Estrategias inteligentes para un marketing más personalizado Smart strategies for more personalized marketing
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jul 7, 2026
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https://doi.org/10.30878/ces.v33n0a67
Main Article Content
Juan Carlos Leyva López
http://orcid.org/0000-0002-4821-6324
Omar Alejandro Reyna Gutiérrez
http://orcid.org/0009-0006-6605-7466
http://orcid.org/0000-0002-4821-6324
Omar Alejandro Reyna Gutiérrez
http://orcid.org/0009-0006-6605-7466
Resumen
Este artículo tiene como objetivo analizar cómo la inteligencia artificial, la ciencia de datos y el análisis multicriterio transforman el marketing dirigido, mejorando la personalización y la toma de decisiones. La metodología consiste en una revisión sistemática documental de literatura científica en Scopus, Web of Science y Google Scholar, aplicando criterios de inclusión y exclusión basados en pertinencia, calidad y actualidad. Los resultados evidencian que estas tecnologías superan las limitaciones del marketing tradicional al integrar múltiples fuentes de datos y criterios, permitiendo segmentaciones más precisas y recomendaciones personalizadas. Se concluye que su adopción optimiza las campañas, incrementa la eficiencia y favorece estrategias sostenibles, incluso en pequeñas y medianas empresas.
Article Details
Como citar
LEYVA LÓPEZ, Juan Carlos; REYNA GUTIÉRREZ, Omar Alejandro.
Estrategias inteligentes para un marketing más personalizado.
CIENCIA ergo-sum, [S.l.], v. 33, jul. 2026.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/26182>. Fecha de acceso: 18 ago. 2026
doi: https://doi.org/10.30878/ces.v33n0a67.
Sección
Ciencias sociales
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
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Willetts, M., Atkins, A. S., & Stanier, C. (2020). Barriers to SMEs Adoption of Big Data Analytics for Competitive Advantage. 2020 Fourth International Conference on Intelligent Computing in Data Sciences (ICDS), 1–8. https://doi.org/10.1109/ICDS50568.2020.9268687
Yin, B., Wei, X., & Liu, Y. (2019). Finding the most influential product under distribution constraints through dominance tests. Applied Intelligence, 49, 2723–2740. https://doi.org/10.1007/s10489-018-1293-0
Yoganarasimhan, H. (2020) Search personalization using machine learning. Management Science. 66(3):1045–1070.
Wang, Y., Tao, L., & Zhang X.X. (2025) Recommending for a Multi-Sided Marketplace: A Multi-Objective Hierarchical Approach. Marketing Science 44(1):1-29. https://doi.org/10.1287/mksc.2022.0238
Zhao Z, Hong L, Wei L, Chen J, Nath A, Andrews S, Kumthekar A, et al. (2019) Recommending what video to watch next: A multi-task ranking system. Recsys 19:43–51.
CC BY-NC-ND
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749. https://doi.org/10.1109/TKDE.2005.99
Ahmed, M. Z., Singh, A., Paul, A., Ghosh, S., & Chaudhuri, A. K. (2022). Amazon product recommendation system. International Journal of Advanced Research in Computer and Communication Engineering, 11(3). https://doi.org/10.17148/IJARCCE.2022.11356
Alfaifi, Y. H. (2024). Recommender Systems Applications: Data Sources, Features, and Challenges. Information, 15(10), 660. https://doi.org/10.3390/info15100660
Chandra, S., Verma, S., Lim, W. M., Kumar, S., & Donthu, N. (2022). Personalization in personalized marketing: Trends and ways forward. Psychology & Marketing, 39(8), 1529–1562. https://doi.org/10.1002/mar.21670
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42. https://doi.org/10.1007/s11747-019-00696-0
Denecke, K., Bamidis, P., Bond, C., Gabarron, E., Househ, M., Lau, A. Y., Mayer, M. A., Merolli, M., & Hansen, M. (2015). Ethical issues of social media usage in healthcare. Yearbook of Medical Informatics, 10(1), 137–147. https://doi.org/10.15265/IY-2015-001
Gómez-Uribe, C. A., & Hunt, N. (2015). The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19. DOI: http://dx.doi.org/10.1145/2843948
Gonçalves, A. R., Costa, P. D., Shuqair, S., Dalmoro, M., Mattila, A. S., Artificial intelligence vs. autonomous decision-making in streaming platforms: A mixed-method approach, International Journal of Information Management, 76, 2024, 102748, https://doi.org/10.1016/j.ijinfomgt.2023.102748
Hadad, Y., & Keren, B. (2022). A decision‐making support system module for customer segmentation and ranking. Expert Systems. https://doi.org/10.1111/exsy.13169
Huang, J., Zhong, N., & Yao, Y. (2014). A Unified Framework of Targeted Marketing Using Customer Preferences. Computational Intelligence, 30(3), 472–496.
