Public sample
A real scan of Nike: report structure, evidence captures, the scorecard, PDF export, and the prioritised implementation path. Every number is checkable — this is exactly what your brand's report looks like.
AI visibility audit report
Directional read: AI already names Nike in 24 of 25 buying questions — here's how to defend and widen that lead.
Directional read: AI already recommends Nike in 24 of 25 sportswear and athletic apparel buying questions (96% AI presence). This audit shows how to defend and widen that lead.
Treat the summary as a directional read, not a settled fact pattern.
What drives the score
By buyer-journey angle
The one-line truth
This report is built so a non-technical founder can read it top-to-bottom and know exactly what to do. Every finding is a row you can open — the dropdown contains the action, why it matters, and what we saw.
AI Visibility (GEO) · Retrieval Engine
Live engines queried at Tue, 9 Jun, 2026, 8:46 pm IST
Visibility per AI engine
Aggregated by majority vote across engines and 5 runs— so a one-off AI fluke can't swing your score.
How AI talks about Nike
Who AI recommends instead
A question you lose
“New or emerging sportswear and athletic apparel brands in the US and global market worth trying.”
AI named Gymshark, Vuori, Outdoor Voices — not Nike.
What we asked Retrieval Engine · 25 buying questions
Do these first
Ranked by how much demand each gap is costing you. Start at the top — the full paste-ready fix for every one is in the Fix Kit below.
Executive summary
Evidence confidence
1 claim fully backed · 3 directional
We surface this so older or inference-led claims do not read with the same certainty as fresh observed evidence.
Biggest leak
Under Armour was the most-named brand in your category (73 total mentions across 25 sportswear and athletic apparel buying questions × 5 runs), while you were named 24 times — AI is routing that demand to them. (Measured for the inferred category "sportswear and athletic apparel" in the US and global market. If that's not your category/market, this finding may not apply.)
Strongest proof
AI/search systems can already identify the brand entity better than the average early D2C store.
Priority move
Directional read: Add product schema and tighten the product detail surfaces first. This is the cleanest way to improve machine readability without changing the brand story.
Why this audit reads this way
Different brands produce different audits because Outkast adapts the question bank, rule gating, and confidence language to the brand context we could actually verify on this scan.
marketplace
Category: products across many categories (high)
Market: global (medium)
This context decides which findings are relevant enough to survive into the final audit.
25 prompts · 5 passes
66% cross-engine / cross-run agreement
Cross-engine agreement makes the verdict sturdier than a one-model anecdote.
36 of 38 audit rules applied to Nike;2 were filtered out by context gating.
Most rules applied to this brand.
Revenue leakage · index
A relative diagnostic index of how far each area is from a healthy target, weighted by typical revenue impact — NOT a prediction or guarantee of rupees gained or lost.
How this is calculated:a weighted index of the gaps below — each area’s distance from a healthy target, weighted by how much revenue that area typically drives. It shows where demand is leaking and what to fix first; it is a directional signal, not a measured rupee amount or a promised recovery.
Share of AI buying-question answers vs an 80/100 target. ~30% of buyers research via AI; LLM-referred traffic converts 30–40%.
Product surface, buying path (WhatsApp/COD), visible policies vs an 82/100 target. Documented checkout friction ≈ 35% conversion lever (Baymard).
HTTPS, security headers, policies, contact vs an 85/100 target. Cold buyers verify these before a first prepaid order.
Review depth, rating, source breadth vs an 82/100 target. Proof density is a direct trust lever for unknown D2C brands.
Real PageSpeed mobile performance vs an 80/100 target. 78% of D2C traffic is mobile; 53% abandon a load >3s.
Consultant scorecard · 5 categories
AI-era target 80
At or above target
Buyers now ask AI search engines for brand recommendations. Strong GEO audits measure real citation share, not just SEO.
AI engines name the brand for buying questions — defend and widen the lead.
Premium target 80
30 points to close
Knowledge panel, third-party mentions, and SERP footprint decide whether the brand looks established or invisible.
The brand is thin in search/Google, which makes every paid click less efficient.
AI-era target 78
34 points to close
AI/Google recommend brands they can structurally 'read' — Organization/Product schema, sitemap, machine-readable identity.
Structurally thin: missing schema/entity signals means AI and Google struggle to understand and cite the brand.
Premium target 82
62 points to close
Products with 5+ reviews are far more likely to sell; review depth is a trust lever, not a vanity note.
