Abstract
Nowadays, the telecom industries are going through a big problem that is customer churn. Recently, the market for mobile telecom industry has to change very promptly and there is a ferocious competition between them. Most of the telecom companies always concentrate on to obtain a new customer, but they do not pay too much attention to their existing customer. That’s why the company tries to find out that customers those have tendency to switch over in future. The information picked up from telecom industry to find out the logic of churning and try to solve those problems. The company targets those customers with a special program. The aim of this paper is to predict the customer churn for telecom industries using machine learning techniques namely Logistic Regression, Naïve Bayes and Decision Trees. In telecom industries, the principal objective of churning is to accurately calculate the customer survival and customer risk capabilities to gather the entire information of churn over the client residency. This paper summarizes the technique of predicting the churn so have a wide understanding of the customer churn. So that the telecom industries are aware in advance the big hazard customer and rectify their services to repeal the decision of churn. Customer profiling for predicting the customer who have churned in advance are also analyzed.
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Das, S.K., Kundu, S., Majee, S., Sarkar, C., Biswas, M. (2019). Adaptive Customer Profiling for Telecom Churn Prediction Using Computation Intelligence. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1031. Springer, Singapore. https://doi.org/10.1007/978-981-13-8581-0_6
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DOI: https://doi.org/10.1007/978-981-13-8581-0_6
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