Marketing Attribution in B2B Companies: Associating software and customer data to increase confidence in data-driven channel optimization decisions

Detalhes bibliográficos
Autor(a) principal: Santana, Rafael de Oliveira
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/159890
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and Analytics
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spelling Marketing Attribution in B2B Companies: Associating software and customer data to increase confidence in data-driven channel optimization decisionsIndustrial marketingMarketing attributionAttribution modelingDigital marketingData-driven marketingCustomer insightsSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoProject Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and AnalyticsTo build a solid marketing and sales process, B2B companies invest in robust CRM and Marketing automation systems to help teams to identify and manage companies, contacts, interactions, sales opportunities and more. These systems offer several report types for different roles within the company, being the Marketing Attribution one of the most important to marketing leaders when analyzing what are the campaigns, channels and tactics that drive more revenue. This information is key when choosing which channels companies should allocate budget, but these tools can’t track all touchpoints in the buying journey, which can lead marketing leaders to make misleading decisions on where to invest their budgets. This research tested and analyzed an experiment applied in a prominent B2B company that added a layer of customer-level sourced data to what is tracked by the attribution tool,suggesting a hybrid attribution model that is able to identify hidden touch points and re-distribute the weights of credits attributed. To perform the study, an experimental research collected and analyzed data from a sample of 220 purchase paths, discovering that marketing attribution models widely adopted by companies can be significantly improved when granular user-level data is added to the view. It has significant managerial contributions as marketing leaders can do a better channel performance analysis and make more informed decisions when allocating investments.Dalmoro, MarlonRUNSantana, Rafael de Oliveira2023-11-13T11:29:20Z2023-10-242023-10-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/159890TID:203384407enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:15:39Zoai:run.unl.pt:10362/159890Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:46:24.309622Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Marketing Attribution in B2B Companies: Associating software and customer data to increase confidence in data-driven channel optimization decisions
title Marketing Attribution in B2B Companies: Associating software and customer data to increase confidence in data-driven channel optimization decisions
spellingShingle Marketing Attribution in B2B Companies: Associating software and customer data to increase confidence in data-driven channel optimization decisions
Santana, Rafael de Oliveira
Industrial marketing
Marketing attribution
Attribution modeling
Digital marketing
Data-driven marketing
Customer insights
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Marketing Attribution in B2B Companies: Associating software and customer data to increase confidence in data-driven channel optimization decisions
title_full Marketing Attribution in B2B Companies: Associating software and customer data to increase confidence in data-driven channel optimization decisions
title_fullStr Marketing Attribution in B2B Companies: Associating software and customer data to increase confidence in data-driven channel optimization decisions
title_full_unstemmed Marketing Attribution in B2B Companies: Associating software and customer data to increase confidence in data-driven channel optimization decisions
title_sort Marketing Attribution in B2B Companies: Associating software and customer data to increase confidence in data-driven channel optimization decisions
author Santana, Rafael de Oliveira
author_facet Santana, Rafael de Oliveira
author_role author
dc.contributor.none.fl_str_mv Dalmoro, Marlon
RUN
dc.contributor.author.fl_str_mv Santana, Rafael de Oliveira
dc.subject.por.fl_str_mv Industrial marketing
Marketing attribution
Attribution modeling
Digital marketing
Data-driven marketing
Customer insights
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Industrial marketing
Marketing attribution
Attribution modeling
Digital marketing
Data-driven marketing
Customer insights
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and Analytics
publishDate 2023
dc.date.none.fl_str_mv 2023-11-13T11:29:20Z
2023-10-24
2023-10-24T00:00:00Z
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/10362/159890
TID:203384407
url http://hdl.handle.net/10362/159890
identifier_str_mv TID:203384407
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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