The Research of Online Shopping Customer Churn Prediction Based on Integrated Learning
Guoen Xia, Qingzhe He
Available Online March 2018.
- https://doi.org/10.2991/mecae-18.2018.133How to use a DOI?
- customer churn, artificial neural network,support vector machine.
- The prediction of customer churn is an important research direction of customer churn management. In this paper, take the non-contract scenario of online shopping customers as an example, select transaction data of a domestic e-commerce website for empirical research. On the basis of the single model-BP neural network and support vector machine, apply the integrated learning theory to the online shopping customer classification. The empirical results show that the combined forecasting model has a significant improvement in the hit rate, coverage rate, accuracy rate and lift degree, and so on. In order to effectively identify different types of lost customers, use the RFM theory to classify the different value of the lost customers, thus implementation the strategy of customer churn retention.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Guoen Xia AU - Qingzhe He PY - 2018/03 DA - 2018/03 TI - The Research of Online Shopping Customer Churn Prediction Based on Integrated Learning BT - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018) PB - Atlantis Press SP - 259 EP - 267 SN - 2352-5401 UR - https://doi.org/10.2991/mecae-18.2018.133 DO - https://doi.org/10.2991/mecae-18.2018.133 ID - Xia2018/03 ER -