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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65636

    Title: 滾動式RFM基礎的線上再購行為預測模型 ─以台灣Yahoo!奇摩拍賣女裝分類為例;A Rolling RFM-based Prediction Model of Online Repurchase Behavior: A Case of Women′s Apparel at Yahoo! Taiwan Auction Website
    Authors: 余芷函;Yu,Chih-han
    Contributors: 資訊管理學系
    Keywords: 再購行為;RFM模型;網路購物;滾動式預測;Repurchase Behavior;RFM Model;Online Shopping;Rolling Forecast
    Date: 2014-07-10
    Issue Date: 2014-10-15 17:06:55 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 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。
    ;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.
    Appears in Collections:[資訊管理研究所] 博碩士論文

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