Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

The Research of Online Shopping Customer Churn Prediction Based on Integrated Learning

Authors
Guoen Xia, Qingzhe He
Corresponding Author
Guoen Xia
Available Online March 2018.
DOI
https://doi.org/10.2991/mecae-18.2018.133How to use a DOI?
Keywords
customer churn, artificial neural network,support vector machine.
Abstract
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.

Download article (PDF)

Proceedings
2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Part of series
Advances in Engineering Research
Publication Date
March 2018
ISBN
978-94-6252-493-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mecae-18.2018.133How to use a DOI?
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  - 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
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  -