Huang, S., & Lee, Y. (2021). Identifying customer priority for new products in target marketing: Using RFM model and TextRank. Innovative Marketing, 17(2), 125–136. https://doi.org/10.21511/im.17(2).2021.12
Kannan, P. K., & Li, H. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22-45. https://doi.org/10.1016/j.ijresmar.2016.11.006
Leyva López, J. C., & Reyna Gutiérrez, O. A. (2025). Finding Top-K Preferable Products for Customer-Oriented Marketing Based on the Outranking Approach: A Case Study on Mexican Restaurants. Journal of Universal Computer Science, 31(3), 210–238. https://doi.org/10.3897/jucs.150597
Lin, C.-Y., Koh, J.-L., & Chen, A.L.P. (2013). Determining (k)-Most Demanding Products with Maximum Expected Number of Total Customers. IEEE Transactions on Knowledge and Data Engineering, 25(8), 1732–1747. https://doi.org/10.1109/TKDE.2012.53
Linden, G., Smith, B., & York, J. (2003). Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 7(1), 76-80.
Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135-155. https://doi.org/10.1007/s11747-016-0495-4
Namvar, A., Ghazanfari, M. & Naderpour, M., A customer segmentation framework for targeted marketing in telecommunication, 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, China, 2017, pp. 1-6, https://doi: 10.1109/ISKE.2017.8258803
Ngai, E. W., Xiu, L., & Chau, D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592-2602. https://doi.org/10.1016/j.eswa.2008.02.021
Roy, B. (1996). Multicriteria methodology for decision aiding. Springer.
Tadajewski, M., & Brownlie, D. (2008). Critical marketing: Issues in contemporary marketing. Wiley.
Theodorakopoulos, L., & Theodoropoulou, A. (2024). Leveraging big data analytics for understanding consumer behavior in digital marketing: A systematic review. Human Behavior and Emerging Technologies, Article 3641502, 21 pages. https://doi.org/10.1155/2024/3641502
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413
Wenming, M., Junfeng, S., & Ruidong, Z. (2017). Normalizing Item-Based Collaborative Filter Using Context-Aware Scaled Baseline Predictor, Mathematical Problems in Engineering, 6562371. https://doi.org/10.1155/2017/6562371
Willetts, M., Atkins, A. S., & Stanier, C. (2020). Barriers to SMEs Adoption of Big Data Analytics for Competitive Advantage. 2020 Fourth International Conference on Intelligent Computing in Data Sciences (ICDS), 1–8. https://doi.org/10.1109/ICDS50568.2020.9268687
Yin, B., Wei, X., & Liu, Y. (2019). Finding the most influential product under distribution constraints through dominance tests. Applied Intelligence, 49, 2723–2740. https://doi.org/10.1007/s10489-018-1293-0
Yoganarasimhan, H. (2020) Search personalization using machine learning. Management Science. 66(3):1045–1070.
Wang, Y., Tao, L., & Zhang X.X. (2025) Recommending for a Multi-Sided Marketplace: A Multi-Objective Hierarchical Approach. Marketing Science 44(1):1-29. https://doi.org/10.1287/mksc.2022.0238
Zhao Z, Hong L, Wei L, Chen J, Nath A, Andrews S, Kumthekar A, et al. (2019) Recommending what video to watch next: A multi-task ranking system. Recsys 19:43–51.
CC BY-NC-ND