Integración de LLMs en procesos de Diagnóstico situacional de Marketing Digital
Palabras clave:
Integración, LLMS, Diagnostico situacional, Marketing Digital
Resumen
El rápido auge de los Grandes Modelos de Lenguaje (LLMs) ha propiciado una notable oportunidad de cambiar radicalmente el marketing digital y todo lo referente a los diagnósticos de situación. El presente artículo de revisión narrativa basado en fuentes indexadas de Scopus, Web of Science y Google Scholar (2017-2024), examina de qué forma la implementación de los LLMs cambia la capacidad organizacional de detectar, reconocer y entender mejor, e incluso más rápido que las metodologías convencionales. Los resultados señalan que los LLMs no sustituyen la analítica tradicional, sino que la complementan al acortar los plazos para la detección o expansión de la precisión analítica o escalar diagnósticos estratégicos más allá de la recurrencia manual. No obstante, se especifica que el uso de los LLMs requiere marcos éticos explícitos, supervisión de los humanos en nodos considerados como críticos y una cultura organizacional que conecte el procesamiento automatizado con el juicio situacional. De este modo, se cierra el artículo comentando que los LLMs representan una oportunidad real para agilizar las decisiones en marketing, en un mercado digital volátil, empero, que su adopción cumpla con criterios de uso responsable y constructos de uso sensible desde la óptica cultural.
Citas
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Kim, A. J., & Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. Journal of Business Research, 65(10), 1480–1486. DOI: https://doi.org/10.1016/j.jbusres.2011.10.014
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Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2022). RoBERTa: A robustly optimized BERT pretraining approach [Preprint]. arXiv. Recuperado de https://arxiv.org/abs/1907.11692
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Rust, R. T., & Huang, M. H. (2021). The feeling economy: How artificial intelligence is creating the era of empathy. Palgrave Macmillan. DOI: https://doi.org/10.1007/978-3-030-52977-2
Shaliakhina, T., & Butyrina, M. (2022). Situational marketing as a communication technology. Obraz, 3(41), 9–17
Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2022). Learning from the dark web: Leveraging conversational agents in the era of hyper-personalization to enhance marketing performance. Journal of the Academy of Marketing Science, 48(1), 43–63. DOI: https://doi.org/10.1007/s11747-019-00704-3
Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review, 15(4), 404–428. DOI: https://doi.org/10.1177/1534484316671606
Vargo, S. L., & Lusch, R. F. (2016). Institutions and axioms: An extension and update of service-dominant logic. Journal of the Academy of Marketing Science, 44(1), 5–23. DOI: https://doi.org/10.1007/s11747-015-0456-3
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. En Advances in Neural Information Processing Systems. Curran Associates. Recuperado de https://arxiv.org/abs/1706.03762
Verma, S., Sharma, R., Deb, S., y Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1. DOI: https://doi.org/10.1016/j.jjimei.2020.100002
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. En Advances in Neural Information Processing Systems. Curran Associates. Recuperado de https://arxiv.org/abs/2201.11903
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., Kenton, Z., Brown, S., Hawkins, W., Stepleton, T., Biles, C., Birhane, A., Haas, J., Rimell, L., Hendricks, L. A., & Gabriel, I. (2021). Ethical and social risks of harm from language models. arXiv. Recuperado de https://arxiv.org/abs/2112.04359
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P. S., Mellor, J., & Gabriel, I. (2022, June). Taxonomy of risks posed by language models. In Proceedings of the 2022 ACM conference on fairness, accountability, and transparency, 214-229. DOI: https://doi.org/10.1145/3531146.3533088
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2022). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. DOI: https://doi.org/10.1108/JOSM-04-2018-0119
The Feeling Economy: How Artificial Intelligence Is Creating the Era of Empathy, por R. T. Rust y M. H. Huang, 2021, Palgrave Macmillan. DOI: https://doi.org/10.1007/978-3-030-52977-2
Bogdan, I. (2022). Situational marketing as a tool of effective promotion. Scientific Works of the National Aviation University, 2(91), 295–302. DOI: https://doi.org/10.18372/2306-1472.91.16694
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., & Liang, P. (2021). On the opportunities and risks of foundation models [Preprint]. arXiv. Recuperado de https://arxiv.org/abs/2108.07258
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. DOI: https://doi.org/10.1191/1478088706qp063oa
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. Recuperado de https://arxiv.org/abs/2005.14165
Chen, X. (2023). Analysis of Pepsi's advertising marketing strategy. Highlights in Business, Economics and Management, 19, 244–249
Davenport, T. H., Guha, A., & Grewal, D. (2021). How to design an AI marketing strategy. Harvard Business Review, 99(4), 42–47. Recuperado de https://hbr.org/2021/07/how-to-design-an-ai-marketing-strategy
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. DOI: https://doi.org/10.1007/s11747-019-00696-0
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Artículo 101994. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Ferrari, R. (2015). Writing narrative style literature reviews. Medical Writing, 24(4), 230–235. DOI: https://doi.org/10.1179/2047480615Z.000000000329
