Adidas SEO & GEO Audit: Does Google AI Cite Adidas.com?

What is Adidas.com trying to do — and why audit it second?

This is part 2 of GeoParrot’s sports-brand series, running the same evidence-first method we used on Nike and, before that, on IKEA. Adidas is the obvious next name — Nike’s closest direct competitor, with its own hybrid catalogue-plus-membership model (adiClub), and a flagship lifestyle shoe, the Samba OG, that plays the same cultural role Nike’s Air Force 1 does: a decades-old silhouette currently having a mainstream streetwear moment.

The product we tracked: Adidas Samba OG

AI citation snapshot: 10 real queries, Google AI Mode

We sent the same style of 10 verbatim queries we used on Nike — brand, product and comparison intent — to Google AI Mode (udm=50) and checked whether a cited source was labelled with the Adidas brand name. Method note, reported honestly: one citation chip was initially labelled only in Korean (“아디다스 공식 온라인 스토어” — “Adidas Official Online Store”) and our first pass, which matched on the English string “adidas,” missed it; we caught it on manual review and corrected the count below. Direct a[href] capture again returned almost no raw links this run, consistent with what we found auditing Nike on October 22.

Result: adidas.com cited in 4/10 queries — slightly behind Nike’s 5/10 on the same query style, despite Adidas’s stronger page-level schema (see G-score section below). The pattern echoes what we found on Nike: direct brand, policy and loyalty-program queries get cited; every head-to-head comparison query (“vs nike,” “vs new balance,” “samba vs gazelle”) and the single named-product review query lost to independent publishers, Reddit or TikTok. We could not get a usable ChatGPT read this run — the web composer selector failed to load, the same failure mode we hit auditing Nike and flagged originally in our Week 17 arc — reported as a measurement failure, not a zero.

G-score audit: the 8-pillar hygiene checklist, six pages

We scored six representative Adidas URLs with the same 8-pillar static-HTML checklist (JSON-LD present, FAQ content, visible date, author byline, concrete data points, table/list structure, 3+ subheadings, a 30–500 character early answer) used in our Week 17 quality-score experiment and on Nike.

Average G-score across six pages: 0.646 (Nike’s equivalent audit: 0.4375).

The gap versus Nike comes almost entirely from two pillars: both Adidas product pages pass “date” and “author” because every individual Review object in the page’s JSON-LD carries a datePublished and an author field — something Nike’s Air Force 1 page had none of. FAQ schema is still absent everywhere on both sites, including the Help centre, which — like Nike’s — displays FAQ-style question links in the UI with no FAQPage structured data behind them.

Technical check: robots.txt, llms.txt, sitemap, schema, locale

What works / What to verify

Target query check — reported honestly, not inflated

We have not re-measured “adidas seo” or “adidas geo audit” query volume this session. Per the pattern already confirmed across six earlier subjects in this series on October 20, 2026 (Keyword Planner, US) — five of six showed no measurable search volume at all, and the sixth (IKEA) showed only roughly 10 monthly searches — we’re not claiming this title phrase drives ranking-based traffic. The distribution case for this series rests on internal links, our own index, and readers already following the GEO Lab arc, not on search demand for “[brand] SEO audit” as a query.

Frequently asked questions

Does Adidas’s richer review schema mean it’s better positioned for AI answers than Nike?

On paper, yes — AggregateRating, individually dated and authored Review objects, and a structured return policy are all present on Adidas’s Samba OG page and absent from Nike’s Air Force 1 page. But Adidas was actually cited in slightly fewer of our 10 test queries (4 vs Nike’s 5), so richer schema did not translate into a higher citation rate in this snapshot.

Why does Adidas name ClaudeBot specifically in robots.txt but not other AI crawlers?

We can’t know Adidas’s intent from the outside — robots.txt doesn’t explain itself. What we can say is that it’s an asymmetry: a 6-second crawl-delay is applied to one named AI crawler while GPTBot, PerplexityBot and Google-Extended aren’t mentioned at all, so they fall under the same general User-agent: * rule as every other bot.

Does a G-score of 0.65 mean Adidas is more likely to be cited by AI engines than a lower-scoring site?

Not reliably — and this audit is itself a small data point against that idea. Our Week 17 experiment found hygiene/structure score was not a dependable predictor of AI citation (cited pages in that test actually scored lower on average than non-cited ones). Here, Adidas scored meaningfully higher than Nike on our 8-pillar checklist (0.65 vs 0.44) but was cited in fewer of the same 10 test queries — directionally consistent with “G-score is not the lever,” not proof of it on its own.

Is the llms.txt redirect-instead-of-404 behavior something Adidas should fix first?

Probably not the priority — Google Search and Google AI Mode don’t read llms.txt either way; see our full breakdown of what llms.txt actually does. The more concrete, verifiable item from this audit is the ProductGroup/Product schema-type split between the US and Korea product pages for the same SKU, which is worth resolving before worrying about a text file Google doesn’t consume.

This is part 2 of a daily series. Every day, GeoParrot runs this same evidence-first method — real screenshots, a 10-query AI citation snapshot, an 8-pillar G-score audit, and an honest target-query check — against one more major sports and athletic-wear brand. Tomorrow: the next name on the list.