Gallego Hidalgo, V.; Lingan, J.; Freixes, A.; Juan, AA.; Osorio-Muñoz, C. (2024). Applying Machine Learning in Marketing: An Analysis Using the NMF and k-Means Algorithms. Information. 15(7). https://doi.org/10.3390/info15070368
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/209464
Title:
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Applying Machine Learning in Marketing: An Analysis Using the NMF and k-Means Algorithms
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Author:
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Gallego Hidalgo, Víctor
Lingan, Jessica
Freixes, Alfons
Juan, Angel A.
Osorio-Muñoz, Celia
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UPV Unit:
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Universitat Politècnica de València. Escuela Politécnica Superior de Alcoy - Escola Politècnica Superior d'Alcoi
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Issued date:
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Abstract:
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[EN] The integration of machine learning (ML) techniques into marketing strategies has become increasingly relevant in modern business. Utilizing scientific manuscripts indexed in the Scopus database, this article explores ...[+]
[EN] The integration of machine learning (ML) techniques into marketing strategies has become increasingly relevant in modern business. Utilizing scientific manuscripts indexed in the Scopus database, this article explores how this integration is being carried out. Initially, a focused search is undertaken for academic articles containing both the terms "machine learning" and "marketing" in their titles, which yields a pool of papers. These papers have been processed using the Supabase platform. The process has included steps like text refinement and feature extraction. In addition, our study uses two key ML methodologies: topic modeling through NMF and a comparative analysis utilizing the k-means clustering algorithm. Through this analysis, three distinct clusters emerged, thus clarifying how ML techniques are influencing marketing strategies, from enhancing customer segmentation practices to optimizing the effectiveness of advertising campaigns.
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Subjects:
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Machine learning
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Digital marketing
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Algorithms
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Artificial intelligence
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Copyrigths:
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Reconocimiento (by)
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Source:
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Information. (eissn:
2078-2489
)
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DOI:
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10.3390/info15070368
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Publisher:
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MDPI AG
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Publisher version:
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https://doi.org/10.3390/info15070368
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Project ID:
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-138860NB-I00/ES/INTELIGENCIA ARTIFICIAL E INTERNET DE LAS COSAS PARA OPTIMIZAR EL CONSUMO ENERGETICO EN EL TRANSPORTE CON VEHICULOS ELECTRICOS/
info:eu-repo/grantAgreement/GVA//INVEST%2F2023%2F304/
info:eu-repo/grantAgreement/MICINN//RED2022-134703-T/
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Thanks:
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This work was founded by the Investigo Program of the Generalitat Valenciana (INVEST/2023/304), Coca-Cola Europacific Partners, and the Spanish Ministry of Science and Innovation (PID2022-138860NB-I00 and RED2022-134703-T).[+]
This work was founded by the Investigo Program of the Generalitat Valenciana (INVEST/2023/304), Coca-Cola Europacific Partners, and the Spanish Ministry of Science and Innovation (PID2022-138860NB-I00 and RED2022-134703-T).
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Type:
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Artículo
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