[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127463-en":3,"doc-seo-127463-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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":29},127463,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","APPLICATION OF MACHINE LEARNING FOR CHURN PREDICTION AND CUSTOMER CONVERSION IN ENTERTAINMENT COMPANIES","Organizational engineering, particularly competitive intelligence, supports strategic decision-making through data analysis and predictive methods. In the entertainment sector, managing the customer base is critical because acquisition costs are high and retention strongly affects profitability and long-term sustainability. Machine learning techniques for churn prediction and conversion analysis help detect behavioral patterns before customers leave and reveal factors that drive purchase decisions. The study investigates how these methods can be applied in Brazilian entertainment firms to improve relationship management, optimize marketing actions, and increase commercial performance and customer lifetime value.","APPLICATION OF MACHINE LEARNING FOR CHURN PREDICTION AND CUSTOMER CONVERSION IN ENTERTAINMENT COMPANIES  \nAPLICAÇÃO DE MACHINE LEARNING PARA PREVISÃO DE CHURN E CONVERSÃO DE CLIENTES EM EMPRESAS DE  \nENTRETENIMENTO  \nDE MACHINE LEARNING PARA LA PREDICCIÓN DE CHURN Y CONVERSIÓN DE CLIENTES EN EMPRESAS DE ENTRETENIMIENTO  \nLuiza Helena Oliveira de Lucena1 Natália Ilha Gattai2  \nDaniel Mendes Magliano3 Luiza Lima Dornelles4  \nGustavo Shigueru Tayamichi Sato5  \nDOI: 10.54751/revistafoco.v19n3-066  \nReceived: Feb 16th, 2026  \nAccepted: Mar 10th, 2026  \nABSTRACT  \nOrganizational Engineering, particularly in the field of Competitive Intelligence, plays a fundamental role in supporting strategic decision-making through data analysis and the application of predictive methods. In the entertainment sector, effective customer base management is crucial, as acquisition costs are typically high and customer retention has a direct impact on profitability and long-term sustainability. Within this context, Machine Learning techniques applied to churn prediction and conversion analysis enable the identification of behavioral patterns that precede customer attrition, as well as key factors that influence purchasing decisions. This study aims to investigate how these methods can be applied in Brazilian entertainment companies to enhance relationship management strategies, optimize marketing actions, and improve overall commercial performance and customer lifetime value.  \n1 Graduating in Production Engineering, Universidade de Brasília - campus Universitário Darcy Ribeiro, Gleba A, Asa Norte, Brasília, Distrito Federal, CEP: 70910-900. E-mail: [luizahelena.lucena@gmail.com](luizahelena.lucena@gmail.com)  \n2 Graduating in Production Engineering, Universidade de Brasília - campus Universitário Darcy Ribeiro, Gleba A, Asa Norte, Brasília, Distrito Federal, CEP: 70910-900. E-mail: [natigattai@hotmail.com](natigattai@hotmail.com)  \n3 Graduating in Production Engineering, Universidade de Brasília - campus Universitário Darcy Ribeiro, Gleba A, Asa Norte, Brasília, Distrito Federal, CEP: 70910-900. E-mail: [daniel.mendes2002@hotmail.com](daniel.mendes2002@hotmail.com)  \n4 Graduating in Production Engineering, Universidade de Brasília - campus Universitário Darcy Ribeiro, Gleba A, Asa Norte, Brasília, Distrito Federal, CEP: 70910-900. E-mail: [luizaldornelless@gmail.com](luizaldornelless@gmail.com)  \n5 Graduating in Production Engineering, Universidade de Brasília - campus Universitário Darcy Ribeiro, Gleba A, Asa Norte, Brasília, Distrito Federal, CEP: 70910-900. E-mail: [gustavo.shigueru.sato@gmail.com](gustavo.shigueru.sato@gmail.com)  \nKeywords: Churn prediction; machine learning; competitive intelligence; customer conversion; entertainment.  \nRESUMO  \nA Engenharia Organizacional, particularmente na área de Inteligência Competitiva, desempenha um papel fundamental no apoio à tomada de decisões estratégicas por meio da análise de dados e da aplicação de métodos preditivos. No setor de entretenimento, a gestão eficaz da base de clientes é crucial, visto que os custos deaquisição são tipicamente elevados e a retenção de clientes tem um impacto direto narentabilidade e na sustentabilidade a longo prazo. Nesse contexto, as técnicas de Aprendizado de Máquina aplicadas à previsão de churn e à análise de conversãopermitem a identificação de padrões comportamentais que precedem a perda de clientes, bem como de fatores-chave que influenciam as decisões de compra. Este estudo visa investigar como esses métodos podem ser aplicados em empresas brasileiras de entretenimento para aprimorar as estratégias de gestão derelacionamento, otimizar as ações de marketing e melhorar o desempenho comercialgeral e o valor do ciclo de vida do cliente.  \nPalavras-chave: Churn prediction; machine learning; inteligência competitiva; conversão de clientes; entretenimento.  \nRESUMEN  \nLa ingeniería organizacional, en particular la inteligencia competitiva, desempeña un papel fundamen","cbCaigte9b4Z3Lm1","https://ap.wps.com/l/cbCaigte9b4Z3Lm1","pdf",1138497,1,24,"English","en",105,"# Introduction\n## Customer base management in entertainment\n## Competitive intelligence and data-driven decision-making\n## Churn prediction and conversion analysis\n## Study objectives and expected value","[{\"question\":\"Why is churn prediction important for entertainment companies?\",\"answer\":\"Entertainment companies face high customer acquisition costs, so retention directly impacts profitability and long-term sustainability. Churn prediction helps anticipate customer attrition using behavioral signals.\"},{\"question\":\"How do machine learning techniques support customer conversion analysis?\",\"answer\":\"They identify behavioral patterns that precede churn and determine key factors influencing purchasing decisions. This supports better targeting of customers likely to convert.\"},{\"question\":\"What is the main goal of the study in Brazilian entertainment companies?\",\"answer\":\"To investigate how machine learning methods can strengthen relationship management strategies, optimize marketing actions, and improve commercial performance and customer lifetime value.\"}]","APPLICATION OF MACHINE LEARNING FOR CHURN PREDICTION AND CUSTOMER CONVERSION IN ENTERTAINMENT COMPANIES | PDF",1785939036,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"application-of-machine-learning-for-churn-prediction-and-customer-conversion-in-entertainment-companies","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-for-churn-prediction-and-customer-conversion-in-entertainment-companies/127463/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is churn prediction important for entertainment companies?","Question",{"text":76,"@type":77},"Entertainment companies face high customer acquisition costs, so retention directly impacts profitability and long-term sustainability. Churn prediction helps anticipate customer attrition using behavioral signals.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do machine learning techniques support customer conversion analysis?",{"text":81,"@type":77},"They identify behavioral patterns that precede churn and determine key factors influencing purchasing decisions. 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