ENHANCING TRADITIONAL MARKETING CHANNELS WITH AI: A HOLISTIC APPROACH
Keywords:
AI-Enhanced Marketing, Data-Driven Audience Targeting, Content Personalization, Marketing Automation EthicsAbstract
This article explores the integration of artificial intelligence (AI) technologies into traditional marketing channels, examining how AI can revitalize and enhance the effectiveness of print media, television, radio, and outdoor advertising in an increasingly digital landscape. The study investigates AI-driven techniques for optimizing ad placement, content creation, and audience targeting within these established mediums. Through an analysis of industry reports, case studies, and academic research, the article demonstrates how machine learning, natural language processing, and computer vision can significantly improve marketing ROI, audience engagement, and operational efficiency. The research also addresses the challenges and ethical considerations associated with AI adoption in marketing, including data privacy concerns, potential job displacement, and maintaining brand authenticity. By presenting a comprehensive overview of AI applications in traditional marketing channels, this article provides insights into how businesses can leverage AI to create more cohesive and effective marketing strategies that bridge the gap between conventional and digital approaches.
References
S. Dixon, "Digital advertising spending worldwide 2021-2026," Statista, Mar. 31, 2023. [Online]. Available: https://www.statista.com/statistics/237974/online-advertising-spending-worldwide/
Grand View Research, "Artificial Intelligence In Marketing Market Size Report, 2030," Grand View Research, Feb. 2022. [Online]. Available: https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-marketing-market-report
M. Chui et al., "Notes from the AI frontier: Applications and value of deep learning," McKinsey Global Institute, Apr. 2018. [Online]. Available: https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning
A. De Bruyn, V. Viswanathan, Y. S. Beh, J. K.-U. Brock, and F. von Wangenheim, "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, vol. 51, pp. 91-105, Aug. 2020. [Online]. Available: https://doi.org/10.1016/j.intmar.2020.04.007
Salesforce, "State of Marketing," Salesforce Research, 9th Edition. [Online]. Available: https://www.salesforce.com/content/dam/web/en_us/www/documents/marketingcloud/S-MC-State-of-Marketing-Report-9th-Edition.pdf
D. Kietzmann, J. Paschen, and J. Kietzmann, "Artificial Intelligence in Advertising: How Marketers Can Leverage Artificial Intelligence Along the Consumer Journey," Journal of Advertising Research, vol. 60, no. 3, pp. 263-267, Sep. 2020. [Online]. Available: https://www.journalofadvertisingresearch.com/content/58/3/263
H. A. Davenport and R. Bean, "How AI Is Changing Sales," Harvard Business Review, Aug. 2019. [Online]. Available: https://hbr.org/2018/07/how-ai-is-changing-sales
Salesforce, "State of the Connected Customer," Salesforce Research, 6th Edition, 2022. [Online]. Available:
S. Ransbotham et al., "Winning With AI," MIT Sloan Management Review and Boston Consulting Group, Oct. 2019. [Online]. Available: https://sloanreview.mit.edu/projects/winning-with-ai/
P. Daney, G. D. Maas, A. Mathai, and K. Panter, "The executive's AI playbook," McKinsey & Company, May 2023. [Online]. Available: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-executives-ai-playbook
IBM, "Cost of a Data Breach Report 2024," IBM Security, 2024. [Online]. Available: https://www.ibm.com/reports/data-breach
S. Ransbotham et al., "Achieving Individual — and Organizational — Value With AI," MIT Sloan Management Review and Boston Consulting Group, Oct.
[Online]. Available: https://sloanreview.mit.edu/projects/achieving-individual-and-organizational-value-with-ai/