| Summary: | 碩士 === 國立中央大學 === 工業管理研究所 === 102 === With the popularity of B2C e-commerce, online shopping has become an important issue. Online sellers of goods and services must be through the quality to retain old customers and develop new customers, online buyers only as a selection of trading partners and purchasing decisions through information which platform provider seller disclosed. Generally, the cost of obtaining a new customer is five times as large as the cost of retaining an old customer, thus avoiding the churn of customers is an important issue nowadays for Internet sellers. The purpose of this study is to identify influencing factors of the churn of customers by the actual transaction data analysis for shopping website, and then assess the likelihood of churn of customers.
Using web content mining technique, we collected real data of seller's information from dress shop in Taobao shop street during the January 1, 2013 to April 30, 2013.We consider that online consumer behavior and online seller’s operating performance are two variables which we used to build a predictive model for B2C e-commerce customer’s churn. Online consumer behavior using the RFM variables, including recent trading interval, the cumulative number of transactions and average transaction amount. The variables of online seller, including the rating score, collectibles popularity, assortment size…etc. Using logistic regression analysis, the study found that in addition to the three RFM variables, and there are other variables significantly associated with customer churn rate, the network seller cannot be ignored. Because the model established in this study can effectively predict the churn of the online consumers. On the other hand, this study can provide effective business strategies for Internet shopping platform provider to retain customers and to achieve greater profitability .
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