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。
本研究的資料蒐集自Yahoo!奇摩拍賣女裝分類中2013年9月30日以前的所有交易資料，總交易筆數約為558萬筆。本研究將所有資料以敘述統計作初步分析以觀察再購顧客的特性，並且利用三至六個預測變數建立了滾動式預測模型，此六個預測變數分別為：上次交易時間間隔、交易次數、累積交易金額、平均交易金額、上次交易評價及過去再購家數，也檢測了不同時間點及時間範圍的模型分類正確率，來驗證此滾動式預測模型不會受到時間點及時間範圍改變的影響。最後，本研究針對預測模型進行模型適配度檢定及羅吉斯迴歸分析，分析結果顯示上次交易時間間隔越長、平均交易金額越多，再購行為發生的機率越低；相對地，交易次數越多、累積交易金額越多、上次交易評價越佳或過去再購家數越多，再購行為發生的機率越高。其中只有再購家數的結果和我們提出的假說不一致。本研究的主要貢獻有三：(1)實務上可以幫助線上賣家進行目標行銷以留住舊顧客；(2)以最後一次評價和再購家數擴充RFM模型可以有效提昇預測的準確率；(3)根據完整交易資料的敘述統計結果可以作為其他線上消費者研究的參照。

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.

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