IoT Behavioural Analytics for Retail Engagement

Abstract

The modern-day retailing world is struggling to provide real-time and hyper-personalised customer interaction in the context of fragmented behavioural data, sluggish analytics, and in-store interventions that are generic. Current Internet of Things (IoT) retail systems are mainly focused on inventory and transactional insights and do not capture more in-depth behavioural and emotional indicators that affect purchase intent and satisfaction. In this context, this paper will suggest an Internet of Things (IoT)-Based Behavioural Analytics Platform to Hyper-Personalised Consumer Engagement in Retail Management (IBAPS-RM). The framework incorporates multimodal Internet of Things (IoT) sensing, edge computing, and cloud intelligence in creating Multimedia Behavioural Digital Twins (Behavioural Digital Twin (BDT) that dynamically change in response to contextual, environmental, and Interaction-driven information. One of the most notable novelties is the Behavioural Fusion Neural Unit (BFNU) (Behavioural Fusion Neural Unit (BFNU)), that conducts real-time sensor fusion between gaze movement, dwell time, gestures, proximity, and purchase latency to determine behavioural intent and launch micro-personalised interventions in the form of adaptive light, context sensitive offers and personalised digital content. Reinforcement learning also enhances engagement policies through continuous optimisation based on feedback. Experimental analysis shows that IBAPS-RM has better engagement intelligence, with over 93% of personalisation accuracy, 73% shorter decision latency, and 64% higher conversion rate than traditional Internet of Things (IoT) retail systems. The suggested solution improves responsiveness, consumer experience, and operational effectiveness, and promotes privacy-conscious behavioural modelling. In general, IBAPS-RM creates a dynamic, proactive retail intelligence paradigm that dedicates behavioural inference to real-time engagement delivery.