EMOTION-AWARE CRM SYSTEMS FOR HYPER-PERSONALIZED CUSTOMER JOURNEYS LEVERAGING FEDERATED SENTIMENT ANALYTICS AND PREDICTIVE INTENT MODELING IN PRIVACY-FIRST ENVIRONMENTS

In a rapidly digitizing market, the demand for hyper-personalized customer experiences has led to the convergence of emotion recognition, federated learning, and predictive modeling in Customer Relationship Management (CRM) systems. This paper proposes an Emotion-Aware CRM architecture that integrates federated sentiment analytics and user-level intent forecasting while ensuring data privacy. The model processes affective signals from multi-channel touchpoints, enabling dynamic, real-time personalization without exposing user data to centralized storage. Experimental validation shows improved conversion rates and reduced churn, indicating the viability of privacy-first, emotionally intelligent CRM infrastructures.