How ecommerce brands can improve visibility across ChatGPT, Google AI Mode, Gemini, Perplexity, Copilot and the next generation of AI shopping engines.

Search is changing.

For years, ecommerce SEO was largely about getting your product, collection or content pages as high as possible in Google’s traditional search results.

That still matters.

But consumers are increasingly skipping the traditional list of links and asking AI assistants complete buying questions instead:

• “What’s the best protein powder for women that actually tastes good?”
• “Find me a skincare routine for sensitive skin under $100.”
• “What are the best running shoes for wide feet under $150?”
• “Compare these three products and tell me which one I should buy.”

Instead of returning ten blue links, tools like ChatGPT, Gemini, Perplexity and Google’s AI Mode can research the question, evaluate multiple sources, compare products and recommend a handful of winners.

For an ecommerce business, that creates an entirely new battleground.

At SAMA Labs, we call this AI Search Optimization — sometimes referred to as Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), or AI SEO.

The terminology may continue to evolve, but the objective is simple:

Make your brand easy for AI systems to find, understand, verify, compare and confidently recommend.

Google itself says its generative search experiences remain rooted in its core Search ranking and quality systems, while using techniques such as retrieval-augmented generation and query fan-out to gather information across multiple related searches.

Here are the 10 areas we believe ecommerce brands should prioritize right now.

1. Make Sure AI and Search Engines Can Actually Access Your Website

Before worrying about content, citations or AI recommendations, start with the foundation.

Your products need to be discoverable.

That means reviewing:

• Robots.txt
• Search engine crawler access
• CDN and firewall rules
• Indexability
• XML sitemaps
• Canonical URLs
• Internal links
• JavaScript rendering
• Mobile usability
• Page performance
• Broken URLs and redirects

Google confirms that pages generally need to be indexed and eligible for Search before they can appear in its generative search experiences. It also recommends ensuring crawling is allowed and that important content is available in textual form.

For brands targeting ChatGPT visibility, crawler accessibility matters there as well.

This sounds basic, but during an AI Visibility Audit, SAMA Labs frequently finds that brands are trying to improve AI visibility before confirming whether machines can properly access and interpret the site in the first place.

AI SEO starts with machine accessibility.

2. Give AI More Product Data to Work With

This may be the single biggest ecommerce-specific opportunity.

A traditional product page might contain:

Product name + image + price + short description.

That’s often not enough for an AI assistant trying to determine whether your product satisfies a complicated buying request.

Think about a prompt such as:

“Find me a fragrance-free moisturizer under $60 for sensitive skin that’s cruelty-free and doesn’t contain retinol.”

The AI needs to determine:

• Product category
• Price
• Ingredients
• Skin type
• Fragrance status
• Certifications
• Use cases
• Exclusions
• Availability
• Reviews

The more relevant product attributes you clearly expose, the greater the number of specific queries your product can potentially satisfy.

For ecommerce brands, improve product detail pages with information such as:

• Materials
• Ingredients
• Dimensions
• Sizes
• Compatibility
• Intended audience
• Use cases
• Certifications
• Variants
• Technical specifications
• Origin
• Manufacturing information
• What is included
• What is excluded
• Key differentiators

OpenAI says ChatGPT shopping can use merchant and product metadata, public product information, reviews and other retail sources when helping users compare products. Shopify merchant product data is also integrated into ChatGPT through Shopify Catalog.

Your product feed is no longer just ad infrastructure. It is becoming AI search infrastructure.

3. Optimize Your Merchant Feeds

For ecommerce brands, website content and product feeds should work together.

Google explicitly recommends keeping Merchant Center information up to date and says Merchant Center feeds can help products appear in both traditional and generative search experiences.

Your feed should provide clean, accurate information around:

• Product titles
• Descriptions
• GTINs
• Brand
• Price
• Availability
• Product category
• Images
• Variants
• Shipping
• Promotions
• Condition
• Product identifiers

And critically:

Your data should agree everywhere.

If your website says:

$49.99

but your feed says:

$59.99

and your structured data says:

Out of Stock

while the PDP says:

In Stock

you are creating conflicting information.

AI systems increasingly need to make decisions from multiple sources.

Make those sources agree.

4. Use Structured Data Correctly — But Don’t Treat Schema Like a Magic GEO Hack

Schema is important.

It helps search engines explicitly identify things such as:

• Products
• Offers
• Organizations
• Reviews
• Ratings
• Variants
• Shipping
• Return policies

For ecommerce businesses, common useful structured data can include:

Product
Offer
Organization
Review
AggregateRating
BreadcrumbList
MerchantReturnPolicy
ProductGroup

But there is an important distinction.

Google specifically says there is no special schema required to appear in AI Overviews or AI Mode. Structured data should support your overall SEO infrastructure and accurately represent the visible content of the website — not act as a shortcut for AI rankings.

