Retail personalization has moved from segment-level targeting to individual, real-time adaptation. This paper examines the analytics architecture that shift requires: unified customer data, streaming behavioural signals, and machine learning models that serve decisions at interaction latency rather than in overnight batches.
It covers recommendation systems, dynamic pricing, and the alignment of personalization with inventory and supply-chain constraints, and discusses the governance and privacy considerations that determine whether such systems can be operated at scale.