在台灣61.4%的網路族群有網路購物的經驗，只有28.6%的線上賣家已經獲利。因此，瞭解線上消費者的購買與再購行為，有助於電子商務業者找出潛在的回流顧客，進而降低成本、增加營收。行銷領域的RFM模型已經被廣泛地應用在消費者行為的實務和研究，本研究進一步考量拍賣平台所提供的公開資訊，包括評價、已上架時間、已購買次數、問與答筆數等，以建立一個以RFM模型為基礎的線上消費者再購行為影響因素的研究模型。本研究使用網頁內容探勘的方式，蒐集台灣露天拍賣中流行女裝類別商品在 2012/4/1~2012/5/31期間的真實交易資料，並以邏輯斯迴歸進行資料分析。其結果發現RFM模型與上次交易買家給予賣家的評價對於再購均有顯著的影響。本研究的管理和實務意涵對於網購平台業者和線上賣家有非常務實的建議。With 61.4% Internet users have ever purchased from online stores, only 28.6% online stores are making a profit. Therefore, understanding the purchase and repurchase behaviors of online consumers can help e-commerce businesses to identify potentially returning customers, thereby reducing costs and increase revenues. Given that RFM model has been widely adopted in marketing practice and research, this study takes a step further to consider other public information available at the platform, including the ratings, the elapsed on-shelf time, the number of purchased, and the number of Q&A’s, to established a research model on the repurchase behavior of online consumers based on the RFM model.Using web content mining technique we collected real transaction data of “women’s fashion clothing” from Taiwan’s Ruten auction website from April 1 to May 31 of 2012. Through the logistic regression analysis, we found that the factors of RFM model and the rating given by the buyer in the last purchase have significant impacts on repurchase. The managerial and practical implications of this study are suggested to platform businesses and online sellers.
