Find where your brand is leaking trust, margin, and growth.
A two-part diagnostic. Score your brand's predicted consistency from public signals, then model the dollar impact of improving that consistency on your existing revenue base. Numbers are scenario-bounded and calibrated to the NES v1.0 framework.
Quick consistency diagnostic
Five questions. Each answer is what an attentive operator would observe about their own brand. The result is a Predicted NES on the framework's scale of −100 to +100, where positive numbers indicate consistency-driven compounding and negative numbers indicate consistency-driven decay.
Revenue impact calculator
Set your annual revenue and operational context. The calculator returns the dollar value of what your inconsistency is costing you today, what NES can recover, and the required investment to capture it.
See your real consistency score, not just the estimate.
The diagnostic above is your own read. Run your brand through the NES Scanner for a live, evidence-based consistency score in two minutes. Free, no login.
Run a free scan →Where these numbers come from
The calculator is calibrated to the NES v1.0 framework, documented in the SSRN working paper. Coefficients are not exposed in this interface to keep the diagnostic founder-friendly. The methodology behind them is summarised below.
Construct
NES measures consistency of customer experience over time. It is a measurement primitive, not a growth metric or efficiency multiplier. Consistency is the upstream driver of trust, retention, and revenue compounding.
Where AI fits
AI handles the heavy lifting on the data side: reading reviews across portals, aggregating themes, mapping them to the framework's consistency bands, and producing a Review-Inferred NES read. The framework, the calibration, and the interpretation remain human-locked.
Industry calibration
Each business category carries its own revenue sensitivity to consistency improvement, calibrated against published research from Forrester CX Index, ACSI, Bain, and Heskett's Service-Profit Chain. The framework adjusts sensitivity by category.
System state
Operating state determines how much of a consistency improvement lands as new revenue versus repaired leakage. Optimized systems convert improvement to revenue. Broken systems convert improvement to recovery first, revenue second.
Ambition setting
Improvement ambition reflects the size of the consistency program. Larger programs require proportionally more investment, with a dysfunction multiplier that increases when the system is operating poorly.
Public reference
Working paper available at netentropyscore.com/paper or on SSRN (Abstract 6667158). Trademark filed in India, Class 35.
Numbers are scenario-bounded estimates calibrated to research and are not a precision forecast. The framework does not claim precision predictive validity. Use the results to open conversations, not to commit to specific revenue projections.