Constructing a Unified Data Layer for Scalable 1:1 Personalization in B2C Digital Commerce Environments

Abstract

B2C digital commerce environments operate over heterogeneous transactional, behavioral, and content streams that evolve under high traffic, dynamic assortments, and rapidly changing customer intent. Existing personalization stacks are frequently fragmented, coupling models tightly to application surfaces, and producing inconsistent user experiences as channels proliferate. In this context, a unified data layer that consolidates identity, events, product knowledge, real-time signals, and model outputs into a coherent substrate offers a structured way to support scalable 1:1 personalization without privileging any specific algorithm or channel. This paper examines the construction of such a unified data layer targeted at high-throughput commerce platforms, focusing on architectural primitives, formal data contracts, and latency-aware serving constraints. The discussion emphasizes how this layer can support heterogeneous personalization workloads, including ranking, generation, pricing assistance, and content orchestration, while maintaining tractable guarantees on correctness, observability, and governance. Rather than optimizing for a singular notion of performance, the analysis considers trade-offs between modeling flexibility, operational simplicity, and robustness across a wide variety of traffic patterns and organizational contexts. The resulting formulation aims to clarify how a unified data substrate enables consistent decision-making for 1:1 personalization, under conditions of incomplete information, strict privacy requirements, and incremental system evolution, without prescribing a single algorithmic paradigm or fixed technology stack.