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Predicting customer churn using machine learning: A case study in the software industry

Author

Listed:
  • João Rolim Dias

    (Universidade Nova de Lisboa)

  • Nuno Antonio

    (Universidade Nova de Lisboa)

Abstract

Customer churn can be defined as the phenomenon of customers who discontinue their relationship with a company. This problem is transversal to many industries, including the software industry. This study uses Machine Learning to build a predictive model to identify potential churners in a Portuguese software house. Six popular Machine Learning models: Random Forest, AdaBoost, Gradient Boosting Machine, Multilayer Perceptron Classifier, XGBoost, and Logistic Regression, were developed to assess which one would have a better performance. The experimental results show that boosting techniques such as XGBoost present the best predictive performance. The XGBoost model presents a Recall of 0.85 and a ROC AUC of 0.86. Additionally to the model performance, the study of the model's feature importance revealed that some factors, such as the time to solve a support ticket, the type of application, the license age, and the number of incidents, significantly influence customer churn. These insights can help the software industry key drivers of churn and prioritize retention efforts accordingly.

Suggested Citation

  • João Rolim Dias & Nuno Antonio, 2025. "Predicting customer churn using machine learning: A case study in the software industry," Journal of Marketing Analytics, Palgrave Macmillan, vol. 13(1), pages 111-127, March.
    • Handle: RePEc:pal:jmarka:v:13:y:2025:i:1:d:10.1057_s41270-023-00269-9
    DOI: 10.1057/s41270-023-00269-9
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    References listed on IDEAS

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    1. Erika Slabber & Tanja Verster & Riaan de Jongh, 2023. "Some Insights about the Applicability of Logistic Factorisation Machines in Banking," Risks, MDPI, vol. 11(3), pages 1-21, February.
    2. Tianyuan Zhang & Sérgio Moro & Ricardo F. Ramos, 2022. "A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation," Future Internet, MDPI, vol. 14(3), pages 1-19, March.
    3. Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
    4. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
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