[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118569-en":3,"doc-seo-118569-105":29,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},118569,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","SMART RETENTION - LEVERAGING MACHINE LEARNING TO ENHANCE EMPLOYEE ENGAGEMENT IN REMOTE WORK","Remote work increases difficulty in maintaining employee engagement and retention, especially through isolation, weakened communication, and reduced team cohesion. This study examines how AI and machine learning strengthen human resource management by optimizing workforce planning, forecasting staffing needs, and predicting engagement or turnover risks. It highlights “smart retention” as technology-driven strategies to preserve key talent and proposes practical approaches such as personalized learning, sentiment-aware communication, and predictive analytics. The work concludes with a need for continued research.","UDC 331  \nBahman Nader,  \nmaster student,  \ncathedra of social work and human resources management,  \nUral Humanitarian Institute,  \nFGAOU VO Ural Federal University named after the first President of Russia B.N.Yeltsin .  \nYekaterinburg, Russia  \nAdebisi Kehinde,  \nmaster student,  \ncathedra of social work and human resources management,  \nUral Humanitarian Institute,  \nFGAOU VO Ural Federal University named after the first President of Russia B.N.Yeltsin .  \nYekaterinburg, Russia  \nLysenko Elena Vladimirovna,  \ncandidate of sciences (philosophy), associate professor,  \ncathedra of social work and human resources management,  \nUral Humanitarian Institute,  \nUral Federal University named after the First President of Russia B.N.Yeltsin,  \nYekaterinburg, Russian Federation  \nSMART RETENTION: LEVERAGING MACHINE LEARNING TO ENHANCE EMPLOYEE ENGAGEMENT IN REMOTE WORK  \nAbstract:  \nWith the rise of remote work, organizations face challenges in employee engagement. AI and machine learning can enhance human resource management by optimizing workforce planning and predicting needs. This study evaluates how machine learning improves employee engagement in virtual environments, offering strategies for smart retention and highlighting the need for further research.  \nKeywords:  \nSmart retention, machine learning, employee engagement, remote working, work from home  \nIntroduction  \nThe transition to remote work, accelerated by the global COVID-19 pandemic, has fundamentally altered the traditional workplace. While remote work offers flexibility and other benefits, it also presents significant challenges in maintaining employee engagement and retention. The increase in automation and digitalization has shifted the traditional administrative roles of HR, leading to a greater focus on leveraging technology to enhance effectiveness and employee satisfaction, particularly through the use of machine learning to increase retention. Artificial intelligence (AI) and machine learning are rapidly developing fields of computer science that aim to create machines capable of performing tasks that would normally require human intelligence. These technologies can significantly improve human resource management processes. For example, AI can optimize workforce planning, enhance employee well-being and safety processes, and predict human resource needs to ensure retention. This information can help human resource managers make better and more effective decisions about hiring, training, and developing employees [1]. Machine learning (ML) has emerged as a powerful tool to address these challenges by enhancing employee engagement in remote work settings, as employee retention is pivotal to organizational growth. This article explores how machine learning can be effectively utilized to improve employee retention and engagement in remote work environments.  \nThe Challenge of Remote Work  \nRemote work can lead to feelings of isolation, reduced communication, and a lack of team cohesion, all of which contribute to decreased employee engagement [2]. According to a study by Buffer (2020) [3], the most common struggle faced by remote workers is unplugging after work, followed by loneliness and collaboration difficulties. These challenges necessitate innovative solutions to keep employees motivated and engaged. Anis et al. (2011) [4], stated that focusing on employee engagement, job satisfaction, and organizational commitment can enhance retention effort.  \nFigure 1 – Key challenges of remote work (prepared by authors)  \nDefinition of Smart Retention: Retention is the process employers put in place to ensure that the employees do not quit their jobs. Defining “Smart” in the context of technology using Machine learning we can say “Smart Retention”is the technological strategies employed by employers to preserve their best talent from leaving the organization. Vishwanath et al. (2023) [5], reported that it is now becoming a modern trend where young professionals switch ","cbCaiftXUktoB8h0","https://ap.wps.com/l/cbCaiftXUktoB8h0","pdf",587635,1,3,"English","en",105,"# Introduction\n## The Challenge of Remote Work\n## Definition of Smart Retention\n# Machine Learning in Employee Engagement\n## Personalized Feedback and Learning\n## Enhancing Communication and Collaboration\n## Predictive Analytics for Retention","[{\"question\":\"Why is employee engagement harder in remote work?\",\"answer\":\"Remote work can cause isolation, reduce communication, and weaken team cohesion, which collectively lowers employee engagement. The document also cites remote workers’ struggles such as difficulty unplugging after work and loneliness.\"},{\"question\":\"What is “Smart Retention” in the remote-work context?\",\"answer\":\"Smart retention refers to technology-enabled strategies used by employers to preserve top talent and prevent employees from leaving. It frames retention as an organizational process strengthened by machine learning.\"},{\"question\":\"How can machine learning improve employee engagement remotely?\",\"answer\":\"Machine learning can analyze behavior and communication patterns to detect disengagement signals and estimate turnover risk. It also supports personalized training, sentiment-aware collaboration guidance, and predictive analytics for targeted interventions.\"}]","SMART RETENTION - LEVERAGING MACHINE LEARNING TO ENHANCE EMPLOYEE ENGAGEMENT IN REMOTE WORK | PDF",1785684280,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":28},"smart-retention-leveraging-machine-learning-to-enhance-employee-engagement-in-remote-work","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":21},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/smart-retention-leveraging-machine-learning-to-enhance-employee-engagement-in-remote-work/118569/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"Why is employee engagement harder in remote work?","Question",{"text":73,"@type":74},"Remote work can cause isolation, reduce communication, and weaken team cohesion, which collectively lowers employee engagement. The document also cites remote workers’ struggles such as difficulty unplugging after work and loneliness.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"What is “Smart Retention” in the remote-work context?",{"text":78,"@type":74},"Smart retention refers to technology-enabled strategies used by employers to preserve top talent and prevent employees from leaving. It frames retention as an organizational process strengthened by machine learning.",{"name":80,"@type":71,"acceptedAnswer":81},"How can machine learning improve employee engagement remotely?",{"text":82,"@type":74},"Machine learning can analyze behavior and communication patterns to detect disengagement signals and estimate turnover risk. 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