A Privacy-Preserving, Data-Driven Personalization Framework for B2C Digital Sales Optimization Using Federated Learning and Customer 360 Integration

Abstract

Business-to-consumer digital sales ecosystems have progressively shifted toward data-driven decisioning, with personalization engines determining exposure, pricing, and recommendations across web, mobile, and omnichannel surfaces. Concurrently, regulatory constraints, cross-jurisdictional data residency requirements, and increased sensitivity to surveillance practices have limited the feasibility of centralizing fine-grained behavioral data. Organizations operate multiple legacy stacks, fragmented identifiers, and inconsistent consent records that complicate unified modeling and raise the risk profile of conventional central warehouses. In this setting, there is interest in architectures that enable predictive personalization without concentrating raw identifiable data. This paper presents a privacy-preserving personalization framework that integrates a governed Customer 360 data model with federated learning and edge-resident policy execution for B2C digital sales optimization. The framework describes how identity resolution, feature derivation, consent-aware masking, and eligibility constraints can be combined with decentralized optimization protocols, secure aggregation, and calibrated noise. The focus is on compatibility with ranking, uplift, and budget-constrained exploration models under heterogeneous traffic, partial client participation, and non-stationary behavior. The analysis covers objective formulations, algorithmic components, and operational mechanisms for drift detection, fairness monitoring, and fail-safe fallbacks. Empirical evaluation on simulated and replayed multi-channel data illustrates the behavior of the proposed design under varying privacy parameters, participation levels, and consent churn, without overstating performance. The study aims to provide a technically detailed, implementation-oriented description of how Customer 360 integration and federated learning can be composed to support personalization that remains aligned with privacy, compliance, and engineering constraints in contemporary B2C digital sales environments.