At SAMA Labs, we treat schema as a machine-clarity layer, not a magic ranking button.

Make the underlying information excellent first.

Then make it easier for machines to interpret.

5. Stop Publishing Commodity Content

This is one of the clearest messages coming directly from Google.

Publishing another article titled:

“10 Benefits of Collagen”

probably doesn’t give the internet much information it doesn’t already have.

Google’s current generative search guidance specifically recommends unique, non-commodity content based on experience, expertise and original points of view rather than simply recycling information already available elsewhere.

For ecommerce brands, think about content only your company could realistically create.

Instead of:

What Is Sea Salt?

consider:

We Lab Tested Our Mineral Salt — Here’s What the Analysis Found

Instead of:

5 Benefits of Running Shoes

consider:

We Compared Cushioning, Weight and Toe Box Width Across 12 Popular Running Shoes

Create:

• Original tests
• Customer research
• Surveys
• Laboratory reports
• Product demonstrations
• Expert interviews
• Founder insights
• Benchmark data
• Manufacturing breakdowns
• Case studies
• Firsthand product comparisons

Ask one question before publishing:

What new information does this add to the internet?

That’s a much better AI content strategy than simply publishing more articles.

6. Build Content Around Buying Decisions, Not Just Keywords

People don’t speak to AI assistants the same way they traditionally searched Google.

A traditional search might be:

“best mineral salt”

An AI query might be:

“What’s the best minimally processed sea salt with naturally occurring minerals that isn’t Himalayan pink salt?”

That distinction matters.

Google has publicly explained that AI Mode and AI Overviews can use query fan-out, issuing multiple related searches to gather information across different parts of a complex question.

You should therefore understand the entire query universe surrounding your product.

Research questions involving:

• Best products
• Best for a specific audience
• Best under a certain price
• Alternatives
• Comparisons
• Ingredients
• Materials
• Problems
• Use cases
• Reviews
• Compatibility
• Safety
• Objections
• Specifications
• Availability
• Where to buy

But don’t create 300 nearly identical pages for 300 questions.

Google explicitly warns against scaled content created simply to target every possible long-tail or fan-out variation.

Instead, build comprehensive resources that satisfy clusters of related buying intent.

7. Create More Comparison and “Best For” Content

AI assistants are particularly useful when consumers need help evaluating options.

ChatGPT’s shopping research experience, for example, is specifically designed to evaluate products against preferences, constraints and tradeoffs, and can present users with recommended products and side-by-side comparisons.

That creates a significant opportunity for ecommerce brands.

Develop content such as:

Product A vs. Product B

Brand A vs. Brand B

Best Product for X

Product A vs. the Category

X vs. Y: Which Is Better For You?

Alternatives to X

And make the comparison genuinely useful.

Include:

• Price
• Materials
• Ingredients
• Specifications
• Pros
• Cons
• Best use case
• Audience
• Limitations
• Certifications
• Reviews
• Key differences

Don’t claim your product is perfect for everyone.

Explain who it is best for and when another product might make more sense.

That gives an AI assistant exactly what it needs to resolve a recommendation.

8. Strengthen Your Brand as an Entity Across the Web

Your website shouldn’t be the only place explaining who you are.

AI systems can retrieve information from across the web when researching brands and products.

That means you want consistency surrounding:

Brand > Category > Product > Attributes > Audience > Use Case

If you manufacture premium hiking backpacks, the internet should consistently associate your company with:

hiking
backpacks
outdoor equipment
durability
specific materials
relevant use cases

Look at:

• Review platforms
• Publications
• YouTube
• Reddit
• Podcasts
• Industry websites
• Retailers
• Marketplaces
• Directories
• Expert reviews
• Interviews

This does not mean manufacturing fake mentions.

Google specifically warns against pursuing inauthentic mentions purely to manipulate generative results.

The objective is genuine third-party corroboration.

At SAMA Labs, we think of this less as traditional backlink building and more as:

Building enough independent evidence for machines to confidently understand what your brand is known for.

9. Turn Reviews Into Product Intelligence

Reviews are no longer just conversion-rate optimization.

They’re product data.

A strong review ecosystem can help establish:

• What customers use the product for
• Who buys it
• What customers compare it against
• What they like
• What they dislike
• Common product attributes
• Recurring use cases

OpenAI says ChatGPT can use reviews from public websites when creating product summaries and surfacing common likes and dislikes.

Instead of simply asking customers:

“Leave us a review.”

ask better questions:

Why did you choose this product?

What were you using before?

How are you using it?

What feature mattered most?

Who would you recommend it to?

This produces richer customer language that can then inform:

• Product descriptions
• FAQs
• Buying guides
• Advