| dc.description.abstract | E-commerce revenue has grown rapidly in recent years. It is estimated that revenue from the virtual channel in retailing industry will reach twenty percent in 2020. Enterprises should pay more attention to the operation of the virtual channel. Buyers in Taiwan are most commonly making online transactions on Yahoo!, Ruten and PChome. Given there is a fierce competition between e-commerce platforms in Taiwan, we hope to understand factors influencing buyers’ repurchase behaviors so to help identify valuable online customers. Based on a cross-industrial study in Harvard Business Review, if buyers’ repurchase rate increase five percent, the overall revenue can increase twenty five percent to ninety five percent, and this positive effect would be greater for e-businesses. Therefore this study defines repeated purchase on a certain e-commerce website as the platform repurchase behavior of online consumers, and who are considered valuable customers to the e-commerce website.
This study collected all transaction data from Yahoo! Taiwan auction website, including sixteen categories of products from January 1, 2014 to April 30, 2015, totaled 18 million transaction records. As a sequel to previous studies in platform repurchase behavior, we use January 1, 2015 as the prediction point in time, and three preceding months of transaction records as historical dataset and two succeeding months of transaction records as future dataset to generate 9 independent variables, including 4 RFM-related variables and 5 augmented variables, and the repurchase behavior variable. We conduct descriptive statistical analysis on 16 categories of data and then establish the prediction models of platform repurchase behavior in women apparel category, in popular experience goods categories and in entire experience goods categories respectively, to examine the effectiveness of the prediction model across multiple product category. Our findings show that recency, average monetary and purchase quantity are negatively related to repurchase behavior, while frequency, total monetary, last rating, number of purchased sellers, category diversity are positively related to repurchase behavior. In terms of analytical methodologies, we find that decision tree performs better than logistic regression. Based on the study results, we provide guidelines to identify valuable buyers and practical advices to the platform.
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