Real-Time Hyper-Personalization in Large-Scale B2C Digital Platforms: System Design, Data Pipelines, and Algorithmic Strategies for Sales Uplift

Abstract

Many consumer applications have shifted from static experiences to adaptive interfaces that respond to context, intent, and recent behavior. As mobile, web, and connected devices generate dense interaction streams, business teams seek to convert this signal into timely personalization that is accurate, privacy-aware, and operationally reliable. Real-time hyper-personalization on large-scale business-to-consumer platforms poses distinct challenges that span instrumentation, feature computation, decision orchestration, and evaluation under uncertainty. This paper discusses an end-to-end design for data pipelines and decision services that produce measurable sales uplift while meeting latency, throughput, and observability requirements. The design focuses on the separation of concerns between ingestion, feature computation, online policy selection, and outcome attribution, with attention to model drift, delayed feedback, and safety constraints. Algorithmically, the discussion considers ranking under intervention, uplift estimation, and counterfactual evaluation, along with practical guardrails for experimentation, caps, and pacing. The paper emphasizes the alignment between offline and online representations to minimize training-serving skew, and it outlines procedures to ensure deterministic semantics for near-real-time aggregates. The evaluation section places model quality alongside system reliability, emphasizing tail latency, cost awareness, and accountability through transparent fallbacks. The intent is to describe a neutral, implementable approach that can be adapted to different commerce surfaces and verticals, without asserting a single dominant pattern. The result is a system sketch that balances responsiveness, interpretability, and operational discipline to support incremental commercial outcomes on modern consumer platforms.