Telecom customer churn prediction in context of composite model

Xianshuo Yuan

doi:10.1117/12.2672716

28 March 2023 Telecom customer churn prediction in context of composite model

Xianshuo Yuan

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 125972P (2023) https://doi.org/10.1117/12.2672716
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

For the increasingly saturated telecommunications market, the way to determine whether customers will churn and find the reasons that affect customer churn are the two main factors affecting the operation of telecom companies. In this study, a combinatorial model prediction composed of logistic regression and neural networks was adopted. It contains both the efficient and precise characteristics of machine learning and the good explanatory nature of logistic regression. The prediction accuracy of the final model reached about 80.5%, which is about 3% lower than that of a single neural network model and about 7% higher than that of logistic Regression. According to the results of the significance test, the main factors affecting customer churn are the following aspects: long-term costs, service duration, network services, and contract types. The same approach of this study can be applied to other industries (e.g., video site membership), allowing decision makers to predict the flow of customers and develop plans in advance to reduce losses. These results shed light on guiding further exploration of an analytical approach to contemporary customer churn and methods of decision-making.

Citation Download Citation

Xianshuo Yuan "Telecom customer churn prediction in context of composite model", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 125972P (28 March 2023); https://doi.org/10.1117/12.2672716

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KEYWORDS

Neural networks

3D modeling

Data modeling

Internet

Principal component analysis

Education and training

Industry

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