01 PUBLIC RESEARCH · 2026

How readable is ecommerce to machines?

We analyzed public evidence from 20 real ecommerce storefronts. The result is a practical baseline for product data, AI readability, discoverability, commerce readiness, and trust.

SAMPLE20

real public storefronts

OBSERVABLE17

returned usable public evidence

SCORED15

met the evidence threshold

MEAN SCORE77.4

among scored audits only

02 WHAT THE SAMPLE SHOWED

Machine-readable does not mean complete.

The strongest recurring pattern was not total absence of product data. It was incomplete identity, offer, and verification context around otherwise visible products.

GTIN/EAN missing11 of 15 scored audits

Stable identifiers help matching, comparison, and offer verification across systems.

Seller identity not machine-readable5 of 15

Merchant identity connects products, offers, policies, and responsibility.

Product structured data not detected3 of 15

Without an explicit Product entity, machines must infer what is being sold.

Contradictory offer data detected2 of 15

Conflicting price or availability signals reduce confidence in an offer.

Counts are observations within the 15 audits that met the scoring threshold. They are not estimates for all ecommerce stores.

03 SCORE DISTRIBUTION

A useful spread, not a vanity score.

Five stores did not receive a readiness score because public evidence was insufficient or blocked. Confidence is measured separately from readiness.

LOWEST ISSUED · 60MEAN · 77.4HIGHEST · 94

04 FIVE DIMENSIONS

Readiness is a system.

A store can be crawlable but still difficult to compare, verify, or act on. The benchmark keeps these dimensions separate.

01

Discoverability

Access, canonicals, sitemap signals, and retrievable commerce pages.

02

Product data

Product entities, identifiers, variants, price, currency, and availability.

03

AI readability

Clear facts and context that reduce machine inference.

04

Commerce readiness

Visible offer and purchase signals supporting high-intent workflows.

05

Trust & evidence

Merchant identity, policies, contact paths, and verifiable claims.

05 METHODOLOGY

Public evidence, bounded claims.

Sample

20 publicly accessible storefronts covering Shopify, WooCommerce, Magento/Adobe Commerce, and custom commerce; small and large catalogs; single- and multi-language stores; fashion, food, electronics, home/design, and B2B contexts.

Collection

Read-only retrieval of public homepage, category, product, policy, sitemap, robots, visible commerce facts, and structured data. No purchase, account, or form submission.

Scoring

Five deterministic subscores. A readiness score is issued only when the evidence threshold is met. Confidence remains independent from readiness.

Reporting

Public findings are aggregate. Store-specific observations remain out of this report to prevent misleading league tables or decontextualized claims.

06 INTERPRETATION LIMITS

What this benchmark does not claim.

It is not a census of ecommerce.

It does not measure private catalog, feed, ERP, or Merchant Center data.

It does not guarantee AI citations, rankings, traffic, or revenue.

It is a time-bound sample of evidence publicly observable during collection.

YOUR STORE / REAL PUBLIC EVIDENCE

See what machines can verify about your store.

The Free AI Commerce Check samples public pages and shows preliminary findings. The optional Full Audit costs EUR 299.

Run the Free Check