Edge AI Frameworks for Real-Time Personalised Marketing Communication in 6G Networks

Abstract

The recent development of Artificial Intelligence (AI) has brought about dramatic changes to digital marketing, allowing personalised and real-time communication with clients. Nevertheless, with the further sophistication of AI systems, their efficiency depends on the possibility of providing personalised marketing messages in real time. The 6G networks and the Edge AI integration are an innovative solution to this issue that provides the possibility of ultra-low latency and high bandwidth to provide real-time and personalised customer experiences. The paper suggests a new Edge AI architecture that can support customised marketing communication in 6G contexts. With machine learning models, e.g., Gradient Boosting Machine (GBM), Random Forest (RF), and Support Vector Machine (SVM), the framework predicts customer engagement and loyalty, providing customised content on the basis of real-time customer data. Key metrics used to assess the performance of the proposed models were accuracy, AUC-ROC, precision, recall, and F1-score. Results show that GBM outperforms both RF and SVM in all evaluated metrics. Moreover, the low-latency features of 6G can also improve the responsiveness in real-time, which will raise brand loyalty, customer engagement, and satisfaction. The Structural Equation Modelling (SEM) demonstrated that AI communication systems have a significant influence on engagement, which, in its turn, leads to brand loyalty. This study explains why the integration of Edge AI and 6G networks can be used to streamline personalised marketing tactics and provide businesses with an edge in terms of providing customers with timely and relevant information.