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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/72231
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/72231


    Title: 資料採礦流程在顧客關係管理中之應用
    Other Titles: Data Mining Process for Customer Relationship Management
    Authors: 鄭宇庭;夏學理
    Cheng, Yu-Ting;Hsia, Jerry H.
    Contributors: 統計系
    Keywords: 資料採礦流程;跨行業資料採礦標準流程;顧客關係管理;Data Mining Process;Cross Industry Standard Process for Data Mining;Customer Relationship Management
    Date: 2008-02
    Issue Date: 2014-12-23 15:20:07 (UTC+8)
    Abstract: 自從人類有商業行為以來,顧客就是所有交易關係中最基礎而直接的元素。過去由於企業掌握較多的市場訊息,但隨著資訊科技的進步以及電子化時代的來臨,現在的顧客已經可以從多種管道獲得豐富的產品資訊。所以企業若欲維持營運與獲利成長,便不得不善用顧客關係管理,去瞭解顧客、為顧客量身訂作客制化產品、維繫顧客忠誠度及和顧客建立良好的互動關係。又唯有透過資料採礦去分析顧客資料,從中學習顧客的行為模式、瞭解顧客的喜好,繼而提供顧客需要的產品或服務,才可增加顧客的忠誠度,並為企業帶來豐厚的利潤。本研究的目的即在針對資料採礦在顧客關係管理中的實施流程進行討論。除探討相關理論及應用,發展出一套適用的系統化流程外,亦利用一原始資料,嘗試運用所發展出之系統化流程進行實作,提供企業一整合性的解決方案。本研究從顧客關係管理的角度出發,提出一個新的資料採礦流程,將此流程分成了定義商業問題、資料暸解、資料前置處理、資料抽樣、建立模型、評估與擴展模型等七個步驟,整個流程透過資料的傳遞和結果的回饋成為一個迴圈,隨著資料採礦的不斷運作,企業知識亦可以不斷累積。而本流程的重點將著重在真正開始進行資料採礦前的資料準備作業。
    Customer is the most fundamental and direct element of business activities. By the progress of informational technology, customers can now access plentiful product information through various channels. It is then only through customer relationship management and customer data analysis: that is to understand customers, to produce customized products, to maintain customers' loyalty, and to keep good interaction with customers, can enterprises maintain business operating and gain profit.The purpose of this research is to explore a suitable systematic procedure of the data mining application to customer relationship management through related theory and application study. A proposed integrated solution is also expected by the application of this systematic procedure through a raw data study.From customer relationship management perspective, a new data mining procedure, CRISP-DM, is proposed with construction of seven steps: business understanding, data understanding, data preparation, sampling, model building, evaluation and deployment. A cycle is formed by continuously data transmitting and information feedback throughout the procedure which accumulate customer knowledge of enterprises. CRISP-DM procedure places more emphasis on the data preparation before conducting formal data mining fields work.
    Relation: Journal of Data Analysis, Vol.3, No.1, 167-174
    Data Type: article
    Appears in Collections:[統計學系] 期刊論文

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