Edge-AI for Zero-Latency Customer Micro-Segmentation

Abstract

For the purpose of driving targeted marketing, improving customer experience, and increasing conversion rates, real-time customer micro-segmentation has become an essential component in this era of hyper-personalization. Traditional cloud-based segmentation approaches, on the other hand, frequently face challenges in the form of delayed responses, issues around data privacy, and limitations on bandwidth. The purpose of this study is to offer an Edge-AI framework for zero-latency consumer micro-segmentation. This framework would enable on-device data processing and rapid behavioural insights. Real-time clustering and dynamic segmentation based on user behaviour, preferences, and contextual data are both accomplished by the system through the use of lightweight machine learning models that are deployed at the network edge. We investigate the possibility of using federated learning in order to protect the privacy of users while also enhancing the performance of models. When compared to cloud-centric techniques, the results of the experiments show that there is a considerable reduction in inference latency and an improvement in responsiveness. This achievement is achieved without compromising accuracy. The implementation of this Edge-AI architecture paves the way for consumer analytics that are scalable, sensitive to privacy concerns, and extremely quick in industries such as retail, banking, and digital services.