@article{2335,
  author = {Weihua Zhang, Li Zhu},
  title = {Electronic Commerce Customer Churn Prediction Model Based on Web Data Mining},
  journal = {Progress In Computing Applications },
  year = {2017},
  volume = {6},
  number = {2},
  doi = {},
  url = {http://www.dline.info/pca/fulltext/v6n2/pcav6n2_1.pdf},
  abstract = {Web data mining is a new subject which has been applied in many fields. With the rapid development of
electronic commerce, the consumers have started to accept this kind of online shopping purchasing way. E-commerce
websites have increasingly brought economic income. But on the other hand, this also brings several problems. Some of them
are the instability of customers and high customer churn rate. So in this paper, we will analyze the electronic commerce
customer churn prediction model based on Web data mining. Network and information security issues, have become a
bottleneck for further development of the network economy. Web data mining technology is the key to improve the performance
of network information security technology. And we will use the Pareto / NBD model to figure out the customers’
activity to predict the trend of the customers, which can be useful for the enterprise to formulate retaining strategies and
resistance strategies to control website customers churn.},
}