This article addresses the adaptation of A/B testing methodology for the specific conditions of offline FMCG retail, where its application faces unique challenges: high levels of data noise, the influence of external factors, and physical constraints. The author identifies key gaps in existing research, including the lack of integration of experiment goals with traditional retail metrics and insufficient consideration of qualitative factors. As a solution, a methodological framework is proposed—comprising the Integrative DuPont-SCOR Model → the ROAR Approach → the ROILFL Metric—which presents a comprehensive solution for the strategic alignment of A/B testing goals in offline retail, addressing the gaps identified in the literature review. The central element of the methodology is the introduction of a Unified General Evaluation Criterion (UGEC)—the ROILFL (Return on Investment in Like-for-Like Sales Growth) metric. This allows for an isolated assessment of the effectiveness of managing the existing store network by filtering out the "noise" from investments in expansion and general corporate development. The practical significance of the work is confirmed by a comparative analysis of leading retailers in Russia and the United States, as well as detailed modeling based on data from "Magnit" company, which revealed a potential imbalance in investment strategies. The proposed approach transforms A/B testing from a tactical tool into a system for strategic business performance management.