AI Search Checklist for Ecommerce: 95-point list to get your products recommended by AI

AI search is now sending real traffic to ecommerce stores. And that traffic converts at 10-15% compared to under 2% for regular Google organic.

ChatGPT processes 50 million shopping-related queries every single day. AI traffic to ecommerce sites surged 1,300% during the 2024 holiday season. And yet only 16% of brands are doing anything about it.

I’ve spent the last few months researching what actually works for getting ecommerce stores recommended by AI search engines. I’ve pulled together findings from the Princeton GEO study, Ahrefs’ 17-million-citation dataset, Semrush’s 10-million-keyword AI Overviews study, and SE Ranking’s analysis of 129,000 domains to build the most comprehensive AI search checklist for ecommerce stores I’ve seen.

This covers everything you need across ChatGPT, Perplexity, Google AI Overviews, Gemini, and Microsoft Copilot.

Your AI search optimisation strategy needs to cover several key areas:

Crawler access – Making sure AI bots can actually reach and read your product pages. If they can’t crawl you, you don’t exist.
Technical SEO for AI – Most AI crawlers can’t execute JavaScript. If your product data loads client-side, it’s invisible.
Structured data – The foundational layer for AI recommendations. Incomplete data means AI picks your competitor instead.
Product & category pages – Writing content that answers the questions AI is being asked, not marketing waffle.
Content strategy – Creating the buying guides and comparison content that AI actually cites.
Product feeds – Your feed is becoming more valuable than your homepage for AI commerce.
Brand building – In AI search, brand mentions are the new backlinks.
Reviews & UGC – Reddit is the most-cited website across all LLMs. YouTube is #1-2 for Perplexity and Gemini. Are you there?
Digital PR – Getting featured in authoritative “Best X” lists is the single most impactful off-site tactic.
Measurement – Setting up proper tracking before you start so you can actually see what’s working.

1. Crawler Access & Robots.txt

AI search engines use different crawlers to your site, and blocking the wrong one can make your store completely invisible. OpenAI alone runs three separate bots: GPTBot for model training, OAI-SearchBot for ChatGPT search citations, and ChatGPT-User for real-time browsing. Perplexity, Google, and Microsoft all have their own too.

The critical thing to understand here is that blocking Bingbot is catastrophic. It doesn’t just remove you from Bing. It eliminates you from Copilot, ChatGPT Browse, DuckDuckGo, and Ecosia simultaneously. One robots.txt line, five search engines gone.

Amazon has blocked all OpenAI crawlers, which is actually a massive opportunity for other retailers to capture ChatGPT Shopping traffic. Make sure you’re not accidentally doing the same thing.

Crawler access checklist

Allow OAI-SearchBot in robots.txt to appear in ChatGPT search results and shopping features
Allow PerplexityBot to be indexed in Perplexity’s answer engine and shopping features
Confirm Bingbot is allowed – it powers Copilot, ChatGPT browsing, and multiple partner engines
Confirm Googlebot is allowed – it feeds both traditional search and AI Overviews
Make a conscious decision on GPTBot (training) and Google-Extended (Gemini training) – these are optional and separate from search visibility
Audit your CDN and WAF settings (Cloudflare, AWS, etc.) to confirm AI crawlers aren’t being blocked at the firewall level
Test crawler access by running curl -A "GPTBot" on your top product pages and verifying full content is returned
Consider publishing an llms.txt file – an emerging standard for controlling how AI systems access your content

2. Technical SEO for AI Crawlers

The single most damaging technical issue for ecommerce AI visibility is JavaScript rendering. GPTBot, PerplexityBot, ClaudeBot, and most AI crawlers have zero JavaScript execution capability. A Vercel/MERJ analysis of 569 million GPTBot requests confirmed this.

Think about what that means for your store. If your product names, prices, descriptions, and images load via client-side JavaScript (React, Vue, Angular SPAs), they are completely invisible to every AI search engine except Google. That beautifully rendered product page? It’s a blank shell to ChatGPT and Perplexity.

AI crawlers also enforce tight timeouts of 1-5 seconds for page retrieval. Slow ecommerce sites with heavy imagery and unoptimised code get dropped entirely.

