Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sasikiran Angara, V.; Manu, K.S.; Kumar Chandar, S.; Iyer, Lakshmi Shankar Title A name given to the resource Intelligent Analytical Framework to Improve Customer Retention in the SaaS Industry Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;454 SIST;pp.121-135 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-07837-7_10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-07837-7_10</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036714049?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036714049?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sasikiran Angara V., School of Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Manu K.S., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Kumar Chandar S., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Iyer L.S., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India Description An account of the resource In the software as a service (SaaS) sector, churn is a crucial indicator as it directly affects a businesss earnings, prospects for expansion, and viability over time. Because SaaS companies mostly rely on recurring income from subscriptions, high churn rates can be detrimental to their operations. Customer retention is crucial for SaaS companies as it is frequently more profitable and cost-effective than bringing on new customers. Retention expenses and efforts can be decreased by focusing on an appropriate set of customers. This study focuses on an intelligent analytical framework that uses machine learning and artificial intelligence techniques to find the ideal group of customers for a SaaS-based organization to retain. The previous papers concentrated on either classification or survival analysis to determine the probabilities of churn. A few studies used explainable AI models to improve the predictability of the model predictions. Not having a holistic prediction model and retention strategies provides the research gap for this study. The proposed methodology used feature selection models to identify the most significant drivers of churn, and the most popular predictive models, like logistic regression, random forest, support vector machine, and neural networks, are applied to the training set. The likelihood of churn is calculated by using classification models. The Kaplan-Meier estimate is used for survival analysis to determine the odds of survival based on the tenure of each account. Lastly, the prediction models interpretability is enhanced by using explainable AI models like SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME). The neural networks model gave the best accuracy of 71% for the classification model, which provided the probability of churn and the likelihood of survival, has been predicted by Survival Analysis. Explainable AI models have identified the most important features that the model considers when arriving at the probability. This enabled the company to segment the data based on the probabilities of churn and survival, and the feature importance and respective retention strategies have been planned for each segment. By implementing the suggested analytical methodology, the business may determine which customers are most important to target with customer retention strategies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Churn analysis; Explainable AI; Machine learning predictive models; SaaS; Survival analysis Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-303207836-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online