← Legibility Index 2026

Albertsons

Grocery & Supermarkets · #10 by 2025 U.S. retail sales ($81.77B)

Measured at www.albertsons.com on 2026-09-01

43
Thin
44 platform-adjusted
rank 99 of 111

Pillar breakdown

Each pillar shows the points earned against the maximum that was actually observable. Where a signal could not be observed it is removed from both sides rather than scored as a failure.

Product Data Depth

8.5 / 25

Buyer Answerability

9.8 / 20

Commerce Transparency

9.9 / 20

Machine Readability

4.7 / 20

Agent Interface

10 / 15

Every signal

Product Data Depth

Identity
4.5 / 6
Identifiers
4 / 12
Attribute depth
0 / 7

Buyer Answerability

Descriptive content
3.5 / 6
Imagery
3.3 / 5
Ratings and reviews
0 / 6
Catalog consistency
3 / 3

Commerce Transparency

Public pricing
4.4 / 5
Stock visibility
2.5 / 4
Shipping in markup
0 / 4
Return policy in markup
0 / 4
Un-gated access
3 / 3

Machine Readability

Product markup
3 / 6
Required-field completeness
1.3 / 5
Recommended-field depth
0.4 / 5
Variant expression
0 / 4

Agent Interface

Crawler access to products
4 / 4
AI crawler stance
3 / 4
Product sitemap
3 / 4
Agent surfaceplatform
0 / 3

The Legibility Gap

We read every sampled page twice — once as a plain crawler reading your published markup, once through an extractor. Where the two disagree, an agent is likely to disagree too.

Published, not recovered

gtin

Recovered, not published

sku

1 of 2 compared pages lost at least one identifier in extraction.

What the site-level probe found

robots.txt readableyes
AI crawler stanceno AI user-agents named (an absent rule permits the crawler)
Sitemapyes, including a product sitemap
UCP profilenone at /.well-known/ucp — this is not evidence against adoption
llms.txtnone

The pages we sampled

Drawn by seeded random selection from your own product sitemap — 40,000 candidate product URLs, seed "anglera-legibility-2026". Same seed, same pages, every time. Open any of them and check our arithmetic.

What would move this number

  • Publish a valid GTIN on every page. 13% of your sampled pages carried one that validates. It is the field an agent uses to match your product to the same product at three competitors.
  • Fill in the recommended Product fields. Your markup is present but shallow — shipping, returns, condition and price validity are the ones most often missing.
  • Express variants as a ProductGroup. Size and colour are the retail catalog, and without hasVariant an agent sees unrelated products.
  • Put ratings in the markup, not just the pixels. Reviews are the single biggest factor shoppers cite, and only 0% of your sampled pages expose them where a machine can read them.

What this score is not

It is not an audit and not a judgement of the business — it is a 8-page sample taken on a stated date, scored on a published rubric with no model judgement in it. Nothing here predicts whether any assistant will recommend you; for most answer engines that is decided by a private merchant feed we cannot see. If a number here is wrong, tell us and it goes in the public corrections log.

Go deeper than eight pages

This scorecard is a sample — eight product pages, drawn at random from a published seed. It tells you where Albertsons stands, not which SKUs are costing you. We’ll run a real sample of the whole catalog through the same extractor and send back the fields that are missing, by product.

Everything above stays free either way — the rubric, the sampled pages, the dataset, and every other company’s scorecard.