Enhanced web analytics for health insurance

Detalhes bibliográficos
Autor(a) principal: Maggi, Piero
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/101010
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Enhanced web analytics for health insuranceWeb AnalyticsGoogle AnalyticsSupervised LearningDecision TreeLogistic regressionRandom ForestUser BehaviorInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsNowadays companies need invest and improve on data solution implementation within most of the business workflows and processes, in order to differentiate the offer and stay ahead of their competitors. It’s becoming more and more important to take data driven decisions to boost profitability and improve the overall customer experience. In this way, strategies are defined not anymore on common beliefs and assumptions, but on contextualized and trustful insights. This reports describes the work that has been made during a 9-month internship, in order to provide the business with a new and improved solution for enhancing the web analytics tasks and supporting the improve of the online user digital experience. User-level data related to the website activity has been extracted at the highest granularity level. Afterwards, raw data have been cleaned and stored in an Analytical Base Table with which an initial data exploration has been made. After giving initial insights to the digital team, a predictive model has been developed in order to predict the probability of the users to buy the insurance product online. Finally, based on the initial data exploration and the model’s results, a set of recommendations has been built and provided to the digital department for their implementation in order to make the website more engaging and dynamic.Vanneschi, LeonardoRUNMaggi, Piero2020-07-17T17:13:09Z2020-06-022020-06-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/101010TID:202501507enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:47:22Zoai:run.unl.pt:10362/101010Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:29.363709Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Enhanced web analytics for health insurance
title Enhanced web analytics for health insurance
spellingShingle Enhanced web analytics for health insurance
Maggi, Piero
Web Analytics
Google Analytics
Supervised Learning
Decision Tree
Logistic regression
Random Forest
User Behavior
title_short Enhanced web analytics for health insurance
title_full Enhanced web analytics for health insurance
title_fullStr Enhanced web analytics for health insurance
title_full_unstemmed Enhanced web analytics for health insurance
title_sort Enhanced web analytics for health insurance
author Maggi, Piero
author_facet Maggi, Piero
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Maggi, Piero
dc.subject.por.fl_str_mv Web Analytics
Google Analytics
Supervised Learning
Decision Tree
Logistic regression
Random Forest
User Behavior
topic Web Analytics
Google Analytics
Supervised Learning
Decision Tree
Logistic regression
Random Forest
User Behavior
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2020
dc.date.none.fl_str_mv 2020-07-17T17:13:09Z
2020-06-02
2020-06-02T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/101010
TID:202501507
url http://hdl.handle.net/10362/101010
identifier_str_mv TID:202501507
dc.language.iso.fl_str_mv eng
language eng
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dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
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