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2020). An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. En L. Floridi (Ed.), The ethics of artificial intelligence (pp. 45–73). Oxford University Press. DOI: https://doi.org/10.1007/s11023-018-9482-5
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2013). Electronic word-of-mouth via consumer-opinion platforms: ¿What motivates consumers to articulate themselves on the internet?. Journal of Interactive Marketing, 18(1), 38–52. DOI: https://doi.org/10.1002/dir.10073
Hernández, R., Fernández, C., y Baptista, P. (2014). Metodología de la investigación (6.ª ed.). McGraw-Hill
Herold, M., Ma, S., He, H., Ghadge, S., Wang, X., & Zettlemoyer, L. (2024). Language model adaptation to specialized domains through selective masking based on genre and topical characteristics [Preprint]. arXiv. Recuperado https://arxiv.org/abs/2402.12036
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. DOI: https://doi.org/10.1007/s11747-020-00749-9
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. DOI: https://doi.org/10.1016/j.bushor.2018.08.004
Kim, A. J., & Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. Journal of Business Research, 65(10), 1480–1486. DOI: https://doi.org/10.1016/j.jbusres.2011.10.014
Kırtıl, İ. G., & Aşkun, V. (2021). Artificial intelligence in tourism: A review and bibliometrics research. Advances in Hospitality and Tourism Research (AHTR), 9(1), 205–233. DOI: https://doi.org/10.30519/ahtr.801690
Kotler, P., & Keller, K. L. (2009). Dirección de marketing (12.ª ed.). Pearson Educación
Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2021). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155. DOI: https://doi.org/10.1177/0008125619859317
Lectera. (2024). Marketing situacional: Definición, significado y ejemplos. Recuperado de https://lectera.com/info/es/articles/marketing-situacional
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2022). RoBERTa: A robustly optimized BERT pretraining approach [Preprint]. arXiv. Recuperado de https://arxiv.org/abs/1907.11692
Ma, L., y Sun, B. (2020). Machine learning and AI in marketing – connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481–504. DOI: https://doi.org/10.1016/j.ijresmar.2020.04.005
Merino-Trujillo, A. (2011). Como escribir documentos científicos (Parte 3). Artículo de revisión. Salud en Tabasco, 17(1-2), 36–40. Recuperado de http://www.redalyc.org/pdf/487/48721182006.pdf
Redacción Mercado Negro. (2023). Coca-Cola vs. Pepsi: La batalla de los anuncios de Halloween. Mercado Negro. Recuperado de https://www.mercadonegro.pe/publicidad/coca-cola-vs-pepsi-la-batalla-de-los-anuncios-de-halloween
Rust, R. T., & Huang, M. H. (2021). The feeling economy: How artificial intelligence is creating the era of empathy. Palgrave Macmillan. DOI: https://doi.org/10.1007/978-3-030-52977-2
Shaliakhina, T., & Butyrina, M. (2022). Situational marketing as a communication technology. Obraz, 3(41), 9–17
Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2022). Learning from the dark web: Leveraging conversational agents in the era of hyper-personalization to enhance marketing performance. Journal of the Academy of Marketing Science, 48(1), 43–63. DOI: https://doi.org/10.1007/s11747-019-00704-3
Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review, 15(4), 404–428. DOI: https://doi.org/10.1177/1534484316671606
Vargo, S. L., & Lusch, R. F. (2016). Institutions and axioms: An extension and update of service-dominant logic. Journal of the Academy of Marketing Science, 44(1), 5–23. DOI: https://doi.org/10.1007/s11747-015-0456-3
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. En Advances in Neural Information Processing Systems. Curran Associates. Recuperado de https://arxiv.org/abs/1706.03762
Verma, S., Sharma, R., Deb, S., y Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1. DOI: https://doi.org/10.1016/j.jjimei.2020.100002
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. En Advances in Neural Information Processing Systems. Curran Associates. Recuperado de https://arxiv.org/abs/2201.11903
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., Kenton, Z., Brown, S., Hawkins, W., Stepleton, T., Biles, C., Birhane, A., Haas, J., Rimell, L., Hendricks, L. A., & Gabriel, I. (2021). Ethical and social risks of harm from language models. arXiv. Recuperado de https://arxiv.org/abs/2112.04359
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P. S., Mellor, J., & Gabriel, I. (2022, June). Taxonomy of risks posed by language models. In Proceedings of the 2022 ACM conference on fairness, accountability, and transparency, 214-229. DOI: https://doi.org/10.1145/3531146.3533088
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2022). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. DOI: https://doi.org/10.1108/JOSM-04-2018-0119
The Feeling Economy: How Artificial Intelligence Is Creating the Era of Empathy, por R. T. Rust y M. H. Huang, 2021, Palgrave Macmillan. DOI: https://doi.org/10.1007/978-3-030-52977-2
Publicado
2026-06-22
Cómo citar
Zambrano Valdivieso, O., & Ramírez Castellanos, D. (2026). Integración de LLMs en procesos de Diagnóstico situacional de Marketing Digital. Revista Sinergia, 1(19), 52-78. Recuperado a partir de http://sinergia.colmayor.edu.co/ojs/index.php/Revistasinergia/article/view/261
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