Application of Machine Learning in the Telecommunications Industry: Partial Churn Prediction by using a Hybrid Feature Selection Approach

Fatemeh Mozaffari · Iman Raeesi Vanani · Babak Sohrabi

5 citations0/0 files storedJournal of Information Systems and Telecommunication (JIST)

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

The telecommunications industry is one of the most competitive industries in the world. Because of the high cost of customer acquisition and the adverse effects of customer churn on the company's performance, customer retention becomes an inseparable part of strategic decision-making and one of the main objectives of customer relationship management. Although customer churn prediction models are widely studied in various domains, several challenges remain in designing and implementing an effective model. This paper addresses the customer churn prediction problem with a practical approach. The experimental analysis was conducted on the customers' data gathered from available sources at a telecom company in Iran. First, partial churn was defined in a new way that exploits the status of customers based on criteria that can be measured easily in the telecommunications industry. This definition is also based on data mining techniques that can find the degree of similarity between assorted customers with active ones or churners. Moreover, a hybrid feature selection approach was proposed in which various feature selection methods, along with the crowd's wisdom, were applied. It was found that the wisdom of the crowd can be used as a useful feature selection method. Finally, a predictive model was developed using advanced machine learning algorithms such as bagging, boosting, stacking, and deep learning. The partial customer churn was predicted with more than 88% accuracy by the Gradient Boosting Machine algorithm by using 5-fold cross-validation. Comparative results indicate that the proposed model performs efficiently compared to the ones applied in the previous studies.

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References

1985From Intentions to Actions: A Theory of Planned Behavior 2008Handling class imbalance in customer churn prediction 2019Customer churn prediction in telecom using machine learning in big data platform 2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees 2015A comparison of machine learning techniques for customer churn prediction 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques 2019A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector

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