[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128631-en":3,"doc-seo-128631-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128631,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Categories’ Churn - A Machine Learning Approach in Retail - Dissertação de Mestrado","In the context of business relationships, customer churn—cessation of relations with a company—creates substantial financial exposure through reduced revenue, weaker profitability, and increased reputational risk. Understanding the underlying drivers of churn therefore becomes central to protecting long-term business performance. This dissertation applies clustering techniques together with machine learning models to balance predictive accuracy with clear communication of model behavior, supporting practical retention decision-making and measurable improvements in customer satisfaction and retention.","Universidade do Minho  \nEscola de Ciências  \nCarlos Filipe Fernandes Sousa  \nCategories’ Churn: A Machine Learning Approach in Retail  \noutubro de 2023  \nUniversidade do Minho  \nEscola de Ciências  \nCarlos Filipe Fernandes Sousa  \nCategories’ Churn: A Machine Learning Approach in Retail  \nDissertação de Mestrado Mestrado em Estatística para Ciência de Dados  \nTrabalho efetuado sob a orientação da  \nProfessora Doutora Arminda Manuela Andrade Pereira Gonçalves  \ne da  \nDr.ª Ana da Costa Freitas  \noutubro de 2023  \nDIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS  \nEste é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e  \nboas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada.  \nCaso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstasno licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidade do Minho.  \nLicença concedida aos utilizadores deste trabalho  \nAtribuição  \nCC BY  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nAcknowledgements  \nFirst of all I must thank my teacher Prof. Arminda Manuela for the help during the entire project. Never was a help refused and when I was too enveloped in the project and needed space and autonomy felt no pressure to show results. And more than this project, all the work during the entire Master’s degree. Second I must thank Ana Freitas for the opportunity to work with their team at MC SONAE in developing this project. Working with the Adavanced Analitycs and Insights team was more than an exceptional learning experience, it was truly a pleasure. In here a special thanks to Ana Carvalho, that stood as my buddy during the entire time and who’s availability to help and work on bugs is nothing short of mind blowing! A thank you must be included for the remaining members of the AAI team. Every single one of it’s members contributed at some point to this project and more than exceptional data scientists are amazing people that I am glad to have met. Lastly a thank you to everyone that helped me during the entirety of the project. From family to friends, many are the ones to whom a thank you is owned for no gesture is to small to be thanked.  \nDECLARAÇÃO DE INTEGRIDADE  \nDeclaro ter atuado com integridade na elaboração do presente trabalho académico e confirmo que nãorecorri à prática de plágio nem a qualquer forma de utilização indevida ou falsificação de informações ou resultados em nenhuma das etapas conducente à sua elaboração.  \nMais declaro que conheço e que respeitei o Código de Conduta Ética da Universidade do Minho.  \n”Five years ago I looked at myself and said,”The worst dissertation is the one I don’t write”, before proceeding to write the second worst dissertation.”  \n@phil lol ogist on Twitter  \nAbstract  \nIn the realm of business, the cessation of relations with a company by its customers or can yield profound financial repercussions. Such consequences manifest as diminished revenue and profitability, while also bringing risk to the company’s reputation. Thus, comprehending the roots of customer churn is of major importance. Equally crucial is the formulation of effective strategies to mitigate churn, thereby enhancing customer satisfaction, retention, and overall profitability.  \nWithin the framework of this dissertation, clustering techniques were deployed alongside machine learning methodologies. This combination helped achieve a balance between accuracy and simplified model communication. In addition, data analysis techniques were deployed within the context of churn analysis, where total churn emerged as the optimal solution for the project, even if a case for partial churn could be made.  \nThe outcome was the development of a cohort of models capable of","cbCaigmXi0xHMfsF","https://ap.wps.com/l/cbCaigmXi0xHMfsF","pdf",3505392,3,1,76,"English","en",105,"# Introduction\n## Goals\n## Churn in Retail: The Problem and the Portuguese Reality\n## MC Sonae and its Loyalty Program\n# State of the Art\n## Dimensionality Reduction","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"The dissertation addresses customer churn in business, focusing on how understanding churn drivers helps reduce financial and reputational damage. It also supports retention efforts to improve satisfaction and retention.\"},{\"question\":\"How are machine learning methods used in the study?\",\"answer\":\"Clustering techniques are combined with machine learning methodologies. This design aims to achieve a balance between accuracy and easier communication of the models.\"},{\"question\":\"What is the main outcome of the developed models?\",\"answer\":\"The study produces a cohort of models that identify most churners within selected pilot retail categories. This enables the retention department to run more impactful retention campaigns and respond more effectively to churn.\"}]","Categories’ Churn - A Machine Learning Approach in Retail - Dissertação de Mestrado | PDF",1786002206,192,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"categories-churn-a-machine-learning-approach-in-retail-masters-dissertation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/categories-churn-a-machine-learning-approach-in-retail-masters-dissertation/128631/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",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},"What problem does the dissertation address?","Question",{"text":76,"@type":77},"The dissertation addresses customer churn in business, focusing on how understanding churn drivers helps reduce financial and reputational damage. 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