關係行銷或是一對一行銷是目前行銷領域上廣泛被討論的議 題，企業要如何透過有效的辨識、區隔、互動以及客制化來量身打造 顧客專屬的個人化產品與服務內容，並強化其重複消費動機及忠誠， 為目前各種產業爭相積極追求的目標，此外，由於微利時代風暴，各 產業無不希望透過顧客價值的辨識與經營，實現以更有效、更低的成 本的差異化行銷策略來創造高收益的企業經營目標，以航空產業如此 資本密集，高固定成本，低變動成本以及不對稱的供需平衡，誰掌握 低成本領導與差異化策略優勢，便能決戰存續於二十一世紀超競爭時 代之中。 由於資訊科技、網際網路以及資料探勘技術的臻於成熟， 充份 發揮了跨國、即時、深度滲透與互動的特性，使得關係行銷、一對一 行銷的實現變得更加有效而可行。本研究希望從顧客價值的認定、顧 客忠誠策略以及資料探勘技術的探討，來思考如何運用於航空公司會 員系統的顧客區隔，同時，希能透過航空公司產業通路架構、全球旅 行社訂位系統(CRS)的發展現狀、微妙的航空公司間策略聯盟以及不 同航空公司所提供的會員酬賓計劃內容的探討與陳述，初略地對個案 公司的所在環境進行策略性分析，以建議其所需採取投入關係行銷的 主要焦點客層。 緊接著， 利用資料探勘工具中的分群技術， 選定有效的指標變 數，針對某一區間的會員交易資料進行分群，藉由研究各群會員所蘊 含的特殊屬性，如營收貢獻、產品特性、通路喜好以及消費行為等等， 依據前述所定義的目標客層，以創造顧客價值為目標，精確建立目標 客戶群，並據以設計不同的行銷策略與產品組合，逐步深耕建立完整 會員關係行銷資料庫。 最後， 對於本研究所無法觸及的研究議題， 概略指出後續可能 的研究方向與建議。 / Customer Relationship Management and data mining in this hyper-competitive era have revealed a lot of interesting and innovative opportunities to enrich the capability of company to realize and provide customer value. They touch the most critical issue of the enterprise, “How can we create and sustain successful advantage, and maximize profitability by leveraging new technologies ?＂In this thesis, we will focus on the application of data mining in the FFP of the airlines industry, and look over the differences among FFP members to discover the implicative needs of FFP customers. First of all, we start discussion on literature review in chapter two, which was divided into three parts: customer loyalty strategy, customer value and data mining. In this chapter, we put emphasis on the concepts and definitions of above topics, and they would be helpful to us to select and decide key variables in the following data mining practice. Chapter three of this thesis is to introduce the structure and characteristics of the airlines industry, the history of Computerized Reservation System(CRS), the airlines strategy alliance and the FFP system, and to figure out the way to understand the existing threats and opportunities. Chapter four, which was abode by the steps of data mining process, defines business issues and collects around one year's FFP historical transaction data to establish the target data and perform an actual data mining practice. In this real practice, we use the demographic cluster function of IBM Intelligent Mining tool to do member clustering. We select net revenue, first and business class spending rate, reservation booking designator and customer activation as analytical variables to perform FFP member clustering. Each variable has been well equipped with weight and method to produce best cluster pattern. Finally, according to the mining results we have explored and interpreted, we provide our draft recommendations about marketing planning and mix activities from the perspectives of FFP members clustering.