Applying knowledge transfer in data augmentation to improve online advertising performance of entrepreneurs

Artificial intelligence (AI) is transforming the way businesses operate, enabling entrepreneurs to achieve diagnoses that were once only possible for large companies. This transformation is evident in digital advertising, where AI not only enables advanced analytics, but also offers the possibility...

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Detalles Bibliográficos
Autores: Huertas García, Rubén, Sáez Ortuño, Laura, Forgas Coll, Santiago, Sánchez García, Javier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/223987
Acceso en línea:https://hdl.handle.net/2445/223987
Access Level:acceso abierto
Palabra clave:Intel·ligència artificial
Algorismes computacionals
Segmentació de mercat
Artificial intelligence
Computer algorithms
Market segmentation
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spelling Applying knowledge transfer in data augmentation to improve online advertising performance of entrepreneursHuertas García, RubénSáez Ortuño, LauraForgas Coll, SantiagoSánchez García, JavierIntel·ligència artificialAlgorismes computacionalsSegmentació de mercatArtificial intelligenceComputer algorithmsMarket segmentationArtificial intelligence (AI) is transforming the way businesses operate, enabling entrepreneurs to achieve diagnoses that were once only possible for large companies. This transformation is evident in digital advertising, where AI not only enables advanced analytics, but also offers the possibility of developing creative designs at low cost. However, this technological progress contrasts with predictions of a slowdown in online advertising in the coming years. Thus, entrepreneurs must change their strategies to overcome the defensive positions of competitors. This study proposes the combination of AI analytical algorithms (XGBoost) with data augmentation algorithms (SMOTE) to improve targeting accuracy when launching online communication campaigns. Specifically, a case study illustrates how a lead-gathering company uses these algorithms to profile five market segments (hearing aids, NGOs, energy distributors, telecommunications and finance). Subsequently, a field experiment was conducted with one of the products, solar panels, to assess external validity. The results reveal that the combination of both algorithms improves internal validity for four of the five products, and the field experiment confirms the external validity of the energy product. Finally, recommendations on the use of these tools are proposed to entrepreneurs.Elsevier B.V.2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/223987Articles publicats en revistes (Empresa)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1016/j.jik.2025.100828Journal of Innovation & Knowledge, 2025, vol. 10, núm. 6https://doi.org/10.1016/j.jik.2025.100828cc-by (c) Huertas García, Rubén et al., 2025http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2239872026-05-27T06:46:51Z
dc.title.none.fl_str_mv Applying knowledge transfer in data augmentation to improve online advertising performance of entrepreneurs
title Applying knowledge transfer in data augmentation to improve online advertising performance of entrepreneurs
spellingShingle Applying knowledge transfer in data augmentation to improve online advertising performance of entrepreneurs
Huertas García, Rubén
Intel·ligència artificial
Algorismes computacionals
Segmentació de mercat
Artificial intelligence
Computer algorithms
Market segmentation
title_short Applying knowledge transfer in data augmentation to improve online advertising performance of entrepreneurs
title_full Applying knowledge transfer in data augmentation to improve online advertising performance of entrepreneurs
title_fullStr Applying knowledge transfer in data augmentation to improve online advertising performance of entrepreneurs
title_full_unstemmed Applying knowledge transfer in data augmentation to improve online advertising performance of entrepreneurs
title_sort Applying knowledge transfer in data augmentation to improve online advertising performance of entrepreneurs
dc.creator.none.fl_str_mv Huertas García, Rubén
Sáez Ortuño, Laura
Forgas Coll, Santiago
Sánchez García, Javier
author Huertas García, Rubén
author_facet Huertas García, Rubén
Sáez Ortuño, Laura
Forgas Coll, Santiago
Sánchez García, Javier
author_role author
author2 Sáez Ortuño, Laura
Forgas Coll, Santiago
Sánchez García, Javier
author2_role author
author
author
dc.subject.none.fl_str_mv Intel·ligència artificial
Algorismes computacionals
Segmentació de mercat
Artificial intelligence
Computer algorithms
Market segmentation
topic Intel·ligència artificial
Algorismes computacionals
Segmentació de mercat
Artificial intelligence
Computer algorithms
Market segmentation
description Artificial intelligence (AI) is transforming the way businesses operate, enabling entrepreneurs to achieve diagnoses that were once only possible for large companies. This transformation is evident in digital advertising, where AI not only enables advanced analytics, but also offers the possibility of developing creative designs at low cost. However, this technological progress contrasts with predictions of a slowdown in online advertising in the coming years. Thus, entrepreneurs must change their strategies to overcome the defensive positions of competitors. This study proposes the combination of AI analytical algorithms (XGBoost) with data augmentation algorithms (SMOTE) to improve targeting accuracy when launching online communication campaigns. Specifically, a case study illustrates how a lead-gathering company uses these algorithms to profile five market segments (hearing aids, NGOs, energy distributors, telecommunications and finance). Subsequently, a field experiment was conducted with one of the products, solar panels, to assess external validity. The results reveal that the combination of both algorithms improves internal validity for four of the five products, and the field experiment confirms the external validity of the energy product. Finally, recommendations on the use of these tools are proposed to entrepreneurs.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/223987
url https://hdl.handle.net/2445/223987
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1016/j.jik.2025.100828
Journal of Innovation & Knowledge, 2025, vol. 10, núm. 6
https://doi.org/10.1016/j.jik.2025.100828
dc.rights.none.fl_str_mv cc-by (c) Huertas García, Rubén et al., 2025
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Huertas García, Rubén et al., 2025
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Articles publicats en revistes (Empresa)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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