"The Role of Artificial Intelligence in Transforming Digital Marketing Strategies"
Authors
Dr. H H Ramesha
Associate Professor & Research Supervisor, Department of Management Studies, Visvesvaraya Technological University-Belagavi, Centre for Post-Graduation Studies, Muddenahalli, Chikkaballapur, India. (IN)
Tanuja Nair
Research Scholar, Department of Management Studies (MBA), Centre for Post Graduate Studies, Muddenahalli, Chikkaballapur, Visvesvaraya Technological University, Belagavi, Karnataka State, India. (IN)
Mr. Amar Patil
Student, Department of Management Studies (MBA), Centre for Post Graduate Studies, Muddenahalli, Chikkaballapur, Visvesvaraya Technological University, Belagavi, Karnataka State, India. (IN)
Article Information
DOI: 10.51583/IJLTEMAS.2025.1408000066
Subject Category: Digital Marketing
Volume/Issue: 14/8 | Page No: 542-549
Publication Timeline
Submitted: 2025-09-08
Published: 2025-09-08
Abstract
Abstract: Artificial Intelligence (AI) is currently transforming contemporary business at a very fast pace, and one of the most drastically affected domains is digital marketing. This research paper, "The Role of Artificial Intelligence in Transforming Digital Marketing Strategies," considers the manner in which AI technologies like machine learning, natural language processing, computer vision, predictive analytics, and chatbots are transforming marketing practices. Based on a mixed-method methodology, the research integrates primary data through survey responses of 140 participants with secondary sources such as academic journals, industry reports, and case studies. The results show that AI technologies significantly augment customer personalization, customer engagement, and marketing effectiveness. Predictive analytics allows for more precise forecasts of consumer behavior, and AI-driven chatbots enhance responsiveness and cost savings in customer services. But the study also outlines issues of data privacy, ethics, transparency in algorithms, and consumer trust. These factors point to the importance of accountable AI deployment. The research concludes that AI is not only an enabling tool but also a primary strategic tool in digital marketing. Businesses embracing AI-led strategies can reap long-term competitive benefits through better targeting, automation, and customer satisfaction. In the future, the paper recommends further research into topics like hybrid intelligence, real-time personalization, and the fusion of AI with upcoming technologies like augmented reality (AR), virtual reality (VR), and the Internet of Things (IoT).
Keywords
Artificial Intelligence, Digital Marketing Strategies, Customer Insights, Predictive Analytics, Chatbots
Downloads
References
1. Bindu Maheshwari. (2023). Impact of Artificial Intelligence on Digital Marketing. The Academic, 1(5), 95–111. [Google Scholar] [Crossref]
2. Rao, S., Srivatsala, V., & Suneetha, V. (2016). Optimizing technical ecosystem of digital marketing. In S. Dash, M. Bhaskar, B. Panigrahi, & S. Das (Eds.), Artificial Intelligence and Evolutionary Computations in Engineering Systems (pp. 245–258). Springer. [Google Scholar] [Crossref]
3. Casillas, J., & Martínez-López, F. J. (2010). Marketing Intelligence Systems. Springer-Verlag Berlin Heidelberg. [Google Scholar] [Crossref]
4. Wierenga, B. (2010). Marketing and Artificial Intelligence: Great opportunities, reluctant partners. In Casillas, J., & Martínez-López, F. J. (Eds.), Marketing Intelligent Systems Using Soft Computing. Springer. [Google Scholar] [Crossref]
5. HubSpot Research. (2022). The State of AI in Marketing. Retrieved from https://blog.hubspot.com [Google Scholar] [Crossref]
6. University of Birmingham. (2017). Introduction to Artificial Intelligence. Retrieved from www.cs.bham.ac.uk [Google Scholar] [Crossref]
7. Yuniarthe, Y. (2017). Application of Artificial Intelligence in Search Engine Optimization (SEO). International Conference on Soft Computing, Intelligent Systems and Information Technology, 96–101. [Google Scholar] [Crossref]
8. Krasotkina, O., & Mottl, V. (2015). A Bayesian approach to sparse learning-to-rank for search engine optimization. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition. Springer. [Google Scholar] [Crossref]
