See what your inconsistency is costing you, and what NES recovers.
A two-part diagnostic. First, score your brand's predicted consistency from public signals. Then, see the dollar impact of improving that consistency on your existing revenue base. Numbers are scenario-bounded and calibrated to the NES v1.0 framework.
Step 1
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.
1. Across customer reviews and word-of-mouth, how consistently do customers describe their experience with your brand?
2. Repeat-purchase or retention rate trend over the last 12 months
3. Frequency of customer complaints about operational delivery (shipping, product quality, support response)
4. How aligned is your brand voice across customer-facing surfaces (website, ads, packaging, support, retail)?
5. What share of your customer complaints relate to expectation-vs-delivery mismatch (versus pure product quality)?
Predicted NES
+15
Band
Healthy but constrained
Consistency is positive, but the gap to compounding is meaningful. Material upside available from a structured consistency program.
Step 2
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.
Note. NES Value measures consistency-driven economic value on your existing customer base. Growth from new channels, markets, or product lines is modeled separately and stacks on top of NES Value. The investment number assumes the framework's standard intervention cadence over a 24-month engagement window.
Want a properly measured NES on your brand?
The numbers above are calibrated to public-signal inference. A measured NES requires structured review aggregation, behavioral data, and survey work. The first 25 brands in the prospective tracking cohort get engagement-specific calibration.
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.
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.
Predictive validity
The framework is positioned as v1.0. Predictive validity claims are exploratory and bounded. A 25-brand prospective tracking cohort is open and accepting participants. Engagement-specific calibration sharpens the coefficients with brand-specific data.