Cognitive Digital Twin of the Consumer for Hyper-Personalized Marketing Strategy

Evolution of digital marketing strategies have gone much beyond the static user profiling due to the increasing demand for real time, hyper - personalization of consumer experience. In this paper, we propose an innovative architecture of a Cognitive Digital Twin of the consumer – ECCD-Twin (Emotionally-Aware, Context-Driven Digital Twin), which involves a fusion of multimodal emotion recognition, cross–modal contextual data analysis, and a set of dynamic prediction models of user intents to achieve a digital twin that has much higher fidelity than the existing models. It makes use of self-evolving knowledge graph and deep learning models to constantly update the digital persona of the consumer based on emotional states, environment and inferred micro intentions. Experimental evaluation verifies that ECCD-Twin achieves better performance than existing personalization approaches in the aspects of accuracy, adaptability and user engagement. In addition, it has an explainability module that enhances transparency and trust in marketing recommendations. ECCD-Twin describes a scalable, ethical and high impact improvement for intelligent consumer modeling and personalized content delivery.