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Churn Prediction and Retention in Banking, Telecom and IT Sectors Using Machine Learning Techniques

Part of the Algorithms for Intelligent Systems book series (AIS)


Customer churn prevention is one of the deciding factors when it comes to maximizing the revenues of any organization. Also known as customer attrition, it occurs when customers stop using the products or services of a company. Through our paper, we are predicting customer churn beforehand so that proper customer retention steps can be taken with the help of exploratory data analysis and to make customized offers for the targets. For the churn prediction, our implementation consists of comparative analysis of four algorithmic models, namely logistic regression, random forest, SVM and XGBoost, on three different domains, namely banking, telecom and IT. The purpose of doing this comparative analysis is that there are not many research works which compare the performance of various algorithms in different domains. We also develop various retention strategies with the help of exploratory data analysis.


  • Churn
  • Retention
  • Prediction
  • Logistic regression
  • Random forest
  • SVM
  • XGBoost
  • Telecom
  • Banking
  • IT

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Correspondence to R. Manoov .

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Jain, H., Yadav, G., Manoov, R. (2021). Churn Prediction and Retention in Banking, Telecom and IT Sectors Using Machine Learning Techniques. In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore.

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  • Print ISBN: 978-981-15-5242-7

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