Causal Inference and Uplift Modeling on Integrated Customer 360 Data for Targeted Personalization Strategies in B2C Digital Retail Platforms

Abstract

Business-to-consumer digital retail platforms generate extensive observational traces of browsing, search, transaction, messaging, and support interactions, which can be integrated into customer 360 representations. These representations combine identifiers, event histories, inferred preferences, and contextual attributes into longitudinal profiles capable of supporting targeted personalization. Despite the availability of such data, many operational strategies remain based on response prediction or heuristic segmentation, which can systematically conflate correlation with causal impact and lead to inefficient use of incentives, exposure, and capacity. This paper examines a technical framework for causal inference and uplift modeling built directly on integrated customer 360 data with the objective of estimating heterogeneous treatment effects and deploying stable, auditable targeting policies. The discussion focuses on definition of exposure units, temporal alignment of features and outcomes, assumptions for identification in mixed experimental and observational regimes, and the use of orthogonal, doubly robust, and policy-learning methods that operate under budget and operational constraints. Attention is given to the interaction between model structure, identity resolution strategies, and multi-channel treatment assignment, as well as to mechanisms for drift detection, overlap monitoring, and fairness-aware analysis. The framework is intended to be implementable in production environments that require strict latency, governance, and privacy controls, while remaining explicit about assumptions and sensitivities. The paper is descriptive rather than promotional, outlining a set of consistent design choices and analytical components that can be combined to support cautious deployment of causal personalization in B2C digital retail platforms.