Review depth isn't enough to make a cold buyer feel safe choosing this brand over a known name.
Trust target 85
At or above target
Cold buyers check HTTPS, return/refund/shipping policies, and a real contact path before paying — security badges lift checkout ~32%.
The trust basics are visible — keep policies and contact one tap away.
D2C target 85
At or above target
D2C audits treat Instagram and WhatsApp as real buying surfaces, not secondary social links.
The social buying path is visible enough to support founder-led conversion.
Mobile target 80
38 points to close
78% of D2C traffic is mobile and 53% abandon a site that loads >3s. Measured on real PageSpeed field data, not a guess.
A slow or janky mobile experience is silently abandoning buyers before they reach the cart.
Conversion target 82
12 points to close
Fixing documented checkout friction lifts conversion ~35% (Baymard) — guest checkout, cost transparency, payment + buying-path clarity.
The path to purchase is leaking buyers: tighten the product surface, buying path (WhatsApp/COD), and visible policies.
GEO Visibility · detail layer
GEO Readiness · flagship
The radial breaks the GEO readiness signals into their component scores. The hero GEO Visibility Score above weights Presence (35%), Share of Voice (25%), Prominence (15%), Consistency (15%) and Authority (10%) — observed-or-zero on every factor.
Sentiment is tracked apart from visibility as a separate risk axis (see the AI Visibility verdict above): being named a lot while spoken about negatively is a risk, not a win — so it is never folded into the score.
Voice & authority · AI's view of you
The sources AI engines actually read in your category, what they say about you when they read them, and any place they got the facts wrong — folded into one block so the story reads top-to-bottom.
Who feeds the AI · source authority
The AI's recommendations in your category are driven by 69 domains — your own site is NOT among them. You were mentioned in 24/25 answers, but your own domain was cited as a source in only 0.
Nike was mentioned in 24 of 25 AI answers · your own site was cited as a source in none of them.
Mentioned means the AI named you. Cited means the AI actually read your own website as a source. Earning citations — not just mentions — is what makes the recommendation durable.
Named & cited
0
AI knows you and reads your siteNamed, not cited
24
Recommended from others' contentCited, not named
0
Site was read but you missed the listAbsent
1
Neither named nor citedThese are the 69 domainsshaping AI answers in your category, ranked by how often they were cited. Go earn presence where you're absent.
AI pulls from these source types in your category. Earning a presence on them — not just your own site — is what makes AI recommend you durably (right now your own site isn't cited at all).
Earn a mention in the editorial / comparison content that ranks brands in your category.
e.g. AI cites thesporting.blog here.
Claim and optimise your marketplace listings — AI reads product pages and ratings there.
e.g. AI cites amazon.com here.
AI/search systems can already identify the brand entity better than the average early D2C store.
Instagram and WhatsApp commerce signals are strong enough to support founder-led conversion, not just awareness.
Evidence snapshot
No strong product schema signal was detected.
Refreshed 6/9/2026
A review/testimonial surface was detected (the on-page review count is not published).
Refreshed 6/9/2026
A WhatsApp inquiry or support path is visible.
Refreshed 6/9/2026
Shipping, returns, privacy, or support pages were detected.
Refreshed 6/9/2026
17 search-result signals included third-party mentions.
Refreshed 6/9/2026
Instagram was detected as part of the commerce surface.
Refreshed 6/9/2026
Lighthouse performance 9/100, accessibility 93/100, SEO 100/100.
Refreshed 6/9/2026
Performance & Core Web Vitals
Trust & Security · live checks
AI and cold buyers weigh verifiable, recent, photo-backed reviews far more than a bare star count — and unverifiable reviews carry FTC risk.
India conversion · COD / UPI / RTO
Return-to-origin (RTO) on COD orders is a top-3 D2C P&L line — each failed COD costs ₹350–700. Prepaid incentives + visible trust badges shift orders to prepaid, which returns far less.
No trust certifications (FSSAI, secure-checkout, dermatologically-tested, etc.) were detected near the buy decision — cold buyers look for these before a prepaid order.
Technical health · crawl checks
Structured data looks valid — WebSite carry their required rich-result fields.
No noindex directive found (robots: "index, follow") — search engines and AI can index this page.
Canonical points to "https://www.nike.in/" — confirm that's intentional.
All 13 sampled images have alt text — good for image discovery and screen-reader buyers.
“Nike. Just Do It. Nike IN” (25 chars).
