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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Title:	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
Authors:	陳志鴻;Chen,Zhi-hong
Contributors:	資訊管理學系
Keywords:	擴充的RFM模型;消費慣性;買家流失預測;Augmented RFM Model;Consumer Inertia;Churn Prediction
Date:	2015-07-24
Issue Date:	2015-09-23 14:25:49 (UTC+8)
Publisher:	國立中央大學
Abstract:	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。 ;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.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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