Net Entropy Score: A Framework for Measuring Brand Consistency Variance and Its Compounding Effect on Revenue

v1.0.1 (May 2026 revision): This version corrects an editorial error in Section 5 of the v1.0 paper. The convergence gap formula was previously stated as "Measured NES minus Predicted NES"; it has been corrected to "Predicted NES minus Measured NES." The substantive interpretation throughout the paper, including the threshold table in Section 5 and all empirical cases in Section 6, was already aligned with the corrected formula direction. Additional hygiene fixes in v1.0.1: zone-band intervals tightened to remove boundary overlaps, threshold table extended to include a Monitor zone band, and case-count phrasing standardized across the paper. All findings, conclusions, and case interpretations are unchanged. v1.0.1 is purely an editorial revision; the framework's diagnostic logic is identical to v1.0. This paper introduces the Net Entropy Score (NES), a framework for measuring brand consistency variance and quantifying its compounding effect on revenue. NES treats inconsistency, not satisfaction, as the load-bearing variable in compounding businesses, and provides a -100 to +100 measurement scale grounded in public review aggregation and internal experience signals. The paper distinguishes Predicted NES (calibrated from public signals) from Measured NES (calibrated from internal data) and introduces the convergence gap as a diagnostic for brand-experience alignment. A four-layer model maps from measurement to mechanism to outcomes to business impact, with a closed-form revenue-leakage estimate calibrated to industry-specific operational profiles. Worked applications to multi-billion-dollar retail (Walmart, ~$680B revenue) and early-stage DTC (Brand Undisclosed, ~$3M revenue) demonstrate scale invariance, with industry calibration handling the difference. Limitations and avenues for empirical validation through structured customer surveys are discussed. The framework is trademark-filed in India (Class 35) and was first deposited on SSRN (Abstract ID: 6667158). The full working paper and supporting materials are available at netentropyscore.com.