Media mix modeling: a case study on optimizing television and digital media spend for a retailer

Detalhes bibliográficos
Autor(a) principal: Rocha, José Francisco Sá Marques
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/10362/94986
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing Intelligence
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spelling Media mix modeling: a case study on optimizing television and digital media spend for a retailerMedia mix modelingMarketing mix modelingMarketing mix effectivenessMarketing budget allocationBudget allocation optimizationMarketing mixProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceRetailers invest most of their advertising budget in traditional channels, namely Television, even though the percentage of budget allocated towards digital media has been increasing. Since the largest part of sales still happen in physical stores, marketers face the challenge of optimizing their media mix to maximize revenue. To address this challenge, media mix models were developed using the traditional modeling approach, based on linear regressions, with data from a retailer’s advertising campaign, specifically the online and offline investments per channel and online conversion metrics. The models were influenced by the selection bias regarding funnel effects, which was exacerbated by the use of the last-touch attribution model that tends to disproportionately skew marketer investment away from higher funnel channels to lower-funnel. Nonetheless, results from the models suggest that online channels were more effective in explaining the variance of the number of participations, which were a proxy to sales. To managers, this thesis highlights that there are factors specific to their own campaigns that influence the media mix models, which they must consider and, if possible, control for. One factor is the selection biases, such as ad targeting that may arise from using the paid search channel or remarketing tactics, seasonality or the purchase funnel effects bias that undermines the contribution of higher-funnel channels like TV, which generates awareness in the target audience. Therefore, companies should assess which of these biases might have a bigger influence on their results and design their models accordingly. Data limitations are the most common constraint for marketing mix modeling. In this case, we did not have access to sales and media spend historical data. Therefore, it was not possible to understand what the uplift in sales caused by the promotion was, as well as to verify the impact of the promotion on items that were eligible to participate in the promotion, versus the items that were not. Also, we were not able to reduce the bias from the paid search channel because we lacked the search query data necessary to control for it and improve the accuracy of the models. Moreover, this project is not the ultimate solution for the “company’s” marketing measurement challenges but rather informs its next initiatives. It describes the state of the art in marketing mix modeling, reveals the limitations of the models developed and suggests ways to improve future models. In turn, this is expected to provide more accurate marketing measurement, and as a result, a media budget allocation that improves business performance.Pinto, Diego CostaVilares, Manuel JoséRUNRocha, José Francisco Sá Marques2020-03-25T11:48:31Z2020-03-092020-03-09T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/94986TID:202468518enginfo: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:RCAAP2024-03-11T04:43:00Zoai:run.unl.pt:10362/94986Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:38:11.025503Repositório 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 Media mix modeling: a case study on optimizing television and digital media spend for a retailer
title Media mix modeling: a case study on optimizing television and digital media spend for a retailer
spellingShingle Media mix modeling: a case study on optimizing television and digital media spend for a retailer
Rocha, José Francisco Sá Marques
Media mix modeling
Marketing mix modeling
Marketing mix effectiveness
Marketing budget allocation
Budget allocation optimization
Marketing mix
title_short Media mix modeling: a case study on optimizing television and digital media spend for a retailer
title_full Media mix modeling: a case study on optimizing television and digital media spend for a retailer
title_fullStr Media mix modeling: a case study on optimizing television and digital media spend for a retailer
title_full_unstemmed Media mix modeling: a case study on optimizing television and digital media spend for a retailer
title_sort Media mix modeling: a case study on optimizing television and digital media spend for a retailer
author Rocha, José Francisco Sá Marques
author_facet Rocha, José Francisco Sá Marques
author_role author
dc.contributor.none.fl_str_mv Pinto, Diego Costa
Vilares, Manuel José
RUN
dc.contributor.author.fl_str_mv Rocha, José Francisco Sá Marques
dc.subject.por.fl_str_mv Media mix modeling
Marketing mix modeling
Marketing mix effectiveness
Marketing budget allocation
Budget allocation optimization
Marketing mix
topic Media mix modeling
Marketing mix modeling
Marketing mix effectiveness
Marketing budget allocation
Budget allocation optimization
Marketing mix
description Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing Intelligence
publishDate 2020
dc.date.none.fl_str_mv 2020-03-25T11:48:31Z
2020-03-09
2020-03-09T00:00:00Z
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/10362/94986
TID:202468518
url http://hdl.handle.net/10362/94986
identifier_str_mv TID:202468518
dc.language.iso.fl_str_mv eng
language eng
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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
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