Abstract
Switching of customers from one service provider to another service provider is known as customer churn. With the surge of the technologies and increased customer awareness, retaining customers has become vital for a company’s growth. Several studies have been carried out to keep a check on the customer churn of companies and analyze churn prediction but the accuracy rate is not up to the mark. Recently with the extensive research in the field of Artificial Intelligence it has become possible to dig to the core of the factor responsible for customer churn. We present an effective solution to this challenging problem of customer churn prediction using the data set of telecommunication industry and Artificial Neural Networks to determine the factors influencing the customer churn and optimize the solutions by experimenting with different activation functions.
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Kumar, S., Kumar, M. (2019). Predicting Customer Churn Using Artificial Neural Network. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_25
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DOI: https://doi.org/10.1007/978-3-030-20257-6_25
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