One analysis, two transform passes.
The 13 dimensions, across two sub-indices.
Catalog Quality and Discoverability
8 weightedAI Commerce Readiness
4 weightedThe standards we score against.
ECLASS and ETIM lead on technical and distributor catalogs; Google, Amazon, and GS1 carry consumer catalogs. Each maps to specific dimensions and weights, published on the methodology.
ETIM
ECLASS
UNSPSC
GS1
Google
Amazon
AI Commerce Readiness. Built for how machines buy.
Buying is becoming machine mediated. AI agents and answer engines now discover, compare, and increasingly purchase on behalf of the people who used to click through your site. They do not read your catalog the way a person does. They read structured data, feeds, and protocol fields. When those come up short, the agent does not ask for help. It moves on to the next result.
Sub-index B measures whether your catalog is ready for that world. It scores your data against the channels and protocols that now route machine-driven demand.
Marketplace and shopping feeds
Google Shopping, Amazon, and Walmart each define what a product feed must contain to be eligible and to rank. We score your catalog against those requirements.
Agentic commerce protocols
The Agentic Commerce Protocol (ACP), which sits behind ChatGPT checkout, and Google's Universal Commerce Protocol (UCP), which sits behind AI Mode in Search and the Gemini app, set what a catalog must expose for an agent to discover and transact. We score your readiness against them.
These specifications change constantly. ACP and UCP have each shipped multiple revisions this year, and the marketplace feed requirements move with them. We track those changes so you do not have to. Your readiness score reflects the current requirements, not last quarter's.
We measure readiness. We do not implement the protocols for you or move your products through them. We tell you, surface by surface, where your catalog is ready and where it falls short.
Tuned to your industry, not a generic average.
Catalog quality does not mean the same thing in every industry, so we do not score every industry the same way. The Index weights shift to the dimensions that actually drive quality in your category, and the methodology version that produced your score is published with it, so the tuning is visible, not hidden.
The difference is real. In fashion and apparel, imagery and descriptive prose carry the buying decision, so the weighting leans there. In technical B2B distribution, the decision lives in the data: attribute completeness, specification accuracy, and technical documentation are what a buyer matches on, so that is where the weight goes. In automotive aftermarket parts, fitment and compatibility decide everything, because a single wrong attribute is a wrong part and a guaranteed return. Same instrument, tuned to what matters where you sell.
Why it is built for B2B
Most catalog scoring was built for consumer retail, where a good photo and a paragraph win. B2B quality does not live there. It lives in the attributes, the specifications, and the technical documentation, the parts of a catalog that consumer-grade tools underweight or skip. CatalogIQ is tuned to measure exactly what a technical buyer, a search engine, and an AI agent actually use to choose a B2B product.
When your industry has no standard, set your own
Some categories work past the edge of any published standard. In these cases, at the Enterprise tier, you define your own: the attributes you require, the values you accept, and the quality bar you hold your catalog to. We measure every product against that standard, continuously, on every recrawl.
And it stays independent. A standard you define is still an explicit, recorded standard, and your score is measured against it, not against our opinion. You set the bar. The re-score holds you to it.