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.
