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研究生(外文):Ai-Lun Chiu
論文名稱(外文):Application of Data Mining Techniques to Detect the Changes of Customer Behavior
指導教授(外文):Mu-Chen Chen
外文關鍵詞:Data MininigAssociation RuleCustomer Behavior RulesRule MatchingRFM
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根據80/20 法則,企業中80%收益,來自於20%的客戶,且開發一個新顧客的成本,高於維持住一個顧客的5倍,因此如何應用有效的顧客關係管理,維持住對企業最有價值的顧客,成為在變動時代的今天,最重要的議題。而要維持顧客,最重要的是要了解消費者行為,並針對其消費行為改變,做出即時且適當的回應,以滿足顧客的需求。過去在消費者的行為研究上,普遍藉由統計分析來分析消費者行為,然而,統計分析需符合許多假設,例如:資料需為常態,且資料的收集僅只於特定樣本。反之,在電腦化的時代,企業的顧客及交易資料庫中,其實蘊藏了許多有用的知識,因此,本研究運用資料探勘 (Data Mining)技術,挖掘資料庫中有用的資訊;資料探勘技術,不需符合傳統統計假設,且所收集大量且為每日的交易資料,因較適合分析顧客行為改變。因此,本研究即針對顧客及交易資料庫,透過資料轉換技術,以顧客行為變數區別對企業不同價值的市場區隔,並透過資料探勘中之關連法則 (Association Rule),挖掘出不同期間下之顧客消費行為規則,再藉由規則比較指標之計算,以偵測及預測消費行為改變;最後,本研究建構一線上之消費行為改變查詢系統,以即時回饋所得之消費者行為及行為改變規則。
In general, 80 percent of profit is generated by 20 percent of customers in business (80/20 rule). Therefore, building up an effective relationship with the valuable customers has become more necessary for businesses to remain the competitive advantage. Hence, to detect and to predict the customer behaviors are important issues to maintain a successful relationship with customers. Further, detecting the change of customer behavior makes companies to actively intervene and timely win customers.
From the previous studies of consumer behavior, questionnaire is the most conventional manner for detecting the buying behavior of customer. Furthermore, several statistical methods such as Bayesian theory and principal component analysis (PCA) were also applied to model the consumer behavior. Even though these approaches can be applied to predict the customer behavior, there still have several limitations. For instance, the parameter distribution must be normal. Hence, data mining techniques, which do not have to make any preliminary or additional assumptions for data have been proposed to model the association structures of customer behavior.
This study attempts to apply the association rule mining to find the relationship between customer profile and products bought by customers. Then, to model the change of customer behavior by compare to these rules, which are generated from different periods of time. Consequently, the rule matching measures are developed to detect the change of customer behaviors. Finally, an on-line query system was developed to display the results of customer behavior rules.
摘要 iii
Abstract iv
表目錄 vii
圖目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 3
1.4 研究流程與架構 3
第二章 文獻探討 6
2.1 顧客關係管理 6
2.2 消費者行為 9
2.2.1 消費者行為研究 10
2.2.2 消費者行為改變之相關研究 14
2.3 顧客行為變數 15
2.4 資料探勘 18
2.5 關聯法則 21
第三章 探勘消費行為改變方法 24
3.1 概述 24
3.2 資料前處理及轉換 25
3.3 顧客行為變數 27
3.4 關聯法則演算法 33
3.4.1 演算法流程說明 33
3.4.2 簡例說明 37
3.5 消費行為規則比較 39
3.5.1 問題定義 40
3.5.2 消費者行為改變指標 42
第四章 實作結果 54
4.1 資料來源與資料選取 54
4.2 資料前處理與轉換 56
4.3 顧客價值分群 60
4.4 關聯法則分析 65
4.5 消費者行為規則改變 68
4.6 消費行為改變之線上查詢系統 74
第五章 結論與未來研究建議 84
5.1 研究結果 84
5.2 未來研究建議 85
參考文獻 87
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