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This review analyses 212 published articles from 2015–2023 on machine learning approaches for churn prediction, mapping major model stages and contrasting them with partial prior surveys. It highlights engineered features spanning demographics, usage and behavioural signals, social interaction/communication graphs, and customer feedback, with emphasis on datasets from telecommunications, finance, and online gaming. Results note profitability evaluation as under-explored and recommend profit-based metrics, ensembles, deep learning, and explainable methods to improve retention decisions and drive future research.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-review-on-machine-learning-methods-for-customer-churn-prediction-and-recommendations-for-business-practitioners-2024/291202/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-review-on-machine-learning-methods-for-customer-churn-prediction-and-recommendations-for-business-practitioners-2024/291202.png","ImageObject",300,407,{"name":92,"@type":93},"Valentina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-21","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What does the review focus on regarding churn prediction?","Question",{"text":112,"@type":113},"It reviews 212 articles from 2015 to 2023 covering customer churn prediction using machine learning and examining key stages of churn prediction models.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which types of features are emphasized in the reviewed research?",{"text":117,"@type":113},"The review emphasizes demographic, usage-related, and behavioural characteristics, as well as features representing customer social interaction and communications graphs and customer feedback.",{"name":119,"@type":110,"acceptedAnswer":120},"What gap in evaluation metrics does the review identify?",{"text":121,"@type":113},"Profitability-oriented evaluation is described as under-researched, and the review advocates using profit-based evaluation metrics to support decision-making and improve profitability.",{"name":123,"@type":110,"acceptedAnswer":124},"What methodological recommendations does the review conclude with?",{"text":125,"@type":113},"It recommends ensembles and deep learning techniques and the adoption of explainable methods to enable further advancements.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},291202,1789643718,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":133,"read_time":148,"preview_page_count":4},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","Technological University Dublin  \nARROW@TU Dublin  \nArticles  \n2024  \nA Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners  \nAwais Manzoor  \nTechnological University Dublin  \nM. Atif Qureshi  \nTechnological University Dublin, [atif.qureshi@tudublin.ie](atif.qureshi@tudublin.ie)  \nEtain Kidney  \nTechnological University Dublin See next page for additional authors  \nFollow this and additional works at: [https://arrow.tudublin.ie/creaart](https://arrow.tudublin.ie/creaart)  \n Part of the Business Analytics Commons, and the Engineering Commons  \nRecommended Citation  \nManzoor, Awais; Qureshi, M. Atif; Kidney, Etain; and Longo, Luca, \"A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners\" (2024) . Articles. 241.  \n[https://arrow.tudublin.ie/creaart/241](https://arrow.tudublin.ie/creaart/241)  \nThis Article is brought to you for free and open access by ARROW@TU Dublin. It has been accepted for inclusion in Articles by an authorized administrator of ARROW@TU Dublin. For more information, please contact [arrow.admin@tudublin.ie](arrow.admin@tudublin.ie), [aisling.coyne@tudublin.ie](aisling.coyne@tudublin.ie), [vera.ki](vera.ki)[lshaw@tudublin.ie](lshaw@tudublin.ie).  \nThis work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License. Funder: Irish Research Council  \nAuthors  \nAwais Manzoor, M. Atif Qureshi, Etain Kidney, and Luca Longo  \nThis article is available at ARROW@TU Dublin: [https://arrow.tudublin.ie/creaart/241](https://arrow.tudublin.ie/creaart/241)  \nA Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for  \nBusiness Practitioners  \nAWAIS MANZOOR1,2,3, M. ATIF QURESHI1,2, ETAIN KIDNEY2, AND LUCA LONGO3 1ADAPT Centre, eXplainable Analytics Group, Technological University Dublin, Dublin 2, D02 HW71 Ireland  \n2Faculty of Business, Technological University Dublin, Dublin 2, D02 HW71 Ireland  \n3Artificial Intelligence and Cognitive Load Research Laboratories, Technological University Dublin, Dublin 7, D07 H6K8 Ireland Corresponding author: Awais Manzoor ([malik.awaismanzoor@gmail.com](malik.awaismanzoor@gmail.com))  \nThis work was supported by the Irish Research Council under Award GOIPG/2021/1354; and in part by the Science Foundation Ireland through the ADAPT SFI Research Centre, Technological University Dublin, under Grant 13/RC/2106_P2 .  \nABSTRACT Due to market deregulation and globalisation, competitive environments in various sectors continuously evolve, leading to increased customer churn. Effectively anticipating and mitigating customer churn is vital for businesses to retain their customer base and sustain business growth. This research scrutinizes 212 published articles from 2015 to 2023, delving into customer churn prediction using machine learning methods. Distinctive in its scope, this work covers key stages of churn prediction models comprehensively, contrary to published reviews, which focus on some aspects of churn prediction, such as model development, feature engineering and model evaluation using traditional machine learning-based evaluation metrics. The review emphasises the incorporation of features such as demographic, usage-related, and behavioural characteristics and features capturing customer social interaction and communications graphs and customer feedback while focusing on popular sectors such as telecommunication, finance, and online gaming when producing newer datasets or developing a predictive model. Findings suggest that research on the profitability aspect of churn prediction models is under-researched and advocates using profit-based evaluation metrics to support decision-making, improve customer retention, and increase profitability. Finally, this research concludes with recommendations that advocate the use of ensembles and deep learning techniques, and as well as the adoption of explainable m","cbCaijwgMEMj5hMO","https://ap.wps.com/l/cbCaijwgMEMj5hMO","pdf",5199464,32,"English","# Abstract\n# I. Introduction\n## Customer churn challenges and retention strategies\n## Industry-specific churn definitions\n# Index Terms\n# Authors and publication metadata","[{\"question\":\"What does the review focus on regarding churn prediction?\",\"answer\":\"It reviews 212 articles from 2015 to 2023 covering customer churn prediction using machine learning and examining key stages of churn prediction models.\"},{\"question\":\"Which types of features are emphasized in the reviewed research?\",\"answer\":\"The review emphasizes demographic, usage-related, and behavioural characteristics, as well as features representing customer social interaction and communications graphs and customer feedback.\"},{\"question\":\"What gap in evaluation metrics does the review identify?\",\"answer\":\"Profitability-oriented evaluation is described as under-researched, and the review advocates using profit-based evaluation metrics to support decision-making and improve profitability.\"},{\"question\":\"What methodological recommendations does the review conclude with?\",\"answer\":\"It recommends ensembles and deep learning techniques and the adoption of explainable methods to enable further advancements.\"}]","A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners - 2024 | PDF",81]