AI-Powered Customer Journey Mapping: Leveraging Deep Learning for Real-Time Personalization and Conversion Optimization in Omni-Channel Retail

Current customer journey systems are rigid, depending on hard-coded choices and touch points that don't connect. They can't make decisions in real time about both digital and physical touchpoints. The hybrid architecture that was presented in response uses Transformer-based sequence modeling with Graph Neural Networks (GNNs) and Reinforcement Learning to find the best next action. We tested the framework with a real-world omnichannel retail dataset that had 84,672 users and 512,438 sessions. Over the course of 9 months, these systems gathered 2.94 million interaction events from online, mobile CRM, and offline (in-store) channels. The experimental outcomes for the discriminative task yield an AUC of 0.91, and both NDCG@10 and MRR demonstrate satisfactory performance on their respective scales compared to Standard challenges, as indicated by the logistic proposed model. It does better than most of the other approaches, like the Matrix Factorization Model (MF), DeepFM-based methods, and Transformer-only architectures. The study, which used a sample of 18,000 users and an online A/B test, found that the conversion rate improved by 13.8% (p < 0.05), the average order value grew by 11.2% (p < 0.01), and the churn rate dropped by almost 9.4% (p < 0.01). The result confirms that explainable hybrid AI-powered customization can boost consumer engagement by 7X and improve retail decision intelligence.