近年來，企業行銷理論從傳統的將注意力放在吸引新顧客上，轉而變成留意舊顧客的維持，如何留住舊顧客，進而從舊顧客中找出具有高價值的顧客，與他們建立長期且有獲利性的關係，則是近年來企業營運及行銷關注的焦點。 本研究使用了顧客知識管理管理來提升企業實行顧客終身價值管理之效益，並提供了顧客維持度、顧客貢獻度，顧客市場區隔三個維度來分析顧客，能更精確的指出關鍵客群，以「記憶體模組產業」作為研究對象，主要係在探討記憶體模組產業之顧客關係管理及顧客價值管理。 本研究透過次級資料收集與分析，以產業分析架構來了解DRAM模組產業現況及趨勢，並以Cambell顧客知識能力模型及SWOT 分析等策略理論將資料作歸納與整理，最後以深度訪談方式來加強或修正次級資料所得到之結論，得出適合個案公司之終端顧客之顧客知識管理作業流程及資料模型，以做為建議企業營運發展策略之參考。 最後，以資料庫結構面、企業流程面、顧客關係管理面及顧客知識管理面四個構面來歸納總結研究。導入顧客知識管理與顧客終身價值管理流程之目標，在於增進個體戶之顧客滿意度及忠誠度，進而提升整體顧客終身價值，另一方面，除了能提供更完善的顧問及技術諮詢的服務，亦能經由市場區隔之分析，使得產品銷售策略更符合市場需求。 / Recently, Business marketing theory is changed to the customer retention.It is important that find out and pay attention to key customers. Enterprises look out for to keep old customer and find out the customer with high value from the old customer.They focus on establishing long-term relation of having profitability with customers. The research used as an example of P campany in the case study.Based on the benefit of customer knowledge management and customer lifetime value,this study is mainly probed DRAM module industry’s customer ralation management and customer lifetime value .Then, it used customer’s retention degree, customer's contribution degree and market segmentation to analyse customers’ behavior. This research collected and analysed through the secondery materials and the analysis was based on industry analysis models such as Cambell’s customer knowledge ability model and the SWOT analysis model to analyze the pros and cons of DRAM industry’s trend and present situation.Then,the study strengthen or revise the conclusion by way of depth interview finally and get ideal end customer knowledge management workflow and data model of end customer ,regard it as the reference of enterprise's strategy. Above all, this sudy use four aspect such as database structue、enterprise’s workflow、CRM and CKM to summarize. The purpose of establishing the procedure of customer knowledge management and customer lifetime value is to improve the customer satisfaction and loyalty, and then emhance whole customer lifetime value.On the other hand, except to offer better services and consultation, Enterprises also can gain more revenue by market segementation and product position.

傳統上透過行銷研究，如設計問卷透過市場調查、問卷調查等等活動來取得消費者資訊，但在資訊化的時代，利用POS系統所收集到且存在於資料庫中的銷售資料，忠實紀錄所有消費者的消費行為，其中亦隱含著消費者對品牌忠誠度、產品偏好性、價格敏感度等等消費行為。藉由著這些基礎，可進一步探索顧客輪廓(Customer Profile)、顧客忠誠度(Customer Loyalty)及顧客保留率(Customer Retention)，同時預測未來各種行銷活動的可能結果；進而擴大其應用範圍，進行新客源、新產品的開發工作。本研究以資料庫行銷、資料探勘等等理論基礎、統計學技術等等方法，以居家用品業為例，進行資料庫行銷(Database Marketing)的實證工作。實證資料來源為「生活工場」連鎖居家用品店現有之基本資料。並以此資料為基礎，探究顧客購買的消費特性，並以其類別、系列分析其產品關聯性審視其產品組合類別。另外一方面，利用資料庫中的銷售資料及VIP基本資料，以RFM(Recency, Frequency, Monetary)顧客分群方法，分離出不同的顧客族群，並在每一個族群中作交差比對(Cross-Checking)，探究各族群的顧客輪廓及其消費特徵。利用門市基本資料、銷售明細資料等，分析出地域消費特徵。最後，再以金卡、貴賓卡、生活卡三大族群的消費特性及與RFM族群之交差比對，並計算出不同卡別的顧客終身價值(Customer Lifetime Value, CLV)，以期提供「生活工場」與國內連鎖零售體系之產業，未來在對顧客關係經營之參考。 / In tradition, marketing research acquires the information of consumers through questionnaire design, marketing survey, etc. However, in the information age, the POS system is capable to collect the sales information and record them in the data base. It precisely records the consumer behaviors which include the brand loyalty, product preference and price sensitivity. Based on the information, it could more deeply discover the customer profile, customer loyalty and customer retention. It can also forecast the possibility of marketing event for the future and expand to other applications such as new customer creation and new product development. This research, based on the theory of database marketing, data mining methodology and statistic technology, substantiates database marketing in a case study of a home center. Actual data are provided by Working House, a home center chain store. Those data bases establish a foundation of exploring the consumer purchasing characteristic, analyzing series of product classifications and examining the product association and combination. On the other hand, based on the sales information and VIP database, RFM (Recency, Frequency, and Monetary) is utilized to cluster customer segments and research the customer profiling and shopping characteristic by cross checking each shopping group. Data from store locations and sales information are thus employed to explore the geographic characteristic of shoppers. In conclusion, based on the usage data of Gold Card, VIP Card, and Working House Card and cross checks with RFM clustering groups, the Customer Lifetime Value (CLV) of each card will be calculated and accumulated to provide Working House and other retailers the reference for managing customer relationship in the future.