淘寶網商店街女裝B2C電子商務顧客之流失分析

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：79

、訪客IP：3.137.223.190

姓名

林鈺淵(Yu-yuan Lin) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

淘寶網商店街女裝B2C電子商務顧客之流失分析
(A Churn Analysis of Business-to-Customer e-Commerce of Women′s Apparel at Taobao Shop Street)

相關論文

★ 決策的本質-由古巴危機談台灣E銀行危機	★ 女性主管領導力之研究
★ 以策略聯盟快速開發新產品-以網路附接儲存器為例	★ LED產業策略性存貨管理之研究-以LED領導廠商為例
★ 應用結構行動理論探討跨國企業導入供應鏈管理之個案研究-以資訊電子業為例	★ 應用調適性結構行動理論探討ERP卅MES系統導入、轉移和整合之個案研究
★ LCD面板製造廠資訊系統商業價值之個案研究	★ 應用調適性結構行動理論探討CIM系統的導入 -以TFT-LCD產業為例
★ ERP系統品質Enhancement的實徵研究	★ 以資訊處理理論探討出貨管理系統在TFT-LCD產業的導入及影響之個案研究
★ 連接器供應商於中國大陸地區導入出貨管理系統之個案研究	★ 以AHP法探討跨國企業評選固網供應商之決策準則
★ 工具機製造業導入協作式接單服務之探討--以沖床製造廠商為例	★ 製造業導入先進規劃與排程系統之探討—以筆電領導廠商為例
★ 經銷商管理的再造－台灣知名飲料業的個案研究	★ 運用精實六標準差手法改善資料品質─某TFT-LCD業者之個案研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

隨著B2C電子商務的普及,網路購物已經成為業者的兵家必爭之地。線上賣家必須透過商品和服務的品質以留住舊顧客並開發新顧客,線上買家只能透過賣家和平台業者所公開的資訊作為選擇交易對象以及購買決策的依據。一般而言,獲取一位新顧客是留住一位舊顧客成本的五倍,因此避免顧客流失為現今網路賣家的一個重要課題。本研究的目的在藉由購物網站之實際交易資料分析,找出顧客流失的影響因子,進而評估顧客流失的可能性。
本研究使用網頁內容資料探勘的方式,蒐集淘寶網店舖街中的女裝店舖賣家在2013年1月1日至4月30日期間每日的賣家相關公開資訊。我們以線上顧客消費行為和線上賣家之特徵與經營績效兩類的變項,建立一個B2C電子商務顧客流失的預測模型。線上顧客消費行為採用RFM的模型,包括最近交易間隔天數、累積交易次數、以及平均交易金額等三個變項。線上賣家的變項包括評價分數、收藏人氣、商品販售種類、主營占比、服務評價、與回購率等共六個變項。運用模型適配度檢定和羅吉斯迴歸分析,研究發現除了RFM三個變項外,亦有其他的變項和顧客的流失率顯著相關,是網路賣家不容忽視的。因本研究建立之模型可以有效地預測線上消費者的流失行為,對於網路購物之平台業者或賣家,可以提供有效的經營策略之指引方針,藉此設法留住顧客,達到更大的獲利。

摘要(英)

With the popularity of B2C e-commerce, online shopping has become an important issue. Online sellers of goods and services must be through the quality to retain old customers and develop new customers, online buyers only as a selection of trading partners and purchasing decisions through information which platform provider seller disclosed. Generally, the cost of obtaining a new customer is five times as large as the cost of retaining an old customer, thus avoiding the churn of customers is an important issue nowadays for Internet sellers. The purpose of this study is to identify influencing factors of the churn of customers by the actual transaction data analysis for shopping website, and then assess the likelihood of churn of customers.
Using web content mining technique, we collected real data of seller′s information from dress shop in Taobao shop street during the January 1, 2013 to April 30, 2013.We consider that online consumer behavior and online seller’s operating performance are two variables which we used to build a predictive model for B2C e-commerce customer’s churn. Online consumer behavior using the RFM variables, including recent trading interval, the cumulative number of transactions and average transaction amount. The variables of online seller, including the rating score, collectibles popularity, assortment size…etc. Using logistic regression analysis, the study found that in addition to the three RFM variables, and there are other variables significantly associated with customer churn rate, the network seller cannot be ignored. Because the model established in this study can effectively predict the churn of the online consumers. On the other hand, this study can provide effective business strategies for Internet shopping platform provider to retain customers and to achieve greater profitability .

