← Legibility Index 2026

Bloomingdale's

Department Stores · extended universe

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

40
Thin
42 platform-adjusted
rank 103 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

8.8 / 25

Buyer Answerability

7.7 / 14

6 pts not observable

Commerce Transparency

8 / 12

8 pts not observable

Machine Readability

0 / 20

Agent Interface

10 / 15

Every signal

Product Data Depth

Identity
5.8 / 6
Identifiers
3 / 12
Attribute depth
0 / 7

Buyer Answerability

Descriptive content
1 / 6
Imagery
3.7 / 5
Ratings and reviewsnot observable—
Catalog consistency
3 / 3

Commerce Transparency

Public pricing
5 / 5
Stock visibility
0 / 4
Shipping in markupnot observable—
Return policy in markupnot observable—
Un-gated access
3 / 3

Machine Readability

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

Agent Interface

Crawler access to products
4 / 4
AI crawler stance
2 / 4
Product sitemap
4 / 4
Agent surfaceplatform
0 / 3

What the site-level probe found

robots.txt readableyes
AI crawler stancepartial — 1 named, 0 blocked
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 — 49,998 candidate product URLs, seed "anglera-legibility-2026". Same seed, same pages, every time. Open any of them and check our arithmetic.

PageMarkupAttributesImagesGTIN
/shop/product/faherty-cabana-7.5-terry-towel-palm-tree-sweat-shorts?ID=6061070other JSON-LD04—
/shop/product/dalyn-kochi-washable-kc10-area-rug-collection?ID=5673675other JSON-LD05—
/shop/product/rodd-gunn-langs-beach-slim-fit-polo?ID=5024826other JSON-LD05—
/shop/product/elvie-catch-secure-milk-collection-cups-2ct?ID=5614175other JSON-LD05—
/shop/product/polo-ralph-lauren-polo-bear-fleece-hoodie?ID=5842132other JSON-LD06—
/shop/product/free-people-fisherman-wide-leg-pants?ID=6244707other JSON-LD06—
/shop/product/lay-lo-minimal-dog-bed?ID=4954943other JSON-LD04—
/shop/product/kendra-scott-enamel-mother-of-pearl-candy-cane-stud-earrings?ID=5799943other JSON-LD01—

What would move this number

  • Publish a valid GTIN on every page. 0% 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 Bloomingdale's 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.

Talk to us about fixing it