End-to-End Measurement of Personalization Effectiveness in B2C Digital Channels

Abstract

Personalization in B2C digital channels has emerged as a standard operational paradigm as firms seek to align content, pricing, and experiences with heterogeneous customer preferences, constraints, and contexts. Rapid proliferation of algorithmic decisioning across web, app, email, SMS, and paid media channels has increased the need for systematic end-to-end measurement frameworks that quantify incremental value without conflating targeting, selection effects, and background trends. At the same time, organizational dependence on heuristic attribution and local metrics introduces distortions in how personalization strategies are evaluated, optimized, and governed. This paper examines an integrated, channel-agnostic measurement architecture for personalization effectiveness that begins at exposure and decision policies, traces outcomes across sessions and devices, and aggregates results at horizons aligned with economically meaningful objectives such as customer margin, retention, and engagement stability. The proposed perspective emphasizes consistent identity resolution, explicit logging of decision alternatives, careful design of randomization and quasi-experimental variation, and robust causal estimands that can be computed at scale under operational constraints. The discussion includes modeling approaches suited to high-dimensional treatments and policies, multi-touch environments, and dynamic feedback between personalization systems and user behavior. By analyzing measurement design as an end-to-end system property rather than a localized analytics exercise, the paper outlines how organizations can obtain more stable, interpretable, and reproducible estimates of personalization impact while retaining flexibility in algorithms, channels, and optimization criteria.