Real-Time Hyper-Personalization at Scale: A Contextual Bandit Architecture for Dynamic Customer Experience Optimization in Omnichannel Retail

Abstract

Real-time hyper-personalization is increasingly important in omnichannel retail because customers interact across web, mobile, CRM, and in-store channels with rapidly changing intent. Existing personalization systems still often rely on static segmentation, rule-based targeting, or batch recommendation pipelines that adapt slowly to live customer behavior. This gap matters because weak real-time decision quality can reduce engagement, conversion, and customer experience consistency across channels. This article presents a contextual bandit architecture for dynamic customer experience optimization in omnichannel retail, where each interaction is treated as a context-aware decision event and the policy is updated online from observed response signals. The results show that richer customer context improves personalization accuracy across learning rounds and that the proposed framework outperforms conventional personalization strategies in engagement and conversion performance. The study shows that contextual bandit learning can support scalable real-time hyper-personalization in modern retail systems.