A novel methodological approach to SaaS churn prediction using whale optimization algorithm

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dc.authoridCALLI, Levent/0000-0003-2221-1469;
dc.contributor.authorKotan, Muhammed
dc.contributor.authorSeymen, Omer Faruk
dc.contributor.authorCalli, Levent
dc.contributor.authorKasim, Sena
dc.contributor.authorYavuz, Burcu Carkli
dc.contributor.authorOzcelik, Tijen Over
dc.date.accessioned2025-10-11T20:48:01Z
dc.date.available2025-10-11T20:48:01Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractCustomer churn is a critical concern in the Software as a Service (SaaS) sector, potentially impacting long-term growth within the cloud computing industry. The scarcity of research on customer churn models in SaaS, particularly regarding diverse feature selection methods and predictive algorithms, highlights a significant gap. Addressing this would enhance academic discourse and provide essential insights for managerial decision-making. This study introduces a novel approach to SaaS churn prediction using the Whale Optimization Algorithm (WOA) for feature selection. Results show that WOA-reduced datasets improve processing efficiency and outperform full-variable datasets in predictive performance. The study encompasses a range of prediction techniques with three distinct datasets evaluated derived from over 1,000 users of a multinational SaaS company: the WOA-reduced dataset, the full-variable dataset, and the chi-squared-derived dataset. These three datasets were examined with the most used in literature, k-nearest neighbor, Decision Trees, Na & iuml;ve Bayes, Random Forests, and Neural Network techniques, and the performance metrics such as Area Under Curve, Accuracy, Precision, Recall, and F1 Score were used as classification success. The results demonstrate that the WOA-reduced dataset outperformed the full-variable and chi-squared-derived datasets regarding performance metrics.en_US
dc.identifier.doi10.1371/journal.pone.0319998
dc.identifier.issn1932-6203
dc.identifier.issue5en_US
dc.identifier.pmid40359310en_US
dc.identifier.scopus2-s2.0-105005016299en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0319998
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21708
dc.identifier.volume20en_US
dc.identifier.wosWOS:001488721000019en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPublic Library Scienceen_US
dc.relation.ispartofPlos Oneen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.titleA novel methodological approach to SaaS churn prediction using whale optimization algorithmen_US
dc.typeArticleen_US

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