Methodology
How we derive
the 74 and 18 scores
Every Xclaymation engagement begins with a structured diagnostic across two parallel evaluation tracks. The first track evaluates how a product is perceived and selected by human buyers. The second track evaluates how the same product is perceived and selected by autonomous AI procurement agents. The two scores — 74 and 18 — are the averages observed across the first track and second track respectively, measured across Xclaymation’s enterprise client base since 2024.
Track 1
Human buyer evaluation score
The human buyer score measures how a product performs across the signals that drive human selection decisions: category clarity, differentiation strength, proof density, messaging precision, and competitive contrast. Each dimension is scored against a structured rubric derived from documented enterprise buying behavior — how procurement committees evaluate vendors, how shortlists are built, and how final selections are made in regulated and complex B2B categories. The average score across this track for enterprise products entering an Xclaymation engagement is 74 out of 100.
Track 2
AI agent evaluation score
The AI agent score measures how the same product performs across the signals that autonomous procurement agents parse when evaluating vendors: structured data presence, schema markup coverage, machine-readable claim sets, index presence across the databases agents draw from, trust signal architecture, and query match density against the procurement queries agents run in the product’s category. These are not the same signals human buyers evaluate. They are structural, not narrative. The average score across this track for the same enterprise products is 18 out of 100.
The 56-point gap
The difference between 74 and 18 is not a messaging gap. A product that scores 74 with human buyers has strong positioning — clear category, compelling differentiation, credible proof. The same product scores 18 with AI agents because the structured infrastructure layer — the schema, the machine-readable claims, the trust signal architecture — has not been built. Human buyers read the narrative and are persuaded by it. AI agents parse the structure and find nothing to evaluate.
The gap is consistent. Across every enterprise engagement since 2024, no product has entered an X!MCO diagnostic with an AI agent score above 31. The infrastructure layer is uniformly absent in enterprise B2B, regardless of how sophisticated the human-facing positioning is.
How scores are calculated
Each track produces a composite score from 0 to 100. Individual dimension scores are weighted by their relative influence on selection outcomes — dimensions that determine whether a product appears on the shortlist at all are weighted more heavily than dimensions that influence final selection from an existing shortlist. The weighting methodology is proprietary. The scoring rubric is applied consistently across every engagement by Xclaymation’s diagnostic system.
What the scores do not measureWhat the scores do not measure
These scores do not measure brand awareness, content volume, social proof quantity, or search ranking. A product can rank on page one of Google, have fifty G2 reviews, and a polished website and still score 18 with AI agents. The scores measure structural readiness for the specific evaluation logic that autonomous procurement agents apply — nothing more, nothing less.
Start with the X!MCO Readiness Audit
Before engaging X!Vector or X!Anchor, we run a complimentary X!MCO Readiness Audit – a 48-hour benchmark of how your product currently shows up when AI agents evaluate vendors in your category.
One question matters: will an AI agent choose your product when no human is watching?