Artificial intelligence applied to marketing: a literature review on principles, marketing mix and applications throughout the product life cycle
DOI:
https://doi.org/10.69849/4v2wde29Keywords:
Artificial intelligence, Marketing, Marketing mix, Machine learning, Personalization, Product life cycleAbstract
Artificial intelligence has been reshaping marketing in ways that were hard to foresee just a few years ago. This paper revisits the foundational principles of the field and examines how AI technologies fit into each dimension of the marketing mix, from product development through after-sales service. The research follows a qualitative approach grounded in a bibliographic review of publications indexed in Scopus, Web of Science and Google Scholar, covering the period from 2015 to 2024. The findings show that AI meaningfully expands organizations' capacity for consumer experience personalization, pricing precision, market segmentation, campaign automation and customer relationship management. The conclusion points out that the strategic use of AI in marketing can generate lasting competitive advantage, as long as it is guided by sound ethical principles and a genuine commitment to creating value for the consumer.
References
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.
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