Technical SEO checklist

Implement server-side rendering (SSR) or dynamic rendering (e.g. Prerender.io) so AI bots receive pre-rendered HTML
Verify all product info (name, price, description, availability, images) exists in initial HTML source, not loaded via JavaScript
Achieve page load times under 2.5 seconds for product and category pages – AI crawlers enforce 1-5 second timeouts
Submit XML sitemaps to both Google Search Console and Bing Webmaster Tools with accurate lastmod values
Implement the IndexNow protocol for Bing to enable real-time URL submission when products or prices change
Set clean canonical tags on every page and eliminate redirect chains and fix 404 errors
Use clean, descriptive URL structures (/category/product-name) and avoid parameter-heavy faceted navigation URLs
Remove nosnippet meta robots tags from any page you want AI engines to cite

3. Structured Data & Schema Markup

Structured data is the foundational layer for AI search visibility. Google’s Shopping Graph processes 50 billion product listings refreshed 2 billion times per hour. Products with comprehensive schema appear in AI shopping recommendations significantly more often than those without.

The key principle here is what I think of as the “confidence threshold”. AI agents operate on confidence scores. A product with explicit attributes (care instructions: “machine washable”, style: “modern”, explicit price) might score 95% confidence and rank first. A product with vague descriptions (“easy to clean” instead of “machine washable”) might score 62% and get excluded.

The difference between being the first recommendation and the eighth is often data completeness, not product quality. That’s a hard pill to swallow but it’s the reality.

Structured data checklist

Implement Product schema (JSON-LD) on every product page: name, description, image, brand, sku, gtin/mpn, color, size, material, weight
Nest Offer schema within Product: price, priceCurrency, availability, priceValidUntil, shippingDetails, hasMerchantReturnPolicy
Add AggregateRating schema with ratingValue, bestRating, ratingCount, reviewCount and mark up individual Reviews
Implement FAQPage schema on product pages using real customer questions from support tickets and People Also Ask results
Add BreadcrumbList schema sitewide to clarify hierarchical relationships
Set up Organization schema on homepage with sameAs links to Wikipedia, Wikidata, LinkedIn, Crunchbase, and social profiles
Implement HowTo schema on product usage guides, assembly instructions, care guides, and tutorials
Add ItemList schema to category pages and include ImageObject schema for hero product images
Validate all schema using Google Rich Results Test and Schema.org validator and set up automated periodic validation
Ensure schema values match visible on-page content exactly – when schema says £99 but the page shows £119, AI engines flag the data as unreliable

4. Product Page Optimisation

AI engines don’t browse product pages like humans do. They parse content in a specific order: title and description first, then explicit attributes (size, colour, material), categorical data, supplementary fields (Q&A, compatibility, certifications), and finally contextual data (reviews, usage scenarios).

If a field is empty, AI doesn’t guess. It marks it as “unknown” and moves to a competitor whose data is complete.

Write constraint-based copy rather than marketing waffle. Don’t say “high-quality construction” – say “1200-thread-count Egyptian cotton” or “20V brushless motor with 400 in-lbs torque”. Think about the questions AI is being asked: “Is this waterproof?” “Will this fit a 30-inch window?” Answer them explicitly on the page.

Product page checklist

Write descriptions that explicitly answer specific constraints: dimensions, compatibility, care instructions, materials, weight, certifications, use cases
Include a dedicated FAQ section on every product page with 5-10 real customer questions and concise answers (50-100 words each)
Add “Who is this for?” and “Who is this NOT for?” sections to help AI match products to specific user needs
Define product variants clearly (size, colour, configuration) with proper variant schema to prevent AI merging or splitting errors
Structure content with clear heading hierarchy (H1 > H2 > H3), comparison tables, and scannable feature lists
Include product comparison information where relevant – side-by-side specs with competing products or other products in your range

5. Category Page Optimisation

Well-optimised category pages are being cited in AI summaries even when they don’t rank first organically. The key is transforming thin category pages into entity-rich, intent-addressing content hubs.

A weak category intro like “We stock a wide range of door handles to suit any home” tells AI nothing useful. Something like “Our door handle range includes lever handles, door knobs, and pull handles in finishes such as brass, stainless steel, and iron. Whether you’re updating an interior door, fitting a fire door, or finishing a commercial unit, you’ll find durable options from trusted brands such as Carlisle Brass and From the Anvil” gives AI the entities, intent signals, and structured context it needs to cite you.

