Building a predictive lead scoring model for contact prioritization : the case of HUUB
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.14/34877 |
Resumo: | In the last decades, machine learning has become quite popular for solving business problems, as it often delivers high-quality and efficient solutions. Moreover, the amount of data collected by companies has grown substantially, which has contributed to this trend. Companies do not have enough resources to contact every lead, so contact prioritization is essential. Lead scoring supports this task, by assigning a value to each lead based on his actions or characteristics. Even though it is expected that lead scoring contributes to higher conversion rates, there is still very few literature on how to use machine learning to automate this process. This dissertation shows how to combine historical data from Customer Relationship Management platforms and supervised learning to develop a lead scoring model for companies. The approach followed is based on the CRISP-DM method, where several tools were used, such as HubSpot, Microsoft Power BI and RStudio. The classification model proposed is a decision tree that predicts the leads’ conversion outcome (Won or Postpone), developed using the CART algorithm and data from a logistics company – HUUB. The main findings of this project conclude that machine learning can be used to develop a lead scoring model to perform contact prioritization. However, there are several factors, especially data-related, that should be taken into consideration, since they may impact the model’s performance. Lastly, a suggestion for future research is to develop an experiment to compare the results of manual and automated lead scoring, to assess if machine learning actually provides a superior alternative to the manual approach. |
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Building a predictive lead scoring model for contact prioritization : the case of HUUBMarketing automationLead scoringMachine learningContact prioritizationAutomatização do marketingPriorização de contactosDomínio/Área Científica::Ciências Sociais::Economia e GestãoIn the last decades, machine learning has become quite popular for solving business problems, as it often delivers high-quality and efficient solutions. Moreover, the amount of data collected by companies has grown substantially, which has contributed to this trend. Companies do not have enough resources to contact every lead, so contact prioritization is essential. Lead scoring supports this task, by assigning a value to each lead based on his actions or characteristics. Even though it is expected that lead scoring contributes to higher conversion rates, there is still very few literature on how to use machine learning to automate this process. This dissertation shows how to combine historical data from Customer Relationship Management platforms and supervised learning to develop a lead scoring model for companies. The approach followed is based on the CRISP-DM method, where several tools were used, such as HubSpot, Microsoft Power BI and RStudio. The classification model proposed is a decision tree that predicts the leads’ conversion outcome (Won or Postpone), developed using the CART algorithm and data from a logistics company – HUUB. The main findings of this project conclude that machine learning can be used to develop a lead scoring model to perform contact prioritization. However, there are several factors, especially data-related, that should be taken into consideration, since they may impact the model’s performance. Lastly, a suggestion for future research is to develop an experiment to compare the results of manual and automated lead scoring, to assess if machine learning actually provides a superior alternative to the manual approach.Nas últimas décadas, o machine learning tornou-se bastante popular para resolver problemas organizacionais, já que tende a produzir soluções eficientes e de alta qualidade. Adicionalmente, a quantidade de dados colecionados pelas empresas cresceu substancialmente, o que contribuiu para esta tendência. As empresas não têm recursos suficientes para contactar todos os leads, pelo que é essencial priorizá-los. O lead scoring apoia esta tarefa, ao atribuir um valor para cada lead baseado nas suas ações ou características. Embora seja expectável que o lead scoring contribua para melhores taxas de conversão, ainda é escassa a literatura acerca da automatização deste processo através do machine learning. Esta dissertação expõe como combinar supervised learning e dados históricos de sistemas de Customer Relationship Management para desenvolver um modelo de lead scoring para empresas. A abordagem baseia-se no método CRISP-DM, onde diversas ferramentas foram usadas, nomeadamente o HubSpot, o Microsoft Power BI e o RStudio. O modelo de classificação proposto é uma árvore de decisão que prevê o desfecho de conversão dos leads, desenvolvido com o algoritmo CART e dados de uma empresa de logística – a HUUB. As principais descobertas deste projeto concluem que é viável utilizar o machine learning para desenvolver um modelo de lead scoring para priorizar os contactos. Contudo, há fatores que devem ser tidos em conta, especialmente relacionados com os dados, já que podem impactar o desempenho do modelo. Por fim, sugere-se para pesquisa futura o desenvolvimento de um estudo experimental que compare os resultados do lead scoring automatizado e manual, de forma a avaliar se o machine learning é de facto a melhor alternativa.Andrade, António Manuel Valente deVeritati - Repositório Institucional da Universidade Católica PortuguesaPereira, Rita Mafalda Magalhães2021-09-15T11:27:21Z2021-07-132021-052021-07-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.14/34877TID:202750051enginfo: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:RCAAP2023-07-12T17:40:24Zoai:repositorio.ucp.pt:10400.14/34877Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:28:16.934234Repositório 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 |
Building a predictive lead scoring model for contact prioritization : the case of HUUB |
title |
Building a predictive lead scoring model for contact prioritization : the case of HUUB |
spellingShingle |
Building a predictive lead scoring model for contact prioritization : the case of HUUB Pereira, Rita Mafalda Magalhães Marketing automation Lead scoring Machine learning Contact prioritization Automatização do marketing Priorização de contactos Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
Building a predictive lead scoring model for contact prioritization : the case of HUUB |
title_full |
Building a predictive lead scoring model for contact prioritization : the case of HUUB |
title_fullStr |
Building a predictive lead scoring model for contact prioritization : the case of HUUB |
title_full_unstemmed |
Building a predictive lead scoring model for contact prioritization : the case of HUUB |
title_sort |
Building a predictive lead scoring model for contact prioritization : the case of HUUB |
author |
Pereira, Rita Mafalda Magalhães |
author_facet |
Pereira, Rita Mafalda Magalhães |
author_role |
author |
dc.contributor.none.fl_str_mv |
Andrade, António Manuel Valente de Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Pereira, Rita Mafalda Magalhães |
dc.subject.por.fl_str_mv |
Marketing automation Lead scoring Machine learning Contact prioritization Automatização do marketing Priorização de contactos Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
Marketing automation Lead scoring Machine learning Contact prioritization Automatização do marketing Priorização de contactos Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
In the last decades, machine learning has become quite popular for solving business problems, as it often delivers high-quality and efficient solutions. Moreover, the amount of data collected by companies has grown substantially, which has contributed to this trend. Companies do not have enough resources to contact every lead, so contact prioritization is essential. Lead scoring supports this task, by assigning a value to each lead based on his actions or characteristics. Even though it is expected that lead scoring contributes to higher conversion rates, there is still very few literature on how to use machine learning to automate this process. This dissertation shows how to combine historical data from Customer Relationship Management platforms and supervised learning to develop a lead scoring model for companies. The approach followed is based on the CRISP-DM method, where several tools were used, such as HubSpot, Microsoft Power BI and RStudio. The classification model proposed is a decision tree that predicts the leads’ conversion outcome (Won or Postpone), developed using the CART algorithm and data from a logistics company – HUUB. The main findings of this project conclude that machine learning can be used to develop a lead scoring model to perform contact prioritization. However, there are several factors, especially data-related, that should be taken into consideration, since they may impact the model’s performance. Lastly, a suggestion for future research is to develop an experiment to compare the results of manual and automated lead scoring, to assess if machine learning actually provides a superior alternative to the manual approach. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-15T11:27:21Z 2021-07-13 2021-05 2021-07-13T00:00:00Z |
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/10400.14/34877 TID:202750051 |
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http://hdl.handle.net/10400.14/34877 |
identifier_str_mv |
TID:202750051 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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