ICTACT Journal on Soft Computing

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Predictive Analysis of Customer Churn in Telecom Industry using Supervised Learning


Affiliations
1 Department of Computer Engineering, Fr. C. Rodrigues Institute of Technology, India
     

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Customer acquisition and retention is a key concern for several industries and is particularly acute in fiercely competitive and fast growth businesses. Retaining a loyal customer is far more important than acquiring a new one, thus making customer churn one of the critical concerns for big corporations. Finding factors triggering customer churn is vital to implement necessary remediation to pre-empt and cut back this churn. This research focuses on implementing machine learning (ML) algorithms to identify potential churn customers, categorise them based upon usage patterns, and visualize the analysis results. Results show that Extra Trees Classifier, XGBoosting Algorithm and Support Vector Machine have the best churn modelling performance, particularly for 80:20 dataset distribution with average AUC scores of 0.843, 0.787 and 0.735 respectively and low false negatives. The research demonstrates that ML algorithms can successfully predict potential customer churn and help in devising customer retention programmes.

Keywords

Customer or Client Retention, Customer Churn, Telecommunication Industry, Machine Learning.
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  • Predictive Analysis of Customer Churn in Telecom Industry using Supervised Learning

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Authors

Shreyas Rajesh Labhsetwar
Department of Computer Engineering, Fr. C. Rodrigues Institute of Technology, India

Abstract


Customer acquisition and retention is a key concern for several industries and is particularly acute in fiercely competitive and fast growth businesses. Retaining a loyal customer is far more important than acquiring a new one, thus making customer churn one of the critical concerns for big corporations. Finding factors triggering customer churn is vital to implement necessary remediation to pre-empt and cut back this churn. This research focuses on implementing machine learning (ML) algorithms to identify potential churn customers, categorise them based upon usage patterns, and visualize the analysis results. Results show that Extra Trees Classifier, XGBoosting Algorithm and Support Vector Machine have the best churn modelling performance, particularly for 80:20 dataset distribution with average AUC scores of 0.843, 0.787 and 0.735 respectively and low false negatives. The research demonstrates that ML algorithms can successfully predict potential customer churn and help in devising customer retention programmes.

Keywords


Customer or Client Retention, Customer Churn, Telecommunication Industry, Machine Learning.

References