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Research Article Open access CC BY 4.0

Predicting Customer Churn in Telecommunications with Machine Learning Models

Mohammad Raquibul Hossain

Asian Journal of Research in Computer Science · pp. 53–66 · Published 6 Jan 2025

10.9734/ajrcos/2025/v18i1548

Abstract

Customer churn is an important issue in businesses and service sectors including telecommunication industries. Prediction of potential customer churn can be very useful in these fields and it can help to improve customer retention significantly by providing personalized offering to reduce potential churn. This paper mainly focused on customer churn prediction using machine learning (ML) models and Iranian Telco customer churn dataset. Different reasons or variables are involved in customer dissatisfaction or indication of customer’s churn. If ML models are trained with such essential and crucial data, they can provide better prediction with high accuracy. This study experimented 16 ML models (single as well as ensemble) and the model performances were evaluated in two ways - 80:20 train-test split and 10-fold cross-validation. In 80:20 split, categorical boosting CatBoost classifier outperformed other models with 97.54% accuracy. However, Light Gradient Boosting Machine classifier LGBMC performed best in 10-fold cross-validation by achieving average accuracy of 96.39% with 1.08% standard deviation while CatBoost classifier was second best performing model. Thus, machine learning techniques can very effectively serve Telco industry as well as other customer-oriented competitive businesses. However, limitation of this study is that it experimented one dataset having 2850 distinct instances with 14 feature variables including target feature. Hence, further experiments with more and large datasets are suggested. Businesses including telecommunication industries can implement these ML models by deploying them in cloud or online platform. In this regard, an online database is required to maintain to automatically collect the from customers involving all the feature variables. If potential churn of any customer is predicted by the ML models, initiatives should be taken by providing improved customer care and personalized services to retain the customer. 

Customer churn prediction telco industry machine learning artificial intelligence lightgbm catboost histogram-based gradient boosting

References (28)

  1. 1 Crop yield prediction using machine learning: A systematic literature review [DOI]
  2. 2 Customer churn prediction system: a machine learning approach [DOI]
  3. 3 Incorporating textual information in customer churn prediction models based on a convolutional neural network [DOI]
  4. 4 B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM [DOI]
  5. 5 A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners [DOI]
  6. 6 Customer Churn Prediction in Telecommunication Industry Using Deep Learning [DOI]
  7. 7 Leveraging unstructured call log data for customer churn prediction [DOI]
  8. 8 Machine Learning Based Customer Churn Prediction In Banking [DOI]
  9. 9 E-commerce customer churn prevention using machine learning-based business intelligence strategy [DOI]
  10. 10 Investigating customer churn in banking: a machine learning approach and visualization app for data science and management [DOI]
  11. 11 Customer churn prediction using composite deep learning technique [DOI]
  12. 12 Machine Learning to Develop Credit Card Customer Churn Prediction [DOI]
  13. 13 A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy [DOI]
  14. 14 Machine learning based customer churn prediction in home appliance rental business [DOI]
  15. 15 Customer Churn Prediction in Influencer Commerce: An Application of Decision Trees [DOI]
  16. 16 Customer - Churn Prediction Using Machine Learning [DOI]
  17. 17 Deep Learning for Customer Churn Prediction in E-Commerce Decision Support [DOI]
  18. 18 Predicting customer churn: A systematic literature review [DOI]
  19. 19 Research on telecom customer churn prediction based on ensemble learning [DOI]
  20. 20 Customer Churn Prediction using Machine Learning Models [DOI]
  21. 21 An analysis on classification models for customer churn prediction [DOI]
  22. 22 Giant fight: Customer churn prediction in traditional broadcast industry [DOI]
  23. 23 Prediction of Customer Churn Using Machine Learning [DOI]
  24. 24 AI-Based Customer Churn Prediction Model for Business Markets in the USA: Exploring the Use of AI and Machine Learning Technologies in Preventing Customer Churn [DOI]
  25. 25 User churn prediction hierarchical model based on graph attention convolutional neural networks [DOI]
  26. 26 Optimizing Heart Disease Prediction: A Comparative Study of Machine Learning Techniques [DOI]
  27. 27 Prediction of Lower-Grade Glioma and Glioblastoma Multiforme Using Machine Learning Models and Optuna-Optimization [DOI]
  28. 28 Customer Churn Prediction in Telecom Sector Using Machine Learning Techniques [DOI]

Cited by 14

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