博碩士論文 101423054 詳細資訊

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姓名 余芷函(Chih-han Yu)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 滾動式RFM基礎的線上再購行為預測模型 ─以台灣Yahoo!奇摩拍賣女裝分類為例
(A Rolling RFM-based Prediction Model of Online Repurchase Behavior: A Case of Women′s Apparel at Yahoo! Taiwan Auction Website)
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摘要(中) 隨著網路購物的快速成長,企業對顧客電子商務受到實務界和學者更多的重視。線上賣家有更多機會接觸到線上消費者,同時消費者在網路購物也有更多的選擇。線上賣家必須專注於回流的顧客才能以更具成本效益的方式增加營收。要實現這些潛在的利潤,線上賣家需要一個兼具效率和效益的預測工具來掌握其顧客的購買行為。以Yahoo!奇摩拍賣女裝分類為目標,本研究運用真實交易資料建立了一個兼具效果穩定且結果準確的滾動式線上再購行為預測模型。
摘要(英) Online shopping has grown rapidly so that B2C e-commerce gets more attention by both practitioners and researchers. While the seller has more opportunities to reach more online consumers, the online shopper has more choices as well. By focusing on returning customers, online sellers can increase revenues in a more cost-effective way. To realize the potential profits, online sellers need an efficient and effective prediction tool to capture their customers’ purchase behavior. Targeting on the woman apparel at Yahoo! Taiwan auction website, this study uses the real transaction data to develop a rolling prediction model of the online repurchase behavior, which exhibits both stability and prediction accuracy.
The dataset collected from Yahoo! Taiwan auction website includes all transaction data dated before September 30, 2013 and the total number of transaction records is over 5.58 million. Based on this rich dataset, we applied a comprehensive description statistics to observe characteristics of repeat customers. We also propose a rolling repurchase behavior prediction model with up to six independent variables, including RFM (recency, frequency, total/average monetary), the last rating and the number of repurchased sellers. Classification rates of different time points and time intervals used in prediction were examined to validate the model. Through tests of goodness of model fit and logistic regression analysis, we found that the recency and the average monetary are negatively related to the probability of repurchase, whereas the higher the frequency, the total monetary, the last rating, and the number of repurchased sellers, the repurchase is more likely to occur. Only the result of the number of repurchased sellers is contradictory to our hypothesis. The contribution of this study has three: (1) practically help online sellers with target marketing to retain old customers; (2) augment the RFM model with the last rating and the number of repurchased sellers can enhance prediction accuracy effectively; (3) the description statistics based on all real transactions can be a reference for online shoppers’ behavior research.
關鍵字(中) ★ 再購行為
★ RFM模型
★ 網路購物
★ 滾動式預測
關鍵字(英) ★ Repurchase Behavior
★ RFM Model
★ Online Shopping
★ Rolling Forecast
論文目次 摘要 i
Abstract ii
誌謝 iii
Contents iv
List of Tables v
List of Figures vi
1 Introduction 1
1.1 Research Background and Motivations 1
1.2 Research Purposes and Questions 4
2 Literature Review 6
2.1 Online shopping 6
2.2 Consumer Behavior 8
2.3 Loyalty and Online Repurchase Behavior 10
2.4 RFM Model 11
3 Research Methodology 13
3.1 Research Model and Hypotheses 13
3.2 Research Design 16
3.2.1 Data Collection 16
3.2.2 Data Crawling Process 18
4 Data Analysis and Results 21
4.1 Description Statistics 21
4.2 Logistic Regression Analysis 28
5 Conclusion 37
5.1 Research Conclusion 37
5.2 Contributions 40
5.3 Managerial Implications 40
5.4 Limitations and Future Research 41
References 42
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資策會(2012),2012 ICT產業白皮書(下)-顯示/消費性電子/軟體服務,財團法人資訊工業策進會產業情報研究所MIC出版。
資策會(2013),2013台灣網友網路購物行為調查,資策會(MIC) AISP情報顧問服務資料庫。
資策會(2013),台灣網友網路購物消費趨勢分析,資策會(MIC) AISP情報顧問服務資料庫。
何靖遠、廖致淵、許文錦(2013),Yahoo!奇摩拍賣女裝上衣消費者再購行為之預測,2013 ERP國際學術暨實務研討會,台北,銘傳大學。
指導教授 何靖遠(Chin-Yuan Ho) 審核日期 2014-7-10
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