AI-Driven Marketing Communications and Herbal Drugs Brand Awareness Among Retirees in Southwest Nigeria

This study investigated the effects of AI-driven marketing communication messages on herbal drugs brand awareness among retirees in Southwest Nigeria. Specifically, AI-personalized advertising, AI-optimized promotional messaging, and AI-assisted interactive engagement were tested as the explanatory variables of brand awareness among retirees in Southwest, Nigeria. Using a survey research design and Multiple Regression analysis, the three proposed hypotheses were tested.

The findings revealed that AI-driven communication variables exert positive and statistically significant influence on herbal drugs brand awareness among retirees. AI-assisted interactive engagement showed the strongest predictive strength. The study concludes that AI-Driven marketing communication significantly affects brand recall and recognition among retirees. It recommends among others that herbal drug firms adopt AI-based personalization tools to improve message clarity, credibility, and audience targeting.

Marketing Communications, Brand Awareness, Personalized Adverting, Optimized Promotion, Interactive Engagement

1. Arora, N., Dreze, X., Ghose, A., Hess, J. D., Iyengar, R., Jing, B., … Shankar, V. (2021). Putting one-to-one marketing to work. Journal of Marketing, 85(1), 109–127. [Google Scholar] [Crossref]

2. Bleier, A., & Eisenbeiss, M. (2015). The importance of trust in personalized advertising. Journal of Advertising Research, 55(1), 24–38. [Google Scholar] [Crossref]

3. Bleier, A., Harmeling, C. M., & Palmatier, R. W. (2020). Creating effective online customer experiences. Journal of Marketing, 84(2), 98–119. [Google Scholar] [Crossref]

4. Davis, F. D. (1989). Perceived usefulness and user acceptance of IT. MIS Quarterly, 13(3), 319–340. [Google Scholar] [Crossref]

5. Heath, R. (2016). The hidden power of advertising. What really affects the choice of the brand? GWP [Google Scholar] [Crossref]

6. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. [Google Scholar] [Crossref]

7. Jachnis, A. (2022). Consumer psychology. Psychological and sociological determinants of consumer behaviour. Oficyna Wydawnicza Branta. [Google Scholar] [Crossref]

8. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37. [Google Scholar] [Crossref]

9. Kotler, P. (2014). Marketing management: Analysis, planning, implementation and control (9th ed.). Prentice-Hall. [Google Scholar] [Crossref]

10. Kumar, V., Ramachandran, D., & Kumar, B. (2023). Influence of AI-driven personalization on consumer engagement. Journal of Business Research, 156, 113–124. [Google Scholar] [Crossref]

11. Li, J., Monroe, W., & Jurafsky, D. (2020). Deep reinforcement learning for neural dialogue generation. Transactions of the Association for Computational Linguistics, 8, 663-676. [Google Scholar] [Crossref]

12. Mariani, M., Borghi, M., & Gretzel, U. (2023). AI chatbots in healthcare marketing. Technological Forecasting and Social Change, 188, 122–134. [Google Scholar] [Crossref]

13. Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in personalization. Journal of Business Ethics, 152(1), 1–14. [Google Scholar] [Crossref]

14. Mehrabi, N., Morstatter, F., Saxena, N. (2021). A survey on bias and fairness in machine learning systems. ACM Computing Surveys, 54(6), 1-35. [Google Scholar] [Crossref]

15. Nguyen, T., Li, C., & Zhao, K. (2021). Predictive analytics for adaptive digital engagement. Journal of Marketing Analytics, 9(1), 45-60. [Google Scholar] [Crossref]

16. Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion. Springer. [Google Scholar] [Crossref]

17. Radford, A., Wu, J., & Child, R.. (2019). Language models are unsupervised multitask learners. OpenAI Technical Report. [Google Scholar] [Crossref]

18. Reiter, E., & Dale, R. (2000). Building natural language generation systems. Cambridge University Press. [Google Scholar] [Crossref]

19. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. [Google Scholar] [Crossref]

20. Zhao, K., Kumar, A., & Yang, Q. (2019). Deep learning for user modeling and recommendation. Information Retrieval Journal, 22(1-2), 1-28. [Google Scholar] [Crossref]

• Drone-Based Phenotyping and its Utilization in Crop Improvement: A Review
• An IoT-Enabled Smart Healthcare Monitoring System Using Machine Learning for Early Health Risk Prediction
• Strategic Integration of Artificial Intelligence for Achieving Operational Excellence in Container Freight Stations Using Evidence from Chennai, India
• Adoption of OTT platforms: Analyzing User Behavior through the UTAUT2 Model
• Customers’ Buying Behaviour in Relation to Online Food Delivery (Evidence from National Capital Region (NCR) of India)