關鍵字(中)

★ 顧客流失率
★ RFM模型
★ 賣家服務品質
★ 網路購物

關鍵字(英)

★ RFM model
★ Churn Rate
★ Online shopping

論文目次

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
第一章　緒論 1
1-1　研究背景與動機 1
1-2　研究目的與研究問題 4
1-3　論文架構 4
第二章　文獻探討 6
2-1　顧客關係管理 6
2-2　賣家服務品質 7
2-3　評價分數 10
2-4　RFM分析法 11
2-5　忠誠度相關的營運績效 12
2-6　流失率 13
2-7　從眾效應 14
第三章　研究方法 15
3-1　研究模型與假說推導 15
3-2　研究設計 22
3-3　資料分析 28
第四章　資料分析與結果 30
4-1　樣本資料 30
4-2　假說檢定 44
4-3　小結 50
第五章　研究結論與建議 51
5-1　研究結論 51
5-2　研究貢獻 52
5-4　研究限制 54
5-5　未來發展 54
參考文獻 55

參考文獻

Anderson, R. E. and Srinivasan, S. S. (2003), “E-satisfaction and e-loyalty: A contingency framework”, Psychology and Marketing, 20(2), 123-138.
Ariely, D. and Norton, M. I. (2008), “How actions create-not just reveal-preferences”, Trends in cognitive sciences, 12(1), 13-16.
Ba, S. and Pavlou, P. A. (2002), “Evidence of the effect of trust in electronic markets: Price premiums and buyer behavior”, MIS Quarterly, 26(3), 243-268.
Bergen, M., Dutta, S. and Shugan, S. (1996), “Branded variants: a retail perspective”, Journal of Marketing Research, 33(1), 9 -19.
Berger, J., Draganska, M. and Simonson, I. (2007), “The Influence of Product Variety on Brand Perception and Choice”, Marketing Science, 26(4), 460-72.
Bhatia, A. (1999), Customer Relationship Management, 1st ed. U.S.A.;Don Hull.
Bolton, R. N. and Katherine, N. L. (1999), “A Dynamic Model of Customers’ Usage of Services: Usage as an Antecedent and Consequence of Satisfaction”, Journal of Marketing Research. 36(2), 171-186.
Buckinx, W. and Van den Poel, D. (2005), “Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting”, European Journal of Operational Research, 164, 252–268.
Chan, C. C. (2005), “Online auction customer segmentation using a neural network model”, International Journal of Applied Science and Engineering, 3(2), 101-109.
Chen, S. C. and Cao, Z. P. (2008), “Store Choice for Uni- and Multi- loyal customers: A Comparative Study”, Proceedings of the Australian and New Zealand Marketing Academy Conference, Sydney, Australia.
Chen, Y. L., Kuo, M. H., Wu, S. Y. and Tang, K. (2009), “Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data”, Electronic Commerce Research and Applications, 8(5), 241-251.
Cheng, C. H. and Chen, Y. S. (2009), “Classifying the segmentation of customer value via RFM model and RS theory”, Expert Systems with Applications, 36(3),4176-4184.
Erdem, T. and Swait, J. (2004), “Brand credibility, brand consideration, and choice”, Journal of Consumer Research, 31(1), 191-198.
Ganesh, J., Arnold, M. J. and Reynolds, K. E. (2000), “Understanding the Customer Base of Service Providers: An Examination of the Differences Between Switchers and Stayers”, Journal of Marketing, 64, 65-87.
Gilkeson, J. H. and Reynolds, K. (2003), “Determinants of internet auction success and closing price: an exploratory study”, Psychology & Marketing, 20(6), 537-566.
Gommans, M., Krishman, K. S. and Scheffole, K. B. (2001), “From brand loyalty to e-loyalty: a conceptual framework”, Journal of Economic and Social Research, 3(1), 43-59.
Gruen, T. W., Osmonbekov, T. and Czaplewski, A. J. (2005), “eWOM: The impact of customer-to-customer online know-how exchange on customer value and loyalty”, Journal of Business Research, 59(4), 449-456.
Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010), Multivariate Data Analysis, New Jersey: PEARSON.
Heskett, J. L., Sasser, W. E. and Hart, C. W. (1989), Service breakthroughs: Changing the rules of the game, New York: The Free Press.
Homburg, C., Droll, M. and Totzek, D. (2008), “Customer prioritization: does it pay off, and how should it be implemented?” Journal of Marketing, 72(5), 110-130.
Howard, J. A. and Sheth, J. N. (1969), The Theory of Buyer Behavior, New York: John Wiley & Sons.
Hsieh, N. C. (2004), “An integrated data mining and behavioral scoring model for analyzing bank customers”, Expert Systems with Applications, 27(4), 623-633.
Hughes, A. M. (1994), Strategic database marketing, Probus Publishing, Chicago.
Jacoby, J. and Kyner, D.B. (1973), “Brand loyalty vs. repeat purchasing behavior”, Journal of Marketing Research, 10(1), 1-9.
Jones, T. O. and W. E. Sasser, Jr. (1995), “Why Satisfied Customers Defect”, Harvard Business Review, 73(6), 88-99.