Present, 131 chars.
Exactly one H1.
All assets load over https.
No Product structured data found — product rich results (price, stars) can't show in Google or AI answers.
Fix: Add Product JSON-LD on product pages (most Shopify themes/apps do this).
No Organization structured data — AI engines have no machine-readable brand identity to cite.
Fix: Add Organization JSON-LD with name, url, logo, and social sameAs links.
No FAQPage structured data — AI assembles answers from Q&A-shaped content, so it paraphrases rivals instead of quoting you.
Fix: Publish an FAQ (shipping, sizing, returns, COD/UPI) with FAQPage JSON-LD.
All major AI crawlers are allowed to read your site.
Entity found: Nike Q483915 exact match
American athletic equipment company
Visual proof
Website surfaces
Desktop capture of the current brand storefront or homepage.
If the first desktop view lacks category clarity, proof, or contact confidence, colder buyers bounce before product intent compounds.
Mobile capture of the storefront, where many D2C buyers first inspect trust signals.
Mobile friction is usually where discovery-led interest dies first, especially when popups, weak proof, or clutter block the buying path.
Deeper commerce capture used to inspect product trust, pricing, and buying cues beyond the homepage.
If the deeper commerce surface is thin, buyers may discover the brand but still hesitate at the product decision point.
Mobile commerce capture used to inspect above-the-fold CTA, trust, and checkout friction closer to real buyer behavior.
If the mobile product or collection view hides the CTA, trust proof, or pricing context, impulse intent dies before the buyer reaches cart.
Desktop cart page attempt — reveals CTA clarity, shipping/return proof, and checkout-friction signals.
If the cart page buries the CTA or lacks trust signals (returns, payment logos, contact proof), checkout abandonment rises sharply.
Mobile cart page — the most common D2C checkout-abandonment surface.
Mobile cart friction is the biggest single revenue leak for most D2C brands — hidden fees, no trust badges, or a slow checkout destroys impulse intent.
Public social surfaces
Public Instagram profile capture used to inspect bio clarity, proof surface, and the path from profile to product or WhatsApp.
If the public profile does not explain the category, trust, and next step in seconds, discovery stays social but conversion intent leaks before the site visit.
External URL found in the Instagram bio — the brand's chosen link-in-bio or product page.
If the link-in-bio is broken, slow, or redirects to an unrelated page, the social-to-site handoff is leaking buyers.
Facebook profile — public presence and brand page trust signals.
A missing or inactive Facebook profile can reduce social proof for discovery-stage buyers.
X/Twitter profile — public social presence and engagement signals visible to buyers.
An inactive X/Twitter profile can signal brand neglect to buyers who check social proof before purchasing.
Source influence map
https://www.nike.com is reachable and can support a stronger audit trail.
If product, policy, review, and schema evidence stay thin, buyers still need to work too hard to verify the brand.
Next: Make the site the canonical source for category, products, policies, reviews, and schema.
Refreshed 6/9/2026
AI visibility readiness is 86/100.
AI and search systems may identify the brand but still hesitate to cite or recommend it for product-level buying questions.
Next: Add structured data, answer-ready category copy, and citation-worthy product/support content.
Refreshed 6/9/2026
Google trust readiness is 50/100.
The brand can look active on social while still feeling thin in Google, reviews, and third-party proof.
Next: Build consistent entity proof, review surfaces, sameAs links, and public profile consistency.
Refreshed 6/9/2026
Instagram is part of the scanned commerce surface.
Instagram-first discovery is hard to convert if product category, buying path, and trust proof are inconsistent.
Next: Align bio, product category, link path, highlights, proof, and WhatsApp/DM handoff.
Refreshed 6/9/2026
A WhatsApp or inquiry handoff is visible.
Intent leaks when a buyer likes the brand but cannot quickly ask about size, price, shipping, or payment.
Next: Add obvious WhatsApp/DM entry points and saved replies for the common buying objections.
Refreshed 6/9/2026
A review/testimonial surface was detected (review count not published on-page).
Cold buyers need third-party proof before trusting an unknown D2C brand, especially before prepaid purchase decisions.
Next: Collect compliant reviews and place proof near product intent, policy pages, and checkout objections.
Refreshed 6/9/2026
Lighthouse performance is 9/100 and accessibility is 93/100.
A weak page-experience layer increases bounce, trust hesitation, and friction for assistive-tech users.
Next: Treat page speed, semantic structure, contrast, and labeling issues as trust work, not just frontend polish.
Refreshed 6/9/2026
Prioritised issues · finding → fix
1 critical issue costing you buyers right now, then 3 opportunities. Click any row — the dropdown is the fix.
The Fix Kit · paste-ready
Paste each block inside its own <script type="application/ld+json"> tag in your site's <head>. Put the Organization block site-wide; put a Product block (with the real name/price) on each product page.
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Nike",
"url": "https://www.nike.com",
"description": "Nike - Official Online Store for Athletic Shoes, Clothing & Sports Gear. Shop the latest Nike products and enjoy free shipping.",
"sameAs": [
"https://instagram.com/nike"
]
}
{
"@context": "https://schema.org",
"@type": "Product",
"name": "Nike Products Across Many Categories",
"brand": {
"@type": "Brand",
"name": "Nike"
},
"description": "Replace with this product's real name, description, and price.",
"offers": {
"@type": "Offer",
"priceCurrency": "INR",
"price": "0.00",
"availability": "https://schema.org/InStock"
}
}Edit the answers to match your real policies, then paste inside a <script type="application/ld+json"> tag on your FAQ (or home) page. This is what AI quotes back to buyers.
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "Do you offer Cash on Delivery (COD) and UPI?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Yes — we accept COD, UPI, cards, and net banking at checkout."
}
},
{
"@type": "Question",
"name": "What are your shipping times and charges?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Orders ship in 24–48 hours and are delivered across India in 3–7 days. Replace with your real timelines."
}
},
{
"@type": "Question",
"name": "What is your return and exchange policy?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Easy returns/exchanges within 7 days of delivery. Replace with your real policy."
}
},
{
"@type": "Question",
"name": "How do I choose the right size?",
"acceptedAnswer": {
"@type": "Answer",
"text": "See our size chart on each product page, or message us on WhatsApp for help."
}
},
{
"@type": "Question",
"name": "How do I contact Nike?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Reach us on WhatsApp or email — we reply within one business day."
}
}
]
}Broken links lose buyers mid-journey and waste crawl budget.
1. https://www.nike.com/help-center → returns 404; fix or 301-redirect it.
2. https://www.nike.com/lp/football-desktop?root=nav_3&ptype=listing%2Cnew-%26-featured%2Cfeatured%2C1%2Crip-the-script → returns 404; fix or 301-redirect it.
3. https://www.nike.com/new-arrivals/c/94475?root=nav_3&ptype=listing%2Cnew-%26-featured%2Cfeatured%2C2%2Cnew-arrivals → returns 404; fix or 301-redirect it.
4. https://www.nike.com/best-sellers/c/94167?root=nav_3&ptype=listing%2Cnew-%26-featured%2Cfeatured%2C3%2Cbestsellers → returns 404; fix or 301-redirect it.
5. https://www.nike.com/top-picks-under-4999/c/94477?root=nav_3&ptype=listing%2Cnew-%26-featured%2Cfeatured%2C4%2Ctop-picks-under-%E2%82%B94999 → returns 404; fix or 301-redirect it.
6. Re-test after deploying.Competitor head-to-head
| Metric | Under Armour | Adidas | Nike (you) |
|---|---|---|---|
| AI Presence | 292% | 288% | 96% |
| GEO Readiness | not verified | not verified | 44/100 |
| Reviews | — | — | 0 |
| Product schema | — | — | |
| Trust grade | — | — | F |
Under Armour is ahead of Nike in AI answers (292% vs 96% AI presence). The table shows where each rival is structurally stronger.
Report-quality gaps
high gap
Current: Nike has a generated score and public-signal diagnosis.
Industry standard: Strong agency audits show scorecards, source maps, screenshots, competitor context, issue priority, and a roadmap.
Fix: Use the full report as a roadmap with source map, 7-day plan, 30-day sprint, and implementation handoff.
medium gap
Current: This scan tested 25 sportswear and athletic apparel buying questions on Google Gemini (Search grounding) + OpenAI (web search) + Claude (web search) + Grok (Live Search) + Perplexica (SearXNG + self-hosted model) (1× each); the brand was named in 24 (96% AI presence).
Industry standard: Premium AI-search audits test buying prompts across multiple AI engines and track AI presence over time.
Fix: Run across more AI engines and add monthly tracking to watch AI presence move.
medium gap
Current: The current scan now carries dated visual proof from the public storefront and/or social surfaces.
Industry standard: Premium audits include screenshots, page examples, crawl proof, and exact locations of the issue.
Fix: Keep expanding visual proof toward deeper mobile/product/social states so every major claim points to a real dated surface.
high gap
Current: The scan can now judge part of the public social-to-sale path, but the lane still needs stronger profile proof and organic-source depth.
Industry standard: Strong founder-facing audits judge the real commerce system buyers use: profile clarity, proof density, link path, and the organic surfaces feeding trust.
Fix: Carry public profile captures, proof-layer checks, and organic-source movement into the audit so Instagram/WhatsApp-first brands are judged on the system they actually sell through.
high gap
Current: The audit has a service ladder, but the fix ownership and sprint path need stronger packaging.
Industry standard: Implementation-led agencies convert issues into owners, timelines, deliverables, and progress reports.
Fix: Name the offers, list deliverables, and show what changes in 7, 30, and 90 days.
high gap
Current: The audit now includes Lighthouse and accessibility checks, but only when optional browser auditing is enabled.
Industry standard: Premium audits measure speed, accessibility, and technical SEO directly instead of inferring page quality from HTML alone.
Fix: Keep browser-based quality audits enabled for sample, premium, or operator-reviewed scans.
Recommended next move
Add product schema and tighten the product detail surfaces first. This is the cleanest way to improve machine readability without changing the brand story.
Action roadmap
Days 1-7
Make the brand easier to verify before new traffic arrives.
Days 8-30
Increase entity confidence across Google and AI answer surfaces.
Days 31-90
Turn the audit into a recurring visibility and conversion operating loop.
Unlocked report
On Google Gemini (Search grounding) + OpenAI (web search) + Claude (web search) + Grok (Live Search) + Perplexica (SearXNG + self-hosted model) (1× each), Nike was named in 24 of 25 sportswear and athletic apparel buying questions (96% AI presence). Here is each question you lose — and exactly who AI recommends instead.
101 brands were named ahead of or alongside Nike across your category's buying questions. The more often a rival appears, the more of your demand AI is routing to them.
The brand's social surfaces are complete enough to judge the full discovery path. Focus on conversion and proof density. This lane exists so the audit can judge the public social-to-organic buying path, not only the website and AI answer layer.
Nike scores 63/100. Work these top-down — they are ordered by likely commercial drag, not SEO convention.
Do not create a Google Business Profile for a pure online-only brand. Build entity trust through website schema, marketplace profiles, social proof, press, review surfaces, and consistent contact details instead.
The website should become the source of truth for product, proof, policy, contact, and schema signals.
For Indian D2C, discovery often happens on Instagram and conversion happens through WhatsApp or DMs. The scan checks whether that transition is obvious and follow-up ready.
The highest-leverage automation is a trust-and-follow-up loop: capture inquiry, answer objections, request reviews, and update weekly visibility score.
This is a practical recovery path, not a guaranteed revenue claim.
Social + organic traffic
The public discovery-to-buying path
The brand's social surfaces are complete enough to judge the full discovery path. Focus on conversion and proof density.
Website visual proof
We captured homepage plus deeper commerce views, including mobile, so the audit can point to real storefront friction instead of talking abstractly.
Next: Use the captured homepage and commerce views to tighten above-the-fold category clarity, CTA visibility, proof, and buying flow.
Instagram profile clarity
The Instagram bio states the product category and includes a buying path — a strong foundation for social discovery.
Next: Keep the bio tight — each word competes with the scroll. Test one clear category + one CTA.
CTA & link-in-bio quality
The Instagram bio directs visitors toward a next step, whether a link, DM prompt, or website click.
Next: Test link-in-bio tools (Linktree, Beacons, own landing page) that route visitors to the right product without friction.
Local search visibility
No Google Business Profile detected in available search results. Local discovery relies entirely on the website and social surfaces.
Next: Create or claim the Google Business Profile listing. It is free and directly feeds local AI answers and map results.
DM or WhatsApp handoff
A public buying handoff exists from the social or website surface.
Next: Tighten saved replies and objection-handling so the handoff converts, not just exists.
Organic proof trail
69 cited domains or third-party mentions are feeding the brand's discoverability footprint.
Next: Strengthen the best-performing organic surfaces with category copy, proof, and consistent entity naming.
Social proof density
A testimonial or review surface exists, but the scan could not verify a published count.
Next: Move the strongest proof closer to product, DM, and WhatsApp conversion moments.