Pharmaceutical Sales Representatives´ Performance and Impact on Revenue - Using a Data Mining Approach

Detalhes bibliográficos
Autor(a) principal: Candeias, Leonor de Almeida
Data de Publicação: 2024
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/164187
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Pharmaceutical Sales Representatives´ Performance and Impact on Revenue - Using a Data Mining ApproachData MiningUnsupervised LearningClusteringPharmaceutical Sales RepresentativeSalesSDG 3 - Good health and well-beingSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureSDG 10 - Reduced inequalitiesSDG 12 - Responsible production and consumptionSDG 15 - Life on landSDG 16 - Peace, justice and strong institutionsDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceWith the growing number of health disorders on the global level, the pharmaceutical industry has evolved in developing and producing medicines and drugs that improve the quality of life. It is thus desired to make all pharmaceuticals reach a vast number of people worldwide and to fulfill that purpose more promptly the Pharmaceutical Sales Representatives are the vital reference for selling pharmaceuticals to physicians and all sorts of health companies. They are the main source of information for the latest drugs in the market including the ones from their own company and do not make direct sales as they only aim to build their network and relationships with potential clients by setting one-on-one meetings with physicians. In this context, the present research uses a Data Mining approach following the CRISP-DM methodology and Unsupervised Learning techniques to analyze the performance of a Pharmaceutical Sales Representatives team engaged with a certain pharmaceutical company over the years and the consequent impact that it has had in the company’s sales. This assessment was conducted by using a combination of Hierarchical Clustering with K-Modes to segment the data provided by the company and it presented four initial clusters, each demonstrating the level of acceptance of a meeting by a physician, the meeting’s most valuable Therapeutic areas, the number of products and physicians addressed and finally the meeting’s feedback provided from the Pharmaceutical Sales Representative after the visit. Finally, all clusters were merged using Hierarchical Clustering once more to avoid complexity and new other four segments were obtained describing four groups of interactions within the data. All results found met this research expectations and all types of interactions were explored to corroborate the effectiveness of the Pharmaceutical Sales Representatives team of the present pharmaceutical company.Castelli, MauroRUNCandeias, Leonor de Almeida2024-02-27T17:01:28Z2024-01-312024-01-31T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/164187TID:203530632enginfo: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:RCAAP2024-03-11T05:51:10Zoai:run.unl.pt:10362/164187Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:00:04.779522Repositório 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 Pharmaceutical Sales Representatives´ Performance and Impact on Revenue - Using a Data Mining Approach
title Pharmaceutical Sales Representatives´ Performance and Impact on Revenue - Using a Data Mining Approach
spellingShingle Pharmaceutical Sales Representatives´ Performance and Impact on Revenue - Using a Data Mining Approach
Candeias, Leonor de Almeida
Data Mining
Unsupervised Learning
Clustering
Pharmaceutical Sales Representative
Sales
SDG 3 - Good health and well-being
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 10 - Reduced inequalities
SDG 12 - Responsible production and consumption
SDG 15 - Life on land
SDG 16 - Peace, justice and strong institutions
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Pharmaceutical Sales Representatives´ Performance and Impact on Revenue - Using a Data Mining Approach
title_full Pharmaceutical Sales Representatives´ Performance and Impact on Revenue - Using a Data Mining Approach
title_fullStr Pharmaceutical Sales Representatives´ Performance and Impact on Revenue - Using a Data Mining Approach
title_full_unstemmed Pharmaceutical Sales Representatives´ Performance and Impact on Revenue - Using a Data Mining Approach
title_sort Pharmaceutical Sales Representatives´ Performance and Impact on Revenue - Using a Data Mining Approach
author Candeias, Leonor de Almeida
author_facet Candeias, Leonor de Almeida
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Candeias, Leonor de Almeida
dc.subject.por.fl_str_mv Data Mining
Unsupervised Learning
Clustering
Pharmaceutical Sales Representative
Sales
SDG 3 - Good health and well-being
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 10 - Reduced inequalities
SDG 12 - Responsible production and consumption
SDG 15 - Life on land
SDG 16 - Peace, justice and strong institutions
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Data Mining
Unsupervised Learning
Clustering
Pharmaceutical Sales Representative
Sales
SDG 3 - Good health and well-being
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 10 - Reduced inequalities
SDG 12 - Responsible production and consumption
SDG 15 - Life on land
SDG 16 - Peace, justice and strong institutions
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2024
dc.date.none.fl_str_mv 2024-02-27T17:01:28Z
2024-01-31
2024-01-31T00:00:00Z
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/10362/164187
TID:203530632
url http://hdl.handle.net/10362/164187
identifier_str_mv TID:203530632
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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