Jonker, J. J., Piersma, N. and Potharst, R. (2006), “A decision support system for direct mailing decisions”, Decision Support Systems, 42(2), 915-925.
Kahn, B. and Wansink, B. (2004), “The influence of assortment structure on perceived variety and consumption quantities”, Journal of Consumer Research, 30(5), 81-96.
Kalakota, R. and Robinson, M. (1999), E-Business: Roadmap for Success, 1st ed.,U.S.A.: Mary T. O’Brien.
Kauffman, R. J. and Wood, C. A. (2006), “Doing their bidding: An empirical examination of factors that affect a buyer′s utility in Internet auctions”, Information Technology and Management, 7(3), 171-190.
Keaveney, S. M. (1995), “Customer Switching Behaviour in Service Industries: An Exploratory Study”, Journal of Marketing, 59, 71-82.
Kim, D. J., Ferrin, D. L. and Rao, H. R. (2009), “Trust and Satisfaction, Two Stepping Stones for Successful E-Commerce Relationships: A Longitudinal Exploration”, Information Systems Research, 20(2), 237-257.
Kim, S. Y., Jung, T. S., Suh, E. H. and Hwang, H. S. (2006), “Customer segmentation and strategy development based on customer lifetime value: A case study”, Expert Systems with Applications, 31(1), 101-107.
Kotler, P. (1967), Marketing Management: Analysis, Planning and Control , Englewood Cliffs, N.J., Prentice-Hall.
Lascu, D. N. and Zinkhan, G. (1999), “Consumer conformity: Review and applications for marketing theory and practice”, Journal of Marketing Theory and Practice, 7(3), 1-12.
Liu, D. R. and Shih, Y. Y. (2005), “Integrating AHP and data mining for product recommendation based on customer lifetime value”, Information and Management, 42(3), 387-400.
Madden, G. G., Scott, S. J. and Coble-Neal, G. (1999), “Subscriber Churn in the Australian ISP Market”, Information Economics and Policy, 11, 195-207.
Mattersion, R. (2001), Telecom churn management. Fuquay-Varina, NC:APDG Publishing.
Nedungadi, P. (1990), “Recall and consumer consideration sets: influencing choice without altering brand evaluations”, Journal of Consumer Research, 17(3), 263-276.
Parasuraman, A., Zeithaml, V. A. and Berry, L. L. (1985), “A conceptual model of service quality and its implications for future research”, Journal of Marketing, 49(4), 41-50.
Posseltl, T. and Gerstner, E. (2005), “Pre-sale vs. Post-sale e-satisfaction: Impact on repurchase intention and overall satisfaction”, Journal of Interactive Marketing, 19(4), 35-47.
Paul, P., Pennell, M. L. and Lemeshow, S. (2012), “Standardizing the power of the Hosmer-Lemeshow goodness of fit test in large data sets”, Statistics in Medicine, 32(1), 67-80.
Reichheld, F. F. and Sasser W. E. (1990), “Zero Defections：Quality Comes to Services”, Harvard Business Review, 68(5), 105-11.
Reichheld, F.F. and Schefter, P. (2000), “E-loyalty: your secret weapon on the web”, Harvard Business Review, 78(4), 105-113.
Sheth, J.N., Mittal, B. and Newman, B. (1999), Customer behavior: consumer behavior and beyond, Texas: Dryden Press.
Srinivasan, S. S., Anderson, R. and Ponnavolu, K. (2000), “Customer loyalty in E-commerce: An exploration of its antecedents and consequences”, Journal of Retailing, 78(1), 41-50.
Van Den Poel, D. and Lariviere, B. (2005), “Predicting customer retention and profitability by using random forests and regression forests techniques”, Expert Systems with Applications, 29, 472-484.
Weinberg, B. D. and Davis, L. (2005), “Exploring the WOW in online-auction feedback”, Journal of Business Research, 58(11), 1609-1621.
Zeithaml, V. A., Parasuraman, A. and Malhotra, A. (2002), “Service quality delivery through web sites: A critical review of extant knowledge”, Journal of the Academy of Marketing Science, 30(4), 362-410.
呂彥德（2011），「收藏人氣對線上賣家服務品質與購買人數關係之中介影響-以淘寶網女裝店舖為例」，國立中央大學，碩士論文。
何靖遠、賴宜楓（2012），「線上消費者流失行為的實徵研究」, 《電子商務學報》，14(2)，307-328。
何靖遠、廖致淵、許文錦（2012），「Yahoo!奇摩拍賣女裝上衣消費者流失行為之預測」, 《2013 ERP國際學術暨實務研討會》，銘傳大學，台北市。
袁于涵、謝建騰（2010），「婚後女性服裝消費地圖之研究」, 《第26屆纖維紡織科技研討會》，嶺東科技大學，台中市。
中國互聯網絡信息中心（China Internet Network Information Center, CNNIC）：2012年中国网络购物市场研究报告。2013年3月，取自http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/dzswbg/201304/t20130417_39290.htm。
中國電子商務研究中心：2013年(上)中國電子商務市場數據監測報告。2013年8月28日，取自http://www.100ec.cn/zt/2010bgdz/。

指導教授

何靖遠(Chin-yuan Ho)

審核日期

2014-7-9

推文