AI-Augmented Social Media Marketing: Data-Driven Approaches for Optimizing Engagement
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I2P103Keywords:
Artificial Intelligence, Social Media Marketing, Machine Learning, Natural Language Processing, Customer Engagement, Personalization, Marketing Automation, Predictive AnalyticsAbstract
In today’s world, where social media has become a worldwide phenomenon, marketers seek assistance from Artificial Intelligence to make optimum strategies. AASMM is a great paradigm for integrating marketing into AI that helps marketers analyze the users and their actions and forecast the next trends for successful interaction. This paper looks at how artificial intelligence posters like machine learning, deep learning, and natural language processing can be applied in social media marketing. Responsively based on user statistics, such AI tools can facilitate the culling of content and determine its relevance to users, effectively tailor experiences and perform several marketing tasks, increasing customer experience. The paper also formulates the current knowledge on artificial intelligence technologies employed in social media marketing, explains the approaches to making proper business decisions based on data accumulated, and identifies the fundamental measurements to evaluate marketing success. It also explores AI's disadvantages and drawbacks, particularly in marketing, including ethics and privacy. Using case studies and statistical data proved that integrating AI into the social media marketing scheme can help enhance the campaigns' efficiency. Hence, it is even more important for businesses to embrace these advanced technologies as they will help the organization adapt to the ever-evolving environment to ensure very competitiveness in the market
References
[1] Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002.
[2] Chaudhary, D., Subashini, R., Velmurugan, G., Surlekar, R. G., & Dua, P. (2023, November). Role of Modern AI Technologies to Promote Social Media Marketing. In 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI) (pp. 244-249). IEEE.
[3] Kumar, P. (2025). Artificial intelligence (AI)-augmented knowledge management capability and clinical performance: implications for marketing strategies in the health-care sector. Journal of Knowledge Management, 29(2), 415-441.
[4] Alhammadi, S., & Wahab, N. N. A. (2024). Enhancing Customer Experience with AI: Insights from Social Media Marketing Strategies for Dubai Millennials. International Journal of Business and Technology Management, 6(S4), 94-117.
[5] Alnaser, A. (2024). The Impact of AI-Augmented Image and Video Editing on Social Media Engagement, Perception, and Digital Culture. Journal of Computational Intelligence, Machine Reasoning, and Decision-Making, 9(10), 15-32.
[6] Onesi-Ozigagun, O., Ololade, Y. J., Eyo-Udo, N. L., & Oluwaseun, D. (2024). Data-driven decision making: Shaping the future of business efficiency and customer engagement. International Journal of Multidisciplinary Research Updates, 7(2), 19-29.
[7] Anderson, J., Bushey, H., Devlin, M., & Gould, A. J. (2021). Promoting student engagement with data-driven practices. In International Perspectives on Supporting and Engaging Online Learners (pp. 87-103). Emerald Publishing Limited.
[8] Navarro, L. M. (2016). Optimizing audience segmentation methods in content marketing to improve personalization and relevance through data-driven strategies. International Journal of Applied Machine Learning and Computational Intelligence, 6(12), 1-23.
[9] Henry, V. (2019). Role of artificial intelligence in social media marketing. International Journal of Recent Technology and Engineering, 8(4), 66-69.
[10] Gupta, M., Kumar, R., Sharma, A., & Pai, A. S. (2023, July). Impact of AI on social marketing and its usage in social media: A review analysis. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-4). IEEE.
[11] Singh, P., Verma, A., Vij, S., & Thakur, J. (2023). Implications & impact of artificial intelligence in digital media: With special focus on social media marketing. In E3S Web of Conferences (Vol. 399, p. 07006). EDP Sciences.
[12] Liu, R., Gupta, S., & Patel, P. (2023). Applying the principles of responsible AI on social media marketing for digital health. Information Systems Frontiers, 25(6), 2275-2299.
[13] Zhang, H., & Song, M. (2022). Big data analytics, AI, and social media marketing research boost market orientation. Research-Technology Management, 65(2), 64-70.
[14] Al-Ghamdi, L. M. (2021). Towards adopting AI techniques for monitoring social media activities. Sustainable Engineering and Innovation, 3(1), 15-22.
[15] Hayat, M. K., Daud, A., Alshdadi, A. A., Banjar, A., Abbasi, R. A., Bao, Y., & Dawood, H. (2019). Towards deep learning prospects: insights for social media analytics. IEEE Access, 7, 36958-36979.
[16] Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., & Lee, K. C. (2020). Enhancing social media analysis with visual data analytics: A deep learning approach (pp. 1459-1492). Amsterdam, The Netherlands: SSRN.
[17] Melville, P., Sindhwani, V., Lawrence, R. D., Meliksetian, E., Liu, Y., Hsueh, P. Y., & Perlich, C. (2009, November). Machine learning for social media analytics. In Machine learning symposium, New York Academy of Sciences, New York.
[18] Kongar, E., & Adebayo, O. (2021). Impact of social media marketing on business performance: A hybrid performance measurement approach using data analytics and machine learning. IEEE Engineering Management Review, 49(1), 133-147.
[19] Milan, A., Sahu, R., & Sandhu, J. K. (2023, August). Impact of AI on social marketing and its usage in social media: A review analysis. In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT) (pp. 1749-1754). IEEE.
[20] Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266.
[21] Sattikar, A. A., & Kulkarni, R. V. (2012). Natural language processing for content analysis in social networking. International Journal of Engineering Inventions, 1(4), 6-9.