If you’ve followed my SEO checklist before, you’ll know I’m a big advocate for investing in category page content. That’s even more true for AI search.

Category page checklist

Add an entity-rich intro paragraph to every category page mentioning product types, materials, finishes, brands, and key use cases
Create subsections organised by user intent: by style, by material, by use case, by price range
Add a category-level FAQ section with FAQPage schema addressing “best [product] for [use case]” questions
Include buying guide content below the product grid covering selection criteria, comparison factors, and key considerations
Target long-tail keywords with intent prefixes: “best [product]”, “types of [product]”, “compare [product]”, “[product] for [use case]”

6. Content Strategy for AI Citation

This is where it gets really interesting. The Princeton/Georgia Tech GEO study tested nine optimisation strategies across 10,000 queries and found that adding statistics improved visibility by 40%, citing sources improved it by 30-40%, and including expert quotes added another 30-40%. Traditional keyword stuffing actually performed worse than baseline (-10% on Perplexity).

The content types that dominate AI citations tell a clear story. Comparative listicles account for 32.5% of all LLM-cited sources. “Best X” list articles are the primary format AI engines pull from for shopping queries. Pages with original data tables earn 4.1x more AI citations, and content updated within the past 30 days earns 3.2x more citations than older content.

Q&A format is the single best content structure for AI search citation. And each section should work as a self-contained “answer block” of 134-167 words that makes sense even when pulled out of context.

Content strategy checklist

Create comprehensive buying guides for every major category, structured around real customer constraints with comparison tables
Build “Product A vs Product B” comparison pages with structured data tables, side-by-side specs, and winner recommendations
Publish “Best [Category] in [Year]” content and update at minimum quarterly – 80% of AI-cited articles were updated in the past 2-3 months
Include 2-3 specific statistics with sources in every content piece (Princeton GEO study: +40% visibility boost)
Open every section with a direct 1-2 sentence answer, then expand with detail. Front-load key information
Write each section as a self-contained “answer block” of 134-167 words that makes sense when extracted from context
Add visible “Last Updated” timestamps and implement dateModified in structured data – AI platforms cite content 25.7% fresher than traditional search
Create how-to and tutorial content around product usage, care, and setup with numbered steps and HowTo schema
Use conversational, natural language mirroring how customers actually ask AI questions. Avoid jargon and marketing-speak
Include expert quotes with attribution and author bylines with credentials on every content piece

7. Product Feed Optimisation

Product feeds are becoming more valuable than your entire website for AI commerce. When a customer uses Google’s AI Mode, Gemini, ChatGPT Shopping, or Perplexity’s Buy with Pro, they may never see your homepage.

Stores with near-complete attribute data see 3-4x higher visibility in AI recommendations versus stores with sparse data. And here’s a big one: free product listings in Google Merchant Center are now given priority over paid ads inside AI-generated answers. That’s right, free listings beating paid.

Multiple AI platforms now accept product feeds directly. Google Merchant Center powers AI Overviews and Gemini. The Perplexity Merchant Program accepts direct feed uploads. OpenAI has begun accepting merchant product feeds for ChatGPT Shopping. And Shopify merchants are automatically enrolled in Copilot Checkout.

Product feed checklist

Ensure Google Merchant Center feed has 95%+ attribute completion: titles, descriptions, brand, GTIN, condition, availability, price, image, product type
Write descriptive, attribute-rich product titles in feeds – not “Blue Shirt” but “Men’s Blue Oxford Button-Down Shirt – Slim Fit – 100% Cotton – Size L”
Include enrichment attributes: material_composition, care_instructions, weight, dimensions, compatible_with, warranty, performance specs
Verify price, availability, and details match exactly between feed, on-page schema, and website – mismatches reduce visibility
Apply for and submit feeds to the Perplexity Merchant Program for direct product data integration
Ensure product feed data reaches the third-party providers powering ChatGPT Shopping (OpenAI uses partners, not direct feeds)
Set up daily automated feed updates synced with inventory management – pricing and availability must reflect real-time status

8. Brand Entity Building

In AI search, brand mentions are the new backlinks. Ahrefs data shows that brands in the top 25% for web mentions earn over 10x more AI citations than brands in the next quartile. The correlation between AI visibility and branded web mentions is 0.87 – one of the strongest signals identified.

