GEO Audit Checklist for E-commerce (50 AI-Friendly Tests)

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TL;DR: This post gives you a practical, prioritized GEO audit checklist for e-commerce (50 tests) that focuses on product pages, images, structured data, review signals, and a fast remediation sprint you can run this week. Use the checklist to make product pages extractable, cite-worthy, and AI-friendly for ChatGPT, Gemini, Perplexity, and other generative discovery channels.

Why E-commerce need a GEO audit?

AI discovery (generative search) doesn’t list links — it pulls answers from sources it can parse and trust. That makes product pages vulnerable to being invisible even if they rank well in traditional search. A GEO audit checks whether your product pages are machine-readable, authoritative, and extractable so generative engines can cite them. Start with technical crawlability, then product-level structure (short answers, specs, schema), and finally review/retail signals.

What you’ll get in this post

      1. A 50-point GEO audit checklist specifically tuned to e-commerce product pages.
      2. A prioritization matrix + 2-week remediation sprint you can run now.
      3. Micro-insights, warnings, and a downloadable lead magnet idea: E-commerce GEO Audit Google Sheet (50 points) + remediation sprints.

How to use this GEO audit checklist

  • Pick the top 10 revenue product pages → run quick checks (1–12 below).
      1. Mark each test Pass / Fail / Partial in a sheet.
      2. Prioritize quick wins (low effort, high impact). Implement across 2 weeks.
      3. Track citations/mentions with visibility tools and prompt testing.

Site & homepage (site-level GEO audit checklist)

  • llms.txt / policy check — do you allow LLM bots or explicitly block them? (Experiment cautiously).
  • robots.txt & GPTBot access — confirm AI crawlers aren’t accidentally blocked.
  • HTTPS + canonicalization — canonical headers present, no conflicting canonical URLs.
  • Sitemap includes product feed(s) (XML + merchant feed).
  • Organization schema (logo, sameAs, contact) present on the site root.
  • Clear short brand summary on homepage (1–2 sentences extractable for AI).
  • FAQ hub or sitewide Qs — centralized, schema-marked FAQ pages.
  • Core Web Vitals & mobile readiness — especially CLS and LCP for product images.

(Why these matter: site signals and access determine whether LLMs can even see your content.) 

Homepage & category checks (category-level GEO audit checklist)

      1. Category pages include a 50–80-word “what this category is for”—AI-friendly summary.
      2. Category H1 + short answer in the first 100 words.
      3. Breadcrumb schema and pagination markup on category lists.
      4. Category page uses structured product lists (schema or microdata) for top SKUs.
      5. Internal linking from category → top product pages (anchor text = intent phrasing).
      6. Category metadata (meta description + OG) that matches buyer intent.
      7. Canonical + parameter handling for filter pages.

Product page checklist (short answer summary, specs, structured data, images)

Product pages are where the sale happens — make them extractable:

  • Short answer summary (50–70 words) at the top: what it is, who it’s for, one key spec — AI can quote this directly.
  • Unique, human product description — not just manufacturer copy; add micro-use cases.
  • HTML spec table (consistent rows: weight, dims, color, materials). Tables are highly extractable.
  • Product JSON-LD (Product + Offers) is present and valid.
  • Price + availability in schema (offers, priceCurrency).
  • SKU + GTIN + MPN fields filled (search engines & retail feeds use them).
  • Short FAQ (3–7 Qs) per product using FAQ schema.
  • Shipping & returns snippet — short, clear, and machine-readable.
  • Comparison bullets (vs similar models) in a uniform format — easy to extract.
  • “Best for” one-line to aid intent matching (e.g., “Best for budget home cooks”).
  • Stock/availability structured signal (InStock, PreOrder).
  • User Q&A schema if you host product Q&A.
  • Prominent CTA and conversion signals (so AI can infer commercial intent).
  • Concise, summarizable paragraph near the top for snippet extraction.
  • Variant & attribute clarity (color, size) in markup and HTML so LLMs parse variants.

Visual & attribute data (images & media)

  • Alt text including attribute + SKU (e.g., “Men’s blue running shoe – SKU 9876”).
  • Image captions with spec highlights for the main hero image.
  • Image file names include product slug or SKU (avoid random names).
  • Responsive images (srcset) + WEBP + CDN for performance.
  • Image structured data (ImageObject) inside JSON-LD for key images.
  • All images are indexable & not blocked by lazy-load misconfig (render and test).

Structured data & metadata checklist

  • JSON-LD approach for Article/Product/FAQ/Breadcrumb/Organization. (Prefer JSON-LD over microdata).
  • AggregateRating + reviewCount populated when available.
  • FAQ schema for product Qs (3–7 short Q/A pairs).
  • BreadcrumbList schema matching visible breadcrumbs.
  • Organization + sameAs links to major profiles.
  • Offer valid until for promotions to avoid stale price pull issues.
  • Variant structure (isVariantOf) is properly modeled for SKUs.

Retail-media & reviews signals

  • Review diversity: mix of site reviews + third-party (Google, Trustpilot).
  • Verified buyer flag or markups in reviews where possible.
  • Review schema passes Rich Results test (use Google/Schema validators).
  • Retail feed parity: product feed (Google Merchant/Shopify/Marketplace) matches site: price, availability, GTIN.

Tech, crawlability & detection tests

  • GPTBot / relevant LLM user-agent crawl test — simulate or check logs.
  • Page speed (LCP, FID/INP, CLS) — images and JS-heavy pages penalize extraction.
  • Server headers & rendering: structured data appears after JS render? Use server-side JSON-LD or prerender where needed.

Prioritization matrix + 2-week remediation sprint (step-by-step)

Prioritize: top 10 product pages by revenue/traffic.

Sprint Week 1 (Quick wins):

      • Day 1: Add a 50–70-word short answer to the top 10 pages (items 16 & 29).
      • Day 2–3: Add HTML spec tables (18) + consistent SKU/GTIN fields (21).
      • Day 4–5: Add JSON-LD Product + Offers + FAQ schema for those 10 pages (19, 22, 37).

Sprint Week 2 (Medium wins):

      • Week: Update image alt/file names + implement srcset (31–35).
      • Finish: Ensure feeds match (47), run Rich Results test (46), and set tracking.

Measure: Use prompt tests and visibility tools (Nozzle / SearchEye / GenRank) to detect early mentions or paraphrases; track on a weekly cadence.

Micro-insights & warnings 

      • Micro-insight: A 50–70 word summary written as an answer to the explicit buyer question (“What is X and who should buy it?”) is often enough for LLMs to quote.
      • Warning: llms.txt is experimental — use it only if you understand tradeoffs; some providers treat it inconsistently.
      • Micro-insight: Tables and bullet lists are more likely to be paraphrased or copied into answers than long prose. Use them for specs.
      • Warning: Don’t duplicate manufacturer specs blindly—add short, actionable use cases and UGC to increase citation value.
Example JSON-LD snippet (product + offers + FAQ) — paste into head

 

{
“@context”:”https://schema.org”,
“@type”:”Product”,
“name”:”Acme Running Shoe — Model X”,
“sku”:”ACX-2025″,
“gtin13″:”0123456789012”,
“image”:[“https://cdn.example.com/acx-hero.webp”],
“description”:”A lightweight running shoe best for daily jogs — breathable mesh, 8mm drop.”,
“offers”:{
“@type”:”Offer”,
“price”:”79.99″,
“priceCurrency”:”USD”,
“availability”:”https://schema.org/InStock”,
“url”:”https://example.com/product/acx”
},
“aggregateRating”:{
“@type”:”AggregateRating”,
“ratingValue”:”4.5″,
“reviewCount”:”123″
}
}

FAQs

  • What is a GEO audit checklist?
    A systemized list of checks that make content machine-readable and cite-worthy for generative engines.
  • Will GEO optimization hurt SEO?
    No — most GEO best practices (structure, schema, clarity) help both humans and search engines.
  • How often should I run a GEO audit?
    Every 3–6 months or after major model/search UX updates.
  • Do I need llms.txt?
    It’s experimental. Only use it if you understand provider adoption and risk.
  • Which pages should I prioritize?
    Top revenue/product pages, high-traffic category pages, and pages that already rank in snippets.
  • Does product schema make ChatGPT cite my pages?
    Schema makes content easier to parse — it increases chances but doesn’t guarantee citation. Track with prompt tests and tools.
  • How do I know if AI is using my content?
    Use prompt testing and monitoring tools (Nozzle, SearchEye, GenRank) to detect paraphrases/mentions.
  • What quick wins produce the fastest results?
    Add a short answer (50–70 words), spec table, and JSON-LD to your top product pages first.

 

Conclusion — next steps (concise)

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