以奇摩拍賣與露天拍賣消費行為的實徵資料預測買家流失

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陳志鴻(Zhi-hong Chen) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

以奇摩拍賣與露天拍賣消費行為的實徵資料預測買家流失

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摘要(中)

隨著資料探勘技術的普及，促使企業投入大數據分析，期望從中挖掘出有潛力的顧客及降低顧客流失率，以提升企業獲利。然而，在電子商務中，拍賣平台的低進入障礙，吸引大量對電子商務有興趣的業者進入，大幅提高同業間的競爭程度與潛在進入者威脅，因此賣家除了需要專注於回流的顧客也需要關注流失率，才能以更具成本效益的方式增加營收。為了實現這些潛在的利潤，賣家需要一個兼具效率與效益的預測工具，以掌握買家流失。本研究將流失定義為未來一段時間內，買家沒有出現再購行為時則稱為買家流失，並分別從賣家和平台的觀點進行買家流失的預測。
本研究以奇摩拍賣、露天拍賣為對象，耙取兩大拍賣平台網頁內容，蒐集2014年1月1日到2015年3月31日間全站分類的評價資料與商品資訊，透過實證資料建立一個線上拍賣買家流失預測模型。本研究利用敘述性統計解析買家消費行為與消費慣性，並透過六個預測變數包括RFM相關的四個變數和擴充的兩個變數來預測買家流失。結果發現從平台和賣家觀點具有下列特性的買家最容易流失，即最後一次交易時間間隔長、交易次數少、累計交易金額低、購買類別多樣性低、忠誠類型低。平均交易金額在平台和賣家的流失預測均顯著，但方向相反。本研究主要貢獻包括：（1）比較台灣兩大拍賣平台消費行為的差異性；（2）實務上可以幫助賣家找出可能流失的舊顧客；（3）實證資料的敘述統計結果可以作為後續相關研究的參考。

摘要(英)

With the popularity of data mining technology, companies are urged to invest in big data and try to improve their profits by digging out potential customers and reducing the churn rate. However, e-commerce auction platforms have low entry barriers, which attract a large number of sellers to enter so that both competition and the threat from potential entrants are higher. In order to increase revenues in a more cost-effective way, the seller need not only to focus on the returning customers, but also to reduce the churn rate. This study aims to establish an efficient and effective model to predict the churn of buyers. We define the buyer’s churn as the customer does not return and purchase for a certain period of time in the future. We will predict the churn from the perspectives of both the seller and the platform.
The dataset of this study is collected from Yahoo! Taiwan and Ruten auction websites, including all transactions and products information dated from January 1, 2014 to March 31, 2015. Through these empirical data, we establish a prediction model of the churn in auction platforms. We use descriptive statistics to show the consumer’s behavior and consumer inertia, and use six predictors, including 4 RFM-related and 2 augmented variables to predict the churn of buyers. Results show that the buyers are most likely to churn if they have longer last purchase interval, fewer transactions, lower total transaction amount, lower transaction diversity, and lower loyalty type. Average transaction amount is also significant but in reverse direction for the seller and the platform. Major contributions of this study includes: (1) comparing the consumer behavior of the two largest auction platforms in Taiwan; (2) helping sellers identify existing customers who are likely to churn; (3) the results of the descriptive statistics can be used as a reference for the future studies.

關鍵字(中)

★ 擴充的RFM模型
★ 消費慣性
★ 買家流失預測

關鍵字(英)

★ Augmented RFM Model
★ Consumer Inertia
★ Churn Prediction

論文目次

一、緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究問題與研究目的 4
1-4 論文架構 4
二、文獻探討 5
2-1 流失 5
2-2 資料庫行銷 5
2-3 買家RFM模型 6
2-4 消費慣性 7
三、研究方法 10
3-1 概念模型 10
3-2 假說推導與研究模型 12
3-2-1 買家RFM特性 12
3-2-2 買家消費慣性 14
3-3 研究設計 16
3-3-1 資料蒐集 20
3-4 資料分析 25
四、資料分析與結果 27
4-1 資料整理 27
4-1-1 樣本描述性統計 29
4-1-2 變數描述性統計 39
4-2 假說檢定 47
五、研究結論與建議 55
5-1 研究結論 55
5-2 研究貢獻 56
5-3管理意涵與實務意涵 57
5-4 研究限制 59
5-5未來發展 60
參考文獻 61

參考文獻

一、英文部分
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二、中文部分
[1]. 林鈺淵 (2014)。淘寶網商店街女裝B2C電子商務顧客之流失分析。國立中央大學資訊管理學系碩士論文。
[2]. 許景泰 (2014)。為何只有5%的人，網路開店賺到錢。三采出版社。
[3]. 陳慧玲 (2014)。線上消費者平台再購行為的 RFM 預測模型-以 Yahoo! 奇摩拍賣女裝為例。 Journal of Data Analysis, 9(1), 1-23。
[4]. 資策會 (2014)。 2013網路商店經營現況。資策會(MIC)AISP情報顧問服務資料庫。
[5]. 廖致淵 (2012)。線上消費者再購行為之預測-以Yahoo!奇摩拍賣女裝上衣為例。國立中央大學資訊管理學系碩士論文。

三、網頁部分
[1]. Custora. (2013). E-Commerce Customer Acquisition Snapshot.
Retrieved 2015-06-10, from
http://blog.custora.com/2013/06/e-commerce-customer-acquisition-snapshot/
[2]. Hughes. (2010). Quick profits with RFM analysis. Database Marketing Institute. Retrieved 2015-06-10, from http://www.dbmarketing.com/articles/Art149.htm
[3]. 國家發展委員會 (2015)。國情統計通報。
Retrieved 2015-06-10， from http://www.dgbas.gov.tw/public/Data/54291621278VQBV68D.pdf
[4]. 鷹眼數據 (2014)。2014Q4台灣購物網站排行榜。
Retrieved 2015-06-10， from http://www.inside.com.tw/2015/01/26/eagleeye-taiwan-ec-2014q4

指導教授

何靖遠、許秉瑜(Chin-yuan Ho Ping-yu Hsu)

審核日期

2015-7-24

推文