Research on E-commerce Customer Churn Prediction Based on Improved Value Model and XG-Boost Algorithm



In recent years, with the development of Internet technology, the market competition is fiercer, the cost of acquiring new customers is increasing, and the cost of maintaining old customers is far less than the cost of acquiring new customers. Most companies are trying to market precisely through customer segmentation in order to reduce the rate of customer churn. Aiming at the customer characteristics of social network e-commerce, this paper builds a customer value model that integrates the value of social network to help companies subdivides the customer accurately. Then we use the machine learning algorithm XG-Boost to predict the churn of customers before and after the subdivision. The research found that the prediction accuracy is higher after customer segmentation. In addition, the XG-Boost algorithm is more advantageous than other algorithms.


Customer value; XG-Boost algorithm; Social network value

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Cheng, H., & Fan, C. J. (2018). Application research of neural network in risk prediction of e-commerce enterprises’ customer loss in China. Chinese Collective Economy, (17), 54-56.

Cheng, Y. S., & Zou, H. (2018). Evaluation of bank credit risk based on random forest RFM model. Journal of An Qing Normal University, 24(03), 34-37.

Du, K., Deng., J. W., & Chen., J. H. (2018). Research and application of improving RFM model in real estate customer segmentation. Computer Knowledge and Technology, 14(19), 243-245+251.

Feng, X., Wang, C., Liu, Y., Yang, Y., & An, H. G. (2018). Research on customer churn prediction based on comment emotional tendency and neural network. Journal of China Academy of Electronics Science, 13(03), 340-345.

Huang, X., Liu, X., & Liu, J. (2019). A new research on impact evaluation algorithm of Weibo user. Computer Engineering, 1-7.

Huang, J. (2018). A Comparative Study of Social E-Commerce and Traditional E-commerce. Economic and Trade Practice, (23), 188-189.

He., Y., J (2017). Social network user discourse influence value fractal dimension method. System Engineering, 35(02), 145-152.

Liao, J. F., Chen, T. G., & Chen, X. (2018). Research on social media user flow based on customer churn theory. Information Science, 36(01), 45-48.

Liu., D. (2018). Research on civil aviation passenger value and travel prediction model based on social network. China Civil Aviation University.

Lu, N., Liu, X. W., & Lee, L. (2018). Research on customer value segmentation of online shop based on RFM. Computer Knowledge and Technology, 14(18), 275-276+284.

Qian, S. L., He, J. M., & Wang, C. L. (2007). Telecom customer churn prediction model based on improved support vector machine. Management Science, (01), 54-58.

Ren, H. J., & Xia, G. E. (2018). Summary of customer churn research. Chinese Business Theory, (32), 166-167.

Shao, D. (2016). Analysis and prediction of insurance company’s customer loss based on BP neural network. Lanzhou University.

Su, M. (2016). Customer relationship management (p.15). Higher Education Press.

Tian, L., Qiu, H. Z., & Zheng, L. H. (2007). Modeling and implementation of telecom customer churn prediction based on neural network. Computer Application, (09), 2294-2297.

Weng, J. (2018). Implementation of telecom customer churn prediction system based on big data mining. Nanchang University.

Wang, R., Zhou, Y., Liu, Y. L., & Jue, S. P. (2018). Research on MOOC learner segmentation based on improved RFM model. Computer Products and Circulation, (07), 239-240+268.

Wang, T., Cong, Q., Shang, Y. L., Chen, H., Zhang, Z. F., & Fan, B. B. (2018). Customer churn prediction model for product service system based on improved support vector machine. Combined Machine Tools and Automated Machining Technology, (05), 181-184.

Weng, D. (2016). Research on corporate customer churn and customer segmentation. Nanjing University.

Wu, X. J., & Meng, S. S. (2017). Research on e-commerce customer churn prediction based on customer segmentation and Ada-Boost. Industrial Engineering, 20(02), 99-107.

Yao, D. (2017). Research on customer churn prediction model and its application. Northwest University.

Zhai, X. K., & Liao., M. H. (2018). Research on WeChat social e-commerce information communication behavior based on SIR model. Journal of Changsha University, 32(05), 70-73.

Zhang, D. (2015). Establishment and application of customer churn prediction model. Beijing Institute of Technology.

Zhang, J. (2015). A customer churn prediction model based on C5.0 decision tree. Statistics and Information Forum, 30(01), 89-94

Zhou., D. (2014). Research on telecom customer churn prediction based on feature selection of rough sets. Jiangsu University of Science and Technology



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