Designing Customer 360-Centric Recommender Systems: A Machine Learning Approach to Optimizing B2C Digital Sales and Engagement Outcomes

Abstract

In contemporary B2C environments, customer interactions are increasingly fragmented across web, mobile, physical channels, and third-party ecosystems. Organizations seek to consolidate these touchpoints into a unified Customer 360 view in order to understand behavioral patterns, align content delivery with latent preferences, and manage engagement at scale with consistent semantics across products and segments. Recommender systems sit at the center of this consolidation effort, mediating how customers discover content, offers, and services, and thereby shaping measurable outcomes such as click-through, conversion, retention, and downstream revenue. Traditional recommendation pipelines, often siloed by channel or product line, are not well aligned with a Customer 360 paradigm in which signals, constraints, and objectives must be integrated at the level of individual identities and their temporal trajectories. This paper develops a technical perspective on designing Customer 360-centric recommender systems for B2C digital sales and engagement optimization, focusing on representation learning for heterogeneous data, multi-objective modeling of business and behavioral outcomes, and architectures that close the loop between model outputs and operational feedback signals. The discussion emphasizes modeling formalisms for feature fusion, ranking, calibration, and counterfactual reasoning under production constraints, including latency, robustness, and governance. The aim is to outline a coherent machine learning approach in which Customer 360 data structures are not auxiliary assets but primary modeling substrates through which recommendation quality, consistency, and controllability can be achieved in a measurable and adaptable manner.