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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/52406


    Title: Analysis on repeat-buying patterns
    Other Titles: 重複購買行為分析
    Authors: 陳劭平;Chen, Shao-ping
    Contributors: 淡江大學資訊工程學系博士班
    蔣定安;Chiang, Ding-an
    Keywords: 序列型樣;週期挖掘;重複購買;時序資料挖掘;sequential pattern mining;Periodic Mining;Repeat-Buying;Temporal Data Mining
    Date: 2010
    Issue Date: 2010-09-23 17:36:27 (UTC+8)
    Abstract: 在商業環境裡,客戶是整個產品價值鏈最後付錢購買的人,因此對於客戶的理解,以及對於顧客價值管理與顧客貢獻度的研究,是企業獲利分析的最重要環節之一。消費品市場的特色包括產品項目多、顧客數目多、消費筆數多、在一定時間間隔內持續購買、消費金額不大、重複購買相同產品等;而且,不同的顧客群體,因為購買力不同,使用習慣不同,購買金額、購買間隔也會不同。因此,時間軸的資訊,是分析顧客消費行為的關鍵要素之一。本研究以時間性資料探勘技術(Temporal Data Mining),建立重複購買序列型樣的數學模型,將序列型樣中各事件的次序、間隔、頻率轉換為一個離散數值的數學結構。藉著分析函數的數學特性,找出實際消費行為的規律與變化關係,包括:是否有重複購買現象、重複購買是否有週期、反釓妎R同一項目次數、特定消費行為的延續時間等。透過數學模型的建構,結合產業知識 (industry know-how) ,得到更豐富、準確的關於顧客的知識。根據顧客消費行為的知識,企業經營者可以採取更有效率、更即時、更有針對性的行銷策略,以獲得最佳收益。
    Consumer market has several characteristics in common such as repeat-buying over the relevant time frame, a large number of customers, and a wealth of information detailing past customer purchases. Analyzing the characterizations of repeat-buying is necessary to understand and adapt to dynamics of customer behavior for company to survive in a continuously changing environment. The aim of this research is to develop a methodology to detect the existence of repeat-buying behavior and discover the potential period of the repeat-buying behavior. A mathematical model to capture the characteristics of repeat-buying behavior is devised. The algorithms based on our previous works then proposed to provide a scheme to discover periodicity and trends of the purchase. Two fundamental repeat-buying types has been identified and analyzed. Any repeat-buying scenarios can be expressed as the combination of the two fundamental types. The proposed mathematical model coupled with our works on repeat-buying modeling form a process to uncover the characteristics of repeat-buying phenomenon. Coupled with industry domain knowledge and marketers’ expertise, the constructed model helps to predict likely buying behavior, then the corresponding actions can be taken to maximize enterprise''s revenue.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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