Omni-channel retail marketing effect evaluation framework integrating big data and artificial intelligence - Si, Zhuanghao; Ali, Dhakir Abbas; Rosli, Rozaini Binti; Bhaumik, Amiya; Ghosh, Abhijit (Edelweiss Applied Science and Technology, 2025)
This study proposes an innovative AI-driven framework for evaluating and optimizing omnichannel retail marketing effectiveness to address challenges in integrating multiple retail channels and leveraging data for strategic decision-making. The research develops a comprehensive framework integrating big data analytics and advanced AI techniques, including reinforcement learning and graph neural networks. The framework combines diverse data sources, employs sophisticated algorithms for analysis, and utilizes adaptive optimization methods across channels. Validation uses controlled experiments and a case study with GlobalMart retail corporation. Experimental results demonstrate significant improvements in key performance indicators, including a 23.7% increase in sales revenue and a 27.6% boost in marketing ROI compared to traditional methods. The GlobalMart case study showed substantial enhancements in customer segmentation accuracy (37%), campaign conversion rates (28%), and online-to-offline integration (42%). The proposed framework offers retailers a powerful tool for marketing optimization in complex omnichannel environments, though future research should explore its adaptability to emerging technologies and address privacy concerns. Retailers can leverage this framework to enhance data-driven marketing strategies, improve resource allocation, and deliver seamless customer experiences across all touchpoints.