Inteligência artificial aplicada ao marketing: uma revisão de literatura sobre princípios, composto mercadológico e aplicações ao longo do ciclo de vida do produto

Autores/as

  • Sergio Arándiga Autor/a

DOI:

https://doi.org/10.69849/4v2wde29

Palabras clave:

Inteligência artificial, Marketing, Composto mercadológico, Machine learning, Personalização, Ciclo de vida do produto

Resumen

A inteligência artificial vem transformando o marketing de maneiras que, até pouco tempo atrás, seriam difíceis de imaginar. Neste artigo, busca-se revisitar os fundamentos do campo e examinar como as tecnologias de IA se inserem em cada dimensão do composto mercadológico, da criação do produto ao atendimento pós-venda. A pesquisa segue uma abordagem qualitativa, sustentada por revisão bibliográfica de publicações indexadas nas bases Scopus, Web of Science e Google Scholar, com recorte temporal entre 2015 e 2024. Os achados mostram que a IA amplia de forma relevante a capacidade de personalização da experiência do consumidor, a precisão na precificação, a segmentação de mercado, a automação de campanhas e a gestão do relacionamento com o cliente. A conclusão aponta que o uso estratégico da IA no marketing pode gerar vantagem competitiva duradoura, contanto que seja orientado por princípios éticos sólidos e pelo compromisso genuíno com a criação de valor para o consumidor.

Biografía del autor/a

  • Sergio Arándiga

    Professor e Pesquisador em Marketing e Estratégia Empresarial

    E-mail: sergioarandiga@gmail.com

Referencias

ADAM, M.; WESSEL, M.; BENLIAN, A. AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, v. 31, n. 2, p. 427-445, 2021.

AMERICAN MARKETING ASSOCIATION. Definitions of Marketing. Chicago: AMA, 2017. Disponível em: https://www.ama.org/the-definition-of-marketing-what-is-marketing/. Acesso em: 10 jan. 2024.

AUTODESK. Generative Design in Manufacturing. San Francisco: Autodesk, 2022.

BOOMS, B. H.; BITNER, M. J. Marketing strategies and organizational structures for service firms. In: DONNELLY, J. H.; GEORGE, W. R. (Eds.). Marketing of Services. Chicago: American Marketing Association, 1981. p. 47-51.

BROWN, T. B. et al. Language models are few-shot learners. Advances in Neural Information Processing Systems, v. 33, p. 1877-1901, 2020.

BRYNJOLFSSON, E.; HU, Y.; RAHMAN, M. S. Competing in the age of omnichannel retailing. MIT Sloan Management Review, v. 54, n. 4, p. 23-29, 2013.

CHAFFEY, D.; ELLIS-CHADWICK, F. Digital Marketing: Strategy, Implementation and Practice. 7. ed. Harlow: Pearson, 2022.

CHOPRA, S.; MEINDL, P. Supply Chain Management: Strategy, Planning, and Operation. 7. ed. Hoboken: Pearson, 2021.

DAVENPORT, T. H.; HARRIS, J. Competing on Analytics: Updated, with a New Introduction. Boston: Harvard Business Review Press, 2017.

DAVENPORT, T. H.; RONANKI, R. Artificial intelligence for the real world. Harvard Business Review, v. 96, n. 1, p. 108-116, 2018.

EDELMAN. 2023 Edelman Trust Barometer: Special Report on AI and Trust. New York: Edelman, 2023.

EZRACHI, A.; STUCKE, M. E. Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy. Cambridge: Harvard University Press, 2016.

GARTNER. Top Strategic Technology Trends for 2023. Stamford: Gartner, 2023.

GOODMAN, B.; FLAXMAN, S. European Union regulations on algorithmic decision-making and a 'right to explanation'. AI Magazine, v. 38, n. 3, p. 50-57, 2017.

HASTIE, T.; TIBSHIRANI, R.; FRIEDMAN, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2. ed. New York: Springer, 2009.

HUANG, M. H.; RUST, R. T. A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, v. 49, n. 1, p. 30-50, 2021.

IYENGAR, R.; JEDIDI, K.; KOHLI, R. A conjunctive model of tag recommendation. Marketing Science, v. 27, n. 4, 2008.

JARAMILLO, F.; MULKI, J. P.; MARSHALL, G. W. Critical role of the sales manager in salespeople's relational behaviors and key outcomes. Journal of Business Research, v. 58, n. 7, p. 988-998, 2005.

JURAFSKY, D.; MARTIN, J. H. Speech and Language Processing. 3. ed. (draft). Stanford: Stanford University, 2023.

KANNAN, P. K.; LI, H. A. Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, v. 34, n. 1, p. 22-45, 2017.

KOTLER, P.; KARTAJAYA, H.; SETIAWAN, I. Marketing 5.0: Technology for Humanity. Hoboken: Wiley, 2021.

KOTLER, P.; KELLER, K. L. Administração de Marketing. 15. ed. São Paulo: Pearson, 2019.

LAMBRECHT, A.; TUCKER, C. Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, v. 65, n. 7, p. 2966-2981, 2019.

LAUTERBORN, R. New marketing litany: 4 P's passé; C-words take over. Advertising Age, v. 61, n. 41, p. 26, 1990.

LEMON, K. N.; VERHOEF, P. C. Understanding customer experience throughout the customer journey. Journal of Marketing, v. 80, n. 6, p. 69-96, 2016.

LIU, B. Sentiment Analysis and Opinion Mining. San Rafael: Morgan & Claypool, 2012.

McCARTHY, E. J. Basic Marketing: A Managerial Approach. Homewood: Irwin, 1960.

McKINSEY & COMPANY. The Value of Getting Personalization Right or Wrong is Multiplying. New York: McKinsey, 2021.

NAGLE, T. T.; MÜLLER, G. The Strategy and Tactics of Pricing. 6. ed. New York: Routledge, 2018.

OBERMEYER, Z. et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science, v. 366, n. 6464, p. 447-453, 2019.

OPENAI. GPT-4 Technical Report. San Francisco: OpenAI, 2023.

PANG, B.; LEE, L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, v. 2, n. 1-2, p. 1-135, 2008.

PHILLIPS, R. L. Pricing and Revenue Optimization. 2. ed. Stanford: Stanford University Press, 2021.

PINE, B. J.; GILMORE, J. H. The Experience Economy. Boston: Harvard Business School Press, 1999.

PORTER, M. E. Competitive Advantage: Creating and Sustaining Superior Performance. New York: Free Press, 1985.

REICHHELD, F. F. The Loyalty Effect: The Hidden Force Behind Growth, Profits, and Lasting Value. Boston: Harvard Business School Press, 2001.

ROETZER, P.; KAPUT, M. Marketing Artificial Intelligence: AI, Marketing, and the Future of Business. Dallas: BenBella Books, 2022.

RUSSELL, S.; NORVIG, P. Artificial Intelligence: A Modern Approach. 4. ed. Hoboken: Pearson, 2021.

SISMEIRO, C.; BUCKLIN, R. E. Modeling purchase behavior at an e-commerce web site: A task-completion approach. Journal of Marketing Research, v. 41, n. 3, p. 306-323, 2004.

SLATER, S. F.; NARVER, J. C. Does competitive environment moderate the market orientation-performance relationship? Journal of Marketing, v. 58, n. 1, p. 46-55, 1994.

TIROLE, J. The Economics of Artificial Intelligence: An Agenda. Cambridge: NBER, 2022.

TRANFIELD, D.; DENYER, D.; SMART, P. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, v. 14, n. 3, p. 207-222, 2003.

VERHOEF, P. C. et al. Customer experience creation: Determinants, dynamics and management strategies. Journal of Retailing, v. 85, n. 1, p. 31-41, 2010.

VERHOEF, P. C. et al. Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, v. 122, p. 889-901, 2021.

WEDEL, M.; KANNAN, P. K. Marketing analytics for data-rich environments. Journal of Marketing, v. 80, n. 6, p. 97-121, 2016.

ZHANG, S. et al. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, v. 52, n. 1, p. 1-38, 2019.

ZHOU, F. et al. A review of mass customization in research and practice: A systematic literature review. International Journal of Production Research, v. 60, n. 13, p. 4088-4110, 2022.

ZUBOFF, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: PublicAffairs, 2019.

Publicado

2026-04-16

Cómo citar

Arándiga, S. (2026). Inteligência artificial aplicada ao marketing: uma revisão de literatura sobre princípios, composto mercadológico e aplicações ao longo do ciclo de vida do produto. Revista Ft, 30(157), 01-17. https://doi.org/10.69849/4v2wde29