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.
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Funding
This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
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The authors declare that they have a relationship with the studied software house, but this relationship did not influence the work reported in this paper.
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Dias, J.R., Antonio, N. Predicting customer churn using machine learning: A case study in the software industry. J Market Anal (2023). https://doi.org/10.1057/s41270-023-00269-9
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DOI: https://doi.org/10.1057/s41270-023-00269-9