發，業者為透過汽車產品進行金融服務，著重於服務內容，多透過汽車經銷商業務代表或由業者自己旗下業務人員來開發企業客戶，因此整個行銷過程，如何滿足顧客的需求進而創造出產品服務的附加價值，為汽車租賃業永續經營及獲利之決勝關鍵。而在行銷過程中資源的配置為企業的一大問題，如何透過有效的資源配置及提升顧客滿意度與顧客忠誠度更是所有企業追求的目標。
長期租賃車業者在顧客承租車輛的過程中，可同步獲得顧客資料與交易紀錄，透過資料適當地處理、轉換及分析，可了解不同的顧客不同的特性，對於競爭激烈的汽車租賃業者提供適切的行銷資源。本研究將運用資料探勘中的群集化技術，對長期汽車租賃公司顧客進行分群與價值探討，分為三個步驟來進行：
第一步驟為依據RFM理論，對每一顧客進行評分，並計算每位顧客的MLE(Maximum Likelihood Estimation)、WMLE(Weighted Maximum Likelihood Estimation)等資料，藉此可瞭解每位顧客的活躍趨勢。第二步驟為將RFM、MLE及WMLE等資料，利用資料探勘的群集技術產生四組不同價值的顧客群。第三步驟則是利用分群的結果，依顧客屬性調整為為重量級、中量級、潛等級及被動級顧客，再針對各群顧客的特性探討，擬訂不同顧客群的行銷策略。
透過本研究不僅能瞭解顧客的屬性，同時期望能對長期汽車租賃車業區隔出不同價值的顧客群特性及偏好，透過客製化行銷來吸引保留顧客，與顧客建立長期的良好關係，同時並可精確地運用企業有限的人力、成本、時間等資源在行銷策略的制定與執行，以降低企業成本及增進營業利潤。

In the development of long-term car leasing marketing, the industry focuses on contents of services by providing financial services through automobile products. To develop corporation, customers are conducted mostly through the salesman of car dealers or car leasing companies. For the entire marketing process, the issue that how to satisfy the customer needs and thus create value added products and services is the successful key of both sustainability and profitability of the car leasing industry. The allocation of resources is a big problem for enterprises in the marketing process of car leasing industry. Allocating the resources effectively and making customer satisfied and loyal is a goal pursued by all enterprises.
From the process of leasing vehicles, a long-term car leasing company can access customer information and transaction records synchronously. By properly handling and analysing the data, a leasing company can understand different customers’ different characteristics and can provide appropriate marketing resources in the competitive car leasing industry. In order to investigate the customers and the value of clustering, this study is divided into three steps by using the cluster analysis of data mining.
The first step is based on RFM (Recency, Frequency and Monetary) theory by rating and calculating each customer's MLE (Maximum Likelihood Estimation) and WMLE (Weighted Maximum Likelihood Estimation) information to understand each customer's active trend. The second step is to use clustering technology of data mining to differentiate customers into four value groups by using their RFM, MLE and WMLE data. The third step is using the results of clustering, in accordance with customers’ attribute and adjusting them into the heavyweight-class, midweight-class, latent level and passive-class. Using each group of customer characteristics can help develop a specific marketing strategy.
Through this study, not only do we understand the customer's property, but also expect the long-term car leasing industry can distinguish the different value of different customer groups and to retain customers and to build established long-term adverse good relationships with the customers with customized marketing. At the same time, the case company can focus on target customers accurately with limited manpower, cost, time and other resources to develop and implement marketing strategies and to reduce costs and improve operating profit accordingly.

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