Hyper-Personalization at Scale: Using AI and Support History to Revolutionize Customer Interactions

Abstract

Hyper-personalization in customer support—leveraging unified behavioral, transactional, and historical data—significantly elevates customer experience (CX) while reducing operational friction. This article demonstrates how integrating data engineering pipelines with AI models (recommendation engines, NLP, predictive analytics) enables context-aware, dynamically tailored support interactions at scale. Analysis of two enterprise implementations reveals 18–35% gains in Customer Satisfaction (CSAT) and 20–45% reductions in handling time. Key innovations include real-time 360-degree customer profiling, adaptive knowledge delivery, and generative AI for response synthesis. Technical architectures, performance benchmarks, and best practices are dissected, alongside emerging trends like federated learning and emotion AI.