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研究生:胡天豪
研究生(外文):HU, TIAN-HAO
論文名稱:以RFM模型建立線上零售業之客戶族群分析框架
論文名稱(外文):RFM-based Customer Segmentation and Analytical Framework for Online Retailing
指導教授:吳怡瑾吳怡瑾引用關係
指導教授(外文):WU, I-CHIN
口試委員:唐牧群盧宏益
口試委員(外文):TANG, MU-QUNLU,HONG-YI
口試日期:2017-06-16
學位類別:碩士
校院名稱:輔仁大學
系所名稱:國際經營管理碩士學位學程
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:63
中文關鍵詞:線上購物資料探勘RFM顧客模型購買行為關聯規則
外文關鍵詞:Online shoppingData miningRFM customer modelPurchase behaviorAssociation rules
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21世紀是信息時代,隨著越來越多的人開始接觸網路并使用智能手機,網購成為流行的購買方式之一。全球零售銷售額在2016年達到22萬億,同時線上零售銷售額達到2萬億,佔據總零售銷售額的8.7%并向15%發起衝擊。其中,中國在最近幾年刮起了電子商務的風潮,以阿里巴巴為首的電子商務公司紛紛站了出來,為世界的線上零售業作出非凡的貢獻。故而電商公司的運營和行銷策略對於新到的電商公司而言無疑是非常重要的。在本研究中,我們決定採用RFM模式來建立不同顧客族群,并嘗試使用資料探勘技術來分析不同族群的購買行為,在本研究中主要為忠誠顧客和可追蹤新顧客。本研究主要目的為(1)使用資料探勘技術來提取可用的顧客資料和訂單資料;(2)利用RFM顧客模型來歸類同類型顧客并定義其形態;(3)建立決策樹並進一步使用J48分類法來分析不同顧客群組購買行為。本研究透過資料探勘分析方法所找出之顯著顧客購買行為,經飛牛網電子商務公司表示分類規則結果有助於其顧客運營管理,未來將結合本研究成果繼續挖掘顧客資料數據,提出有效的商業決策。由於飛牛網電子商務公司在中國大陸地區擁有較好的知名度并擁有較多的顧客數量,所以資料擁有一定程度的代表性,研究結果可提供給其他電子商務公司作為制定商業決策的參考。
Global retail sales reached $22 trillion in 2016, while online retail sales reached $2 trillion, and China is the world's largest retail electronics business market, are already making extraordinary contributions to global online retailing. In this research, We established the RFM model to build different customer groups and used data mining techniques to analyze the buying behavior of these different customer groups. The main purpose of this study is to: (1) Use data mining technology to collect available customer data and order data; (2) Utilize the RFM customer model to classify customers of the same type and define these customer groups; (3) Establish a decision tree and use J48 classification to analyze different customer group buying behaviors. In this study, the results of the classification rules that were found by the data mining and analysis methods were approved by Feiniu.com. The results of the classification rules will help customer operation and management. In the future, we will continue to deeply search customer data and combine it with the research results to make effective marketing decisions.
摘要 5
Abstract 6
Chapter 1 Introduction 8
1.1 Background 8
1.2 Feiniu.com 10
1.3 Research Motivation and Objective 12
Chapter 2 Literature Review 14
2.1 Data Mining Techniques 14
2.2 Customer Segmentation 18
Chapter 3 Research Methodology 24
3.1 Research Questions and the Framework 24
3.2 Data Preprocessing 28
Chapter 4 RFM-Based Segmentation Behavior Analysis 36
4.1 RFM-based Segmentation Method 36
4.2 RFM-based Behavior Analysis 39
Chapter 5 Data Analysis Results 40
5.1 Data Segmentation Results 41
5.2 Data Mining Results for Specific Groups 43
Chapter 6 Conclusions 50
References 53
Appendices 56

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