← Legibility Index 2026

Lululemon

Apparel, Accessories & Jewelry · #71 by 2025 U.S. retail sales ($6.33B)

Measured at shop.lululemon.com on 2026-09-01

49
Mixed
48 platform-adjusted
rank 86 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

11.9 / 25

Buyer Answerability

7.5 / 20

Commerce Transparency

16.6 / 20

Machine Readability

2.5 / 20

Agent Interface

10 / 15

Every signal

Product Data Depth

Identity
6 / 6
Identifiers
3 / 12
Attribute depth
2.9 / 7

Buyer Answerability

Descriptive content
3 / 6
Imagery
1.5 / 5
Ratings and reviews
0 / 6
Catalog consistency
3 / 3

Commerce Transparency

Public pricing
4 / 5
Stock visibility
1.6 / 4
Shipping in markup
4 / 4
Return policy in markup
4 / 4
Un-gated access
3 / 3

Machine Readability

Product markup
0.9 / 6
Required-field completeness
0.7 / 5
Recommended-field depth
0.3 / 5
Variant expression
0.6 / 4

Agent Interface

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

nothing lost — your markup survives extraction

Recovered, not published

brand

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

What the site-level probe found

robots.txt readableyes
AI crawler stanceblocks — 1 named, 1 blocked
Sitemapyes, including a product sitemap
UCP profilepublished (2026-04-08)
llms.txtnone

The pages we sampled

Drawn by seeded random selection from your own product sitemap — 3,905 candidate product URLs, seed "anglera-legibility-2026". Same seed, same pages, every time. Open any of them and check our arithmetic.

Your storefront returned readable markup to a plain client on 88% of the sampled pages. Where it did not, we make no claim about what the markup contains.

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.
  • Let standards-compliant clients read your product pages. Bot protection that blocks an honest crawler also blocks the agents you want quoting you.

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 Lululemon 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.