AI-Augmented Marketing Automation: Transforming Decision-Making in Omnichannel Retailing
DOI:
https://doi.org/10.63856/ijis/v2i5/00037Keywords:
AI Marketing, Marketing Automation, Omnichannel Retail, AI-Driven Decision Making, Personalization, Machine Learning, Real-Time Marketing.Abstract
Omnichannel retail settings are being transformed by the integration of Artificial Intelligence (AI) with marketing automation and decision-making processes. With consumers engaging with brands across various platforms, businesses face the challenge of delivering a consistent personalized experience. This study introduces the concept of AI-Augmented Marketing Automation (AIMA), which aims to enhance marketing decision-making through machine learning, predictive analytics, and real-time decision-making. The present paper will discuss the main elements of AIMA, introduce a conceptual model and show how this model is applied in practice with the help of a case study. The knowledge gained in this study can be used to demonstrate that AIMA can increase campaign personalization and optimization, enhance customer interaction, and ultimately increase return on investment (ROI) and customer satisfaction. The paper ends with identification of the challenges and recommendations on how to implement them in future.
References
1. Ali, A., & Ali, A. (2024a). Revolutionizing Digital Marketing: AI-driven Content Creation and Optimization Strategies. In Zenodo (CERN European Organization for Nuclear Research). European Organization for Nuclear Research. https://doi.org/10.5281/zenodo.18514281
2. Ali, A., & Ali, A. (2024b). Revolutionizing Digital Marketing: AI-driven Content Creation and Optimization Strategies. In Zenodo (CERN European Organization for Nuclear Research). European Organization for Nuclear Research. https://doi.org/10.5281/zenodo.18514280
3. Bellala, K. R. (2025). Leveraging Artificial Intelligence and Automation in Salesforce Marketing Cloud. International Journal of Innovative Science and Research Technology (IJISRT), 1113. https://doi.org/10.38124/ijisrt/25dec861
4. D. E. (2022). AI-Powered Marketing Automation: Revolutionizing Campaign Management. International Journal For Multidisciplinary Research, 4(2). https://doi.org/10.36948/ijfmr.2022.v04i02.23358
5. Ganesan, S. (2025). Real-time personalization in retail: A blueprint for AI-driven digital transformation. International Journal of Science and Research Archive, 16(2), 265. https://doi.org/10.30574/ijsra.2025.16.2.2109
6. Madanchian, M., Rafiee, A., & Taherdoost, H. (2025). AI-Driven Content Creation and Curation (p. 90). https://doi.org/10.1201/9781003625452-6
7. Mishchenko, E. (2025). ENHANCING PRE-PURCHASE ENGAGEMENT. THE POWER OF LLMS IN PERSONALIZED OMNICHANNEL RETAIL. Экономика и Предпринимательство. https://doi.org/10.34925/eip.2024.174.1.142
8. Mou, A. J. (2024). MARKETING CAPSTONE INSIGHTS: LEVERAGING MULTI-CHANNEL STRATEGIES FOR MAXIMUM DIGITAL CONVERSION AND ROI. 3(4), 1. https://doi.org/10.63125/5w76qb87
9. Mou, A. J. (2024). MARKETING CAPSTONE INSIGHTS: LEVERAGING MULTI-CHANNEL STRATEGIES FOR MAXIMUM DIGITAL CONVERSION AND ROI. 3(4), 1. https://doi.org/10.63125/5w76qb87
10. Muminov, H. (2024). THE FUTURE OF AI IN DIGITAL MARKETING TRENDS AND PREDICTIONS FOR 2025. International Journal of Artificial Intelligence for Digital Marketing, 1(4), 1. https://doi.org/10.61796/ijaifd.v1i4.185
11. Onyinye, C., Researcher, I., Omopariola, B., Aboaba, V., Chen, X., Shen, C., Zheng, W., Porter, M., Davenport, T., Daugherty, P., Wilson, H., Omopariola, B., Liang, M., Yu, J., Jin, Wang, B., Oluwabukola, E.-J., Oluwafunmibi, F., Ayodele, A., … Lessmann, S. (2025). Integrating AI with Omnichannel Marketing: Targeting High-Value Customers through Cross-Platform Data Fusion and Intent Recognition. International Journal of Computer Applications Technology and Research. https://doi.org/10.7753/ijcatr1405.1002
12. Pandugula, C. (2025). Omni-channel Retail: Leveraging Machine Learning for Personalized CustomerExperiences and Transaction Optimization. Journal of Clinical & Biomedical Research, 7(2), 2. https://doi.org/10.47363/jcbr/icbr2025/2025(7)2
13. Pendyala, M. K., & Lakkamraju, V. V. (2024). Impact of Artificial Intelligence in Customer Journey. International Journal of Innovative Science and Research Technology (IJISRT), 1528. https://doi.org/10.38124/ijisrt/ijisrt24aug807
14. Vasundhara, S., Venkatesh, K., Manimegalai, V., Sundharesalingam, P., Sathyakala, S., & Boopathi, S. (2024). AI-Powered Marketing Revolutionizing Customer Engagement Through Innovative Strategies. In Advances in business information systems and analytics book series (p. 21). IGI Global. https://doi.org/10.4018/979-8-3693-2643-5.ch002
15. Yella, S. (2025a). Algorithmic Campaign Orchestration: A Framework for Automated Multi-Channel Marketing Decisions. Journal of Computer Science and Technology Studies, 7(2), 165. https://doi.org/10.32996/jcsts.2025.7.2.15
16. Yella, S. (2025b). Algorithmic Campaign Orchestration: A Framework for Automated Multi-Channel Marketing Decisions. Journal of Computer Science and Technology Studies, 7(2), 323.
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