Churn prediction for SaaS company with machine learning

Hugo Eduardo Sanches, Ayslan Trevizan Possebom and Linnyer Beatrys Ruiz Aylon

Innovation & Management Review, 2025, vol. 22, issue 2, 130-142

Abstract: Purpose - In an era marked by fierce business competition, customer retention is crucial for sustaining profitability. Churn prediction, the ability to forecast customer defections, is essential to enhance retention and can profoundly impact a company’s bottom line. Among prediction techniques, machine learning techniques have proven to be efficient and reliable. Thus, this research aims to develop a model that effectively predicts customer churn for TecnoSpeed and provides insights into customer behavior. Design/methodology/approach - Through a preprocessing and normalization of data, seven machine learning algorithms were applied. The models were trained, and also cross-validation and parameter tuning techniques were applied to improve results. The study also explores feature performance, providing insights into attributes that influence customer churn, thereby guiding effective strategies. Findings - The results of three algorithms achieved over 90% accuracy, with less than 10% of the errors being part false negatives. We also introduce the Churn Probability Index, a novel metric that aggregates the outputs of multiple predictive models to provide an assessment of high-risk churn. This research is of significant importance as it contributes to the development of effective retention strategies for SaaS companies. Originality/value - By applying machine learning to churn prediction, this study offers valuable insights into the performance and comparative analysis of different algorithms in a real-world SaaS environment. This study stands distinguished by its emphasis on a practical business scenario, enriched by a robust dataset provided and a large set of machine learning techniques. The findings provide practical implications for managers and administrators seeking to optimize customer retention and profitability.

Keywords: Machine learning; Churn prediction; Data science; Software as a service (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eme:inmrpp:inmr-06-2023-0101

DOI: 10.1108/INMR-06-2023-0101

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