A multi-layer perceptron approach for customer churn prediction

Mokhairi, Makhtar and Mohd Khalid, Awang and Mohd Nordin, Abdul Rahman (2015) A multi-layer perceptron approach for customer churn prediction. International Journal of Multimedia and Ubiquitous Engineering, 10 (7). pp. 213-222. ISSN 19750080 [P]

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Nowadays, the telecommunication industries are facing substantial competition among the providers in order to capture new customers. Many providers have faced a loss of profitability due to the existing customers migrating to other providers. Customer retention program is one of the main strategies adopted in order to keep customers loyal to their provider. However, it requires a high cost and therefore the best strategy that companies could practice is to focus on identifying the customers that have the potential to churn at an early stage. The limited amount of research on investigating customer churn using machine learning techniques has lead this research to explore the potential of an artificial neural network to improve customer churn prediction. The research proposes Multilayer Perceptron (MLP) neural network approach to predict customer churn in one of the leading Malaysian’s telecommunication companies. The results are compared against the most popular churn prediction techniques such as Multiple Regression Analysis and Logistic Regression Analysis. The result has proven the supremacy of neural network (91.28% of prediction accuracy) over the statistical models in prediction tasks. Overall, the findings suggest that a neural network learning algorithm could offer a viable alternative to statistical predictive approaches in customer churn prediction.

Item Type: Article
Uncontrolled Keywords: Neural Network, Regression Analysis, Multiple Regression, Logistic Regression, Data Mining, Churn
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Informatics & Computing
Depositing User: Syahmi Manaf
Date Deposited: 13 Sep 2022 05:50
Last Modified: 13 Sep 2022 05:50
URI: http://eprints.unisza.edu.my/id/eprint/6567

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