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: PurposeIn 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/approachThrough 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.FindingsThe 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/valueBy 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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