9. Zhu, C., & Wu, G. (2011). Research and analysis of search engine optimization factors based on reverse engineering. International Conference on Multimedia Information Networking and Security, 225–228. [Google Scholar] [Crossref]
10. Li, C., Lu, Y., Mei, Q., Tan, X., & Pandey, S. (2015). Click-through prediction for advertising in Twitter timeline. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1959–1968. [Google Scholar] [Crossref]
11. Eshak, M. I., Ahmad, R. B., & Sarlan, A. B. (2017). Hybrid sentiment model for purchase intention analysis in social commerce. IEEE Conference on Big Data and Analytics, 61–66. [Google Scholar] [Crossref]
12. Isah, H., Neagu, D., & Trundle, P. R. (2014). Social media analysis for product safety using text mining and sentiment analysis. 14th UK Workshop on Computational Intelligence, 1–7. [Google Scholar] [Crossref]
13. Liu, X., Nie, D., Bai, S., Hao, B., & Zhu, T. (2015). Personality prediction for microblog users with active learning methods. In Zu, Q., Hu, B., Gu, N., & Seng, S. (Eds.), Human Cantered Computing. Springer. [Google Scholar] [Crossref]
14. Reis, D. D., Goldstein, F., & Quintão, F. (2012). Extracting unambiguous keywords from micro posts using web and query logs data. Proceedings of MSM Conference. [Google Scholar] [Crossref]
15. Spentzouris, P., Koutsopoulos, I., Madsen, K. G., & Hansen, T. V. (2018). Advertiser bidding prediction and optimization in online advertising. In Iliadis, L., Maglogiannis, I., & Plagianakos, V. (Eds.), Artificial Intelligence Applications and Innovations. Springer. [Google Scholar] [Crossref]
16. Mahdian, M., & Tomak, K. (2007). Pay-per-action model for online advertising. In Deng, X., & Graham, F. C. (Eds.), Internet and Network Economics. Springer. [Google Scholar] [Crossref]
17. Zhang, W., Yuan, S., & Wang, J. (2014). Optimal real-time bidding for display advertising. Proceedings of the 20th ACM SIGKDD International Conference, 1077–1086. [Google Scholar] [Crossref]
18. Cameron, G., Cameron, D., Megaw, G., Bond, R., Mulvenna, M. D., O’Neill, S., Armour, C., & McTear, M. (2017). Towards a chatbot for digital counselling. BCS Human-Computer Interaction Conference. [Google Scholar] [Crossref]
19. Stanica, I., Dascalu, M., Bodea, C. N., & Moldoveanu, A. D. (2018). VR Job Interview Simulator: Where virtual reality meets AI for education. Zooming Innovation in Consumer Technologies Conference (ZINC), 9–12. [Google Scholar] [Crossref]
20. D’Alfonso, S., Santesteban-Echarri, O., Rice, S. G., Wadley, G., Lederman, R., Miles, C. R., Gleeson, J., & Alvarez-Jimenez, M. (2017). AI-assisted online social therapy for youth mental health. Frontiers in Psychology, 8(796). [Google Scholar] [Crossref]
21. Gervais, A., Shokri, R., Singla, A., Capkun, S., & Lenders, V. (2014). Quantifying web-search privacy. Proceedings of the ACM Conference on Computer and Communications Security. [Google Scholar] [Crossref]
22. Kanagarajan, K., & Arumugam, S. (2018). Intelligent sentence retrieval using semantic algorithms. Cluster Computing, 21(1), 1–11. [Google Scholar] [Crossref]
23. Buck, J. W., Perugini, S., & Nguyen, T. V. (2018). Natural language, mixed-initiative personal assistant agents. International Conference on Ubiquitous Information Management and Communication. [Google Scholar] [Crossref]
24. Chai, J. Y., Horvath, V., Nicolov, N., Stys, M., Kambhatla, N., Zadrozny, W., & Melville, P. (2002). Natural language assistant: A dialog system for product recommendation. AI Magazine, 23(1), 63–76. [Google Scholar] [Crossref]
25. Marketing Profs. (2017). The incredible amount of data generated online every minute. Retrieved from https://www.marketingprofs.com. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Advanced Techniques for Fake News Detection on Twitter Using NLP and AI: A Comprehensive Review
- Review of Self Compacting Geopolymer Concrete Using Slag Sand as Fine Aggregate
- Performance of Local Construction Contractors – Case Study of Registered Contractors in Monrovia, Liberia
- Cross-Cultural Perspectives on Innovation Management in Multinational Organizations
- Modeling of Reaction Between Dissolved Oxygen (DO) And Biological Oxygen Demand (BOD) in Degradation River