AI engines don’t just follow links. They track every mention of your brand across the web, analysing context, sentiment, and frequency to build entity understanding.

Wikidata has become the backbone of AI brand recognition. Google migrated its Knowledge Graph from Freebase to Wikidata, making it the primary structured source for 500 billion facts about 5 billion entities. Companies have gained Knowledge Panels within 7 days of creating proper Wikidata entries. Even without a Wikipedia page, a Wikidata entry gives AI a “verified anchor point” that reduces hallucination about your brand.

Brand entity checklist

Create or claim your Wikidata entry with: legal name, founding date, headquarters, industry, CEO, website, products, and QID links
Audit brand consistency across website, LinkedIn, Crunchbase, Google Business Profile, and all directories – AI downgrade confidence when they find conflicting information
Build a comprehensive “Entity Home” on your About page: history, mission, leadership bios, products, certifications, awards, contact info
Pursue editorial mentions in authoritative publications – AI engines track mentions even without clickable links
Build and maintain a Wikipedia page if your brand meets notability criteria – Wikipedia is the most-cited source across ChatGPT (7.8% of citations / 29.7% of top 1,000)

9. Reviews, UGC & Social Proof

Reviews are a primary trust signal AI engines use when deciding which brands to recommend, but each platform uses them differently. ChatGPT analyses review sentiment to generate labels like “Highly rated” and “Ideal for runners with flat feet”. Google AI Overviews leans heavily on user-generated content from Reddit and YouTube. Perplexity favours authentic community discussions and expert reviews.

Google now generates AI-powered review summaries in Chrome, pulling data from licensed review partners. This means having reviews on a Google Licensed Review Partner isn’t just good for badges – it directly feeds AI features.

And here’s a stat that should get your attention: Reddit is the most-cited single website across all LLMs at 40.11% of citations. YouTube is #1 or #2 most-cited domain across Perplexity, Gemini, and Google AI Mode. If you’re not building presence on these platforms, you’re leaving AI visibility on the table.

Reviews & UGC checklist

Use a Google Licensed Review Partner (Yotpo, Reviews.io, Bazaarvoice) that syndicates data into Google’s AI features
Maintain consistent review velocity with automated post-purchase requests – aim for new reviews weekly at minimum
Use smart review prompts guiding customers to mention specific features, use cases, and comparisons in natural language
Add product-specific Q&A sections and actively answer questions – AI tools use Q&A content to resolve product queries directly
Build authentic presence on Reddit by participating genuinely in relevant subreddits – Reddit is the #1 cited website across all LLMs
Create YouTube product content – reviews, comparisons, tutorials, demos. YouTube is the #1-2 most-cited domain across Perplexity and Gemini

10. Off-Site Authority & Digital PR

Backlinks still matter for AI search, but the dynamics have shifted. SE Ranking’s study of 129,000 domains found that referring domains is the strongest predictor of AI citations – sites with 32,000+ referring domains are 3.5x more likely to be cited by ChatGPT. However, SALT.agency research found no domain with a DR below 40 appeared in the top 25% of AI-cited domains.

The bar for entry is high, but a few links from highly authoritative, topic-relevant domains outperform hundreds of weak links.

Digital PR has become more valuable than traditional link building for AI search because AI systems synthesise information from trusted third-party sources. “Best X” list posts account for 43.8% of all pages referenced in AI responses. Getting featured on authoritative comparison lists is now the single most impactful off-site tactic you can pursue.

Off-site authority checklist

Pursue inclusion in authoritative “Best [Category]” listicles – these make up 43.8% of AI-cited pages for shopping queries
Create original research, surveys, and data studies for your category – pages with original data earn 4.1x more AI citations
Launch digital PR campaigns with newsworthy stories, data-driven content, and expert commentary
Convert unlinked brand mentions into backlinks using Ahrefs or BrandMentions
Target a diverse range of linking domains rather than volume from same sources – domain variety carries more weight for AI
Contribute expert quotes and guest content to industry publications – author bios with credentials strengthen entity signals

11. Image & Visual Content

AI-powered visual search is projected to account for 30% of all online searches by 2025, and 48% of Gen Z and millennials already prefer visual search over text. Vision-language models (CLIP, Gemini Vision) process product images through a three-step pipeline: detect objects and logos, OCR any on-image text, then fuse those signals with alt text and captions.

The results speak for themselves: HubSpot boosted image search traffic by 779% after optimising alt tags, and Foot Locker saw a 228% jump in organic traffic after fixing image indexing issues.

Image optimisation checklist

Add descriptive alt text to every product image (under 125 characters): specific colour, material, product type, distinguishing features
Use high-resolution images (min 1500x1500px) with clean backgrounds, multiple angles (front, back, sides, close-ups), plus lifestyle photos
Use descriptive file names (mens-blue-oxford-shirt-front.jpg not IMG_0001.jpg) and compress for fast loading
Include product videos with full transcripts for crawlability – video content is increasingly cited by Gemini, Perplexity, and AI Mode

12. Internal Linking & Site Architecture

Internal linking serves a different purpose for AI engines than for traditional search. Beyond distributing PageRank, internal links clarify content relationships and build the knowledge graph that AI platforms use to understand your content ecosystem.

A study by LLMVisibility found that adding just 3-5 contextually relevant internal links led to a 100-150% boost in traffic from AI search tools. That’s a massive return for what is ultimately a quick win.

The hub-and-spoke model is especially powerful for ecommerce AI search. Hubs (category overview pages, buying guides) link to all spokes (product pages, use-case guides, FAQ pages), and every spoke links back. This gives AI engines the structured context they need to cite your content confidently.

Internal linking checklist

Map content into hub-and-spoke clusters: category/buying guide as hub, product pages and use-case guides as spokes (6-8+ per hub)
Ensure every product page has 3-5 contextually relevant internal links to related products, parent category, and supporting content
Add “Related Products” with text links (not just images) using descriptive, varied anchor text including relevant keywords
Create buying guides that naturally link to product pages and ensure blog content links contextually to relevant products and categories
Audit for orphan pages (product pages with no internal links) and ensure important pages are within 1-4 clicks from homepage

13. Platform-Specific Quick Wins

Each AI platform has distinct behaviours and merchant programmes, so it’s worth understanding the nuances.

ChatGPT draws from the Bing index, not Google. If your site isn’t in Bing, it effectively doesn’t exist for ChatGPT. Perplexity runs real-time web searches using its own index plus Google and Bing APIs. Google AI Overviews and Gemini pull from the Google Search index and Shopping Graph. Copilot relies entirely on Bing.

A critical competitive insight: Amazon has blocked all OpenAI crawlers. This creates a massive opportunity for other retailers to capture ChatGPT Shopping traffic. Meanwhile, ChatGPT’s top product recommendation matches Google Shopping’s first 3 results 75% of the time, so Google Merchant Center optimisation indirectly benefits ChatGPT visibility too.

Platform-specific checklist

ChatGPT: Verify your site in Bing Webmaster Tools and confirm Bing indexing – if not in Bing, you don’t exist for ChatGPT
Perplexity: Join the Perplexity Merchant Program. Write conversationally. Build canonical pages answering 20-50 related questions on a single topic
AI Overviews: Optimise Google Merchant Center feeds – free listings get priority over paid ads inside AI answers
Gemini: Ensure flawless Merchant Center data with real-time sync. Support Google Pay and guest checkout for agentic checkout eligibility
Copilot: Shopify merchants are auto-enrolled in Copilot Checkout – confirm you haven’t opted out. Set up Microsoft Clarity

14. Measurement & Monitoring

Tracking AI search performance requires dedicated setup because AI traffic appears inconsistently in analytics. ChatGPT shows as chatgpt.com/referral for some users, but free users often don’t pass referrer data, appearing as “Direct” instead. Perplexity is the most reliable referrer. And Google AI Overview clicks can’t be separated from regular organic search traffic in GA4.

True AI traffic is estimated to be 2-3x what analytics platforms report due to referrer stripping in mobile apps and in-app browsers.

But here’s why it’s worth tracking: Seer Interactive found ChatGPT referral traffic converts at 15.9% and Perplexity at 10.5%, compared to Google organic at 1.76%. This is high-intent, high-value traffic that deserves your attention.

Measurement checklist

Create a custom channel group in GA4 for AI traffic using this regex: (chatgpt\.com|chat\.openai\.com|claude\.ai|gemini\.google\.com|perplexity\.ai|copilot\.microsoft\.com|deepseek\.com|meta\.ai|grok\.com) – drag it above the Referral channel
Set up an AI Users audience in GA4 for remarketing and conversion analysis using the same regex
Monitor which landing pages receive AI referral traffic monthly – these pages deserve priority optimisation
Subscribe to an AI visibility monitoring tool (Ahrefs Brand Radar, Semrush AI Visibility, or Otterly.ai)
Manually test your top 20 shopping queries across ChatGPT, Perplexity, Google AI Mode, and Copilot monthly
Track branded search volume trends alongside AI visibility – AI discovery often leads to subsequent branded Google searches

15. International & Multi-Market

AI search engines struggle with returning language-appropriate URLs. Glenn Gabe’s 2025 testing found that ChatGPT and Perplexity both returned US English versions even when the user’s language was set to French. Microsoft Copilot was the best performer, correctly returning localised URLs because it leverages Bing’s hreflang support.

The practical implication: your English content must be strong even for non-English markets because most AI engines may pull from any language version regardless of user location.

International checklist

Implement hreflang tags correctly for all language/region variants with proper reciprocal linking
Invest equal content depth across all language versions – don’t create thin translations; adapt for cultural context and local search behaviour
Include region-specific FAQ content mirroring how local consumers ask AI questions (e.g. “trainers” vs “sneakers” for UK vs US)
Ensure your English-language site is comprehensively optimised regardless of primary market – it serves as fallback for ChatGPT and Perplexity

The key research that backs this up

Understanding the research behind these recommendations helps you prioritise what to tackle first. Here are the most important findings from 2024-2025 studies:

The Princeton GEO study established that statistics addition (+40%), source citation (+30-40%), and expert quotes (+30-40%) are the three most impactful content optimisation tactics for AI search visibility. Traditional keyword stuffing actively hurts performance.

Ahrefs’ analysis of 17 million citations found that AI platforms cite content 25.7% fresher than traditional search, with ChatGPT showing the strongest recency bias at 76.4% of most-cited pages updated within 30 days.

SE Ranking’s 129,000-domain study confirmed that referring domain count is the strongest predictor of AI citations, with a 3.5x citation likelihood for high-authority sites.

The September 2025 Chen et al. study demonstrated that AI search shows systematic bias toward earned media (third-party editorial sources) over brand-owned content. That’s a fundamental difference from traditional Google search, where your own site ranks directly.

For ecommerce specifically: ecommerce pages are 5.10x more likely to be cited by AI than the average content type. AI shopping traffic converts at rates between 10.5% and 15.9% versus 1.76% for Google organic. And with Amazon blocking all OpenAI crawlers, non-Amazon retailers have a rare window of competitive advantage.

Where to start

The most impactful actions come down to three interconnected pillars.

First, data completeness. Comprehensive structured data, complete product feeds, and explicit product attributes drive AI confidence scores. This is the single largest quick win for most ecommerce stores. Start with sections 1-3.

Second, citation-worthy content. Buying guides, comparison pages, and FAQ content structured in answer-first, self-contained blocks with statistics and expert attribution. This earns dramatically more AI citations than product pages alone. Work through sections 4-7.

Third, distributed brand authority. Building brand mentions across authoritative third-party sites, review platforms, Reddit, YouTube, and editorial publications. Brands in the top quartile for web mentions earn 10x more AI citations. Build this over time with sections 8-10.

The compounding effect is what makes early action so valuable. Once AI starts citing your brand, familiarity compounds into more citations, greater visibility, and stronger brand gravity. Stores that build this foundation now – while only 16% of brands are systematically optimising for AI search – will be difficult for competitors to displace later.

Download the full checklist

You can get this entire checklist as a spreadsheet – with priority ratings, difficulty levels, platform tags, and source links for every single item.