Analítica de marketing e inteligencia artificial en la transformación de la competitividad empresarial
DOI:
https://doi.org/10.20397/2177-6652/2025.v25i3.3101Palabras clave:
Inteligência Artificial; Marketing analytics; Competitividade Empresarial; Gestão Estratégica; Governança de Dados.Resumen
Objetivo: El estudio analiza cómo la analítica de marketing y la Inteligencia Artificial (IA) impactan en la competitividad empresarial, destacando beneficios, desafíos e implicaciones para la gestión estratégica. Este tema es esencial en un escenario empresarial cada vez más dinámico y basado en datos.
Metodología: La investigación utilizó un enfoque cualitativo basado en la Revisión Sistemática de la Literatura (SLR), analizando artículos de las bases de datos Web of Science y Scopus. La selección siguió estrictos criterios de impacto académico, priorizando publicaciones recientes con un alto índice H y clasificación Q1 en el SCImago Journal Rank.
Originalidad/Relevancia: este estudio llena los vacíos en la literatura al investigar cómo la integración de los análisis de marketing y la IA transforma las decisiones estratégicas. Los hallazgos demuestran el potencial de estas tecnologías para promover la personalización a gran escala, la eficiencia operativa y las prácticas de gobernanza responsable, esenciales para la competitividad sostenible.
Resultados principales: Los resultados indican que la IA mejora el análisis de marketing, permitiéndole transformar grandes volúmenes de datos en información procesable, decisiones rápidas y optimización de recursos. Sin embargo, desafíos éticos como la protección de datos y los sesgos algorítmicos emergen como barreras importantes para una implementación responsable.
Contribuciones teóricas/metodológicas: este artículo integra perspectivas teóricas y metodológicas sobre IA y análisis de marketing, ofreciendo ideas para alinear la innovación tecnológica con principios éticos y estrategias corporativas.
Aportes a la Gestión: Se destaca la importancia de prácticas éticas y transparentes para fortalecer la confianza del consumidor, fomentar relaciones sustentables y asegurar ventajas competitivas de largo plazo.
Citas
Akter, S., et al. (2023). Advancing algorithmic bias management capabilities in AI-driven marketing analytics research. Industrial Marketing Management, 114, 243-261. https://doi.org/10.1016/j.indmarman.2023.08.013
Andrews, M., et al. (2016). Mobile ad effectiveness: Hyper-contextual targeting with crowdedness. Marketing Science, 35(2), 218-233. https://doi.org/10.1287/mksc.2015.0905
Brock, J. K. U., & Von Wangenheim, F. (2019). Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelligence. California Management Review, 61(4), 110-134. https://doi.org/10.1177/1536504219865226
Bughin, J., et al. (2018). Notes from the AI frontier: Modeling the global economic impact of AI. McKinsey Global Institute, 1-64. Available at: https://modeling-the-impact-of-ai-on-the-world-economy. Accessed on: 10 Oct. 2024.
Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can’t do (yet) for your business. McKinsey Quarterly, 1, 97-108.
Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. A. Buchanan & A. Bryman (Eds.), The SAGE handbook of organizational research methods (pp. 671–689). Sage.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
Davenport, T., et al. (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
Dwivedi, Y. K., et al. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Falgas, M. E., et al. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. The FASEB Journal, 22(2), 338-342. https://doi.org/10.1096/fj.07-9492LSF
Garvey, M. D., Samuel, J., & Pelaez, A. (2021). Would you please like my tweet?! An artificially intelligent, generative probabilistic, and econometric-based system design for popularity-driven tweet content generation. Decision Support Systems, 144, 113497. https://doi.org/10.1016/j.dss.2021.113497
Germann, F., Lilien, G. L., & Rangaswamy, A. (2013). Performance implications of deploying marketing analytics. International Journal of Research in Marketing, 30(2), 114-128. https://doi.org/10.1016/j.ijresmar.2012.10.001
González-Pereira, B., Guerrero-Bote, V. P., & Moya-Anegón, F. (2010). A new approach to the metric of journals’ scientific prestige: The SJR indicator. Journal of Informetrics, 4(3), 379-391. https://doi.org/10.1016/j.joi.2010.03.002
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064. https://doi.org/10.1016/j.im.2016.07.004
Gupta, S., et al. (2021). Big data and firm marketing performance: Findings from knowledge-based view. Technological Forecasting and Social Change, 171, 120986. https://doi.org/10.1016/j.techfore.2021.120986
Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569-16572. https://doi.org/10.1073/pnas.0507655102
Hossain, M. A., et al. (2022). Marketing analytics capability, artificial intelligence adoption, and firms' competitive advantage: Evidence from the manufacturing industry. Industrial Marketing Management, 106, 240-255. https://doi.org/10.1016/j.indmarman.2022.08.017
Jannach, D. (2010). Recommender Systems: An Introduction. Cambridge University Press.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007
Jürgensmeier, L., & Skiera, B. (2024). Generative AI for scalable feedback to multimodal exercises. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2024.05.005
Kerr, G., et al. (2023). Understanding the link between an IMC technology capability and organisational integration and performance. European Journal of Marketing, 57(8), 2048-2075. https://doi.org/10.1108/EJM-05-2022-0373
Kitchens, B., et al. (2018). Advanced customer analytics: Strategic value through integration of relationship-oriented big data. Journal of Management Information Systems, 35(2), 540-574. https://doi.org/10.1080/07421222.2018.1451957
Li, Y., & Ngom, A. (2015). Data integration in machine learning. In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1665-1671). IEEE. https://doi.org/10.1109/BIBM.2015.7359925
Lyceett, M. (2013). ‘Datafication’: Making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), 381-386. https://doi.org/10.1057/ejis.2013.10
Manzoor, A., et al. (2024). A review of machine learning methods for customer churn prediction and recommendations for business practitioners. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3402092
Mikalef, P., et al. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276. https://doi.org/10.1016/j.jbusres.2019.01.044
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.jbusres.2019.01.044
Miklošík, A., et al. (2019). Towards the adoption of machine learning-based analytical tools in digital marketing. IEEE Access, 7, 85705-85718. https://doi.org/10.1109/ACCESS.2019.2924425
Nayyar, V. (2023). The role of marketing analytics in the ethical consumption of online consumers. Total Quality Management & Business Excellence, 34(7-8), 1015-1031. https://doi.org/10.1080/14783363.2022.2139676
Nowell, L. S., et al. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1609406917733847. https://doi.org/10.1177/1609406917733847
Pagani, R. N., Kovaleski, J. L., & Resende, L. M. (2015). Methodi Ordinatio: A proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citations, and year of publication. Scientometrics, 105(3), 2109–2135. https://doi.org/10.1007/s11192-015-1744-x
Pagani, R. N., Kovaleski, J. L., & de Resende, L. M. M. (2017). Avanços na composição da Methodi Ordinatio para revisão sistemática de literatura. Ciência da Informação, 46(2). https://doi.org/10.18225/ci.inf.v46i2.1886
Pagani, R. N., Kovaleski, J. L., de Resende, L. M. M., & others. (2023). Methodi Ordinatio 2.0: Revisited under statistical estimation, and presenting Finder and RankIn. Quality & Quantity, 57(5), 4563–4602. https://doi.org/10.1007/s11135-022-01562-y
Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
Rahman, M. S., Hossain, M. A., & Abdel Fattah, F. A. M. (2022). Does marketing analytics capability boost firms' competitive marketing performance in data-rich business environments?. Journal of Enterprise Information Management, 35(2), 455-480. 10.1007/s11356-021-14303-9. https://doi.org/10.1007/s11356-021-14303-9
Rust, R. T., & Huang, M. H. (2014). The service revolution and the transformation of marketing science. Marketing Science, 33(2), 206-221. https://doi.org/10.1287/mksc.2013.0836
Siddaway, A. P., Wood, A. M., & Hedges, L. V. (2019). How to do a systematic review: A best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta syntheses. Annual Review of Psychology, 70(1), 747-770. https://doi.org/10.1146/annurev-psych-010418-102803
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207-222. https://doi.org/10.1111/1467-8551.00375
Vermeer, S. A., et al. (2019). Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media. International Journal of Research in Marketing, 36(3), 492-508. https://doi.org/10.1016/j.ijresmar.2019.01.010
Wamba, S. F., et al. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://doi.org/10.1016/j.ijpe.2014.12.031
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
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2025 Revista Gestão & Tecnologia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Os direitos, inclusive os de tradução, são reservados. É permitido citar parte de artigos sem autorização prévia desde que seja identificada a fonte. A reprodução total de artigos é proibida. Em caso de dúvidas, consulte o Editor.
