Deep Multi-level Ensemble Model for Customer Churn Prediction

Deep Multi-level Ensemble Model for Customer Churn Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deep Multi-level Ensemble Model for Customer Churn Prediction Dang Tho Le, Van Thong Nguyen, Manh Tuan Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7067301/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Customer churn prediction is a critical challenge in customer analytics, especially in highly competitive sectors such as telecommunications and e-commerce. While ensemble learning has shown promise in improving classification accuracy, traditional stacking methods often fail to capture deep interactions between base learners. This study proposes a novel multi-level ensemble model, XMS-Net, which combines XGBoost, LightGBM, and multi-layer perceptron classifiers using a two-level stacking architecture with a deep MLP meta learner. The model is evaluated on four real-world datasets. Experimental results show that XMS-Net significantly outperforms baseline classifiers and traditional stacking methods across multiple metrics, including F1-score, accuracy, recall, precison, and ROC-AUC. Highest accuracy score achives 97, improvements of up to 6.8 compared to the strongest single model are reported. Statistical tests (paired t-test and Wilcoxon signed-rank test) confirm the significance of these gains. An ablation study further highlights the importance of integrating both gradient boosting and neural learners. These findings indicate that XMS-Net provides a robust and scalable approach for churn prediction and may be generalized to other domains involving high-dimensional, imbalanced data. The study contributes a validated ensemble framework for enhancing predictive performance in real-world classification tasks. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7067301","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":485415772,"identity":"b175c915-99b9-4529-b5e9-7ad817e27219","order_by":0,"name":"Dang Tho Le","email":"","orcid":"","institution":"University of Economics Ho Chi Minh City","correspondingAuthor":false,"prefix":"","firstName":"Dang","middleName":"Tho","lastName":"Le","suffix":""},{"id":485415773,"identity":"d23d2b03-96f2-415c-a128-c22e513e9757","order_by":1,"name":"Van Thong Nguyen","email":"","orcid":"","institution":"University of Economics Ho Chi Minh City","correspondingAuthor":false,"prefix":"","firstName":"Van","middleName":"Thong","lastName":"Nguyen","suffix":""},{"id":485415774,"identity":"b64162f8-0fc5-470c-88be-1fb7a2790879","order_by":2,"name":"Manh Tuan Nguyen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYPACGwYGCSDFQ4xaqKI00rUcJkGLvUTys8eFOecT+6UbGB+8bWNI3E7QFok0c+OZ224nzpxzgNlwLlDLzgaCWhLMpHmBWjbcSGCT5m1jMDY4QFBL+jeglnOJ+28ksP8mUksOyJYDiRskEtiYgVrkCGs586YMqCXZeMaNxGbJOeckCGthb0/fBtRiJ9s/I/nghzdlNjwEtSABxgYGSOyMglEwCkbBKKAYAABcKznZ1RuaTAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Economics Ho Chi Minh City","correspondingAuthor":true,"prefix":"","firstName":"Manh","middleName":"Tuan","lastName":"Nguyen","suffix":""}],"badges":[],"createdAt":"2025-07-07 16:13:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7067301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7067301/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93071356,"identity":"b33b82e9-249e-4704-9633-4c6b63ffd186","added_by":"auto","created_at":"2025-10-08 17:56:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1553241,"visible":true,"origin":"","legend":"","description":"","filename":"XMSNetV4.4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7067301/v1_covered_c025a07f-2bf3-465f-88d5-6f6d999819bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Multi-level Ensemble Model for Customer Churn Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7067301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7067301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Customer churn prediction is a critical challenge in customer analytics, especially in highly competitive sectors such as telecommunications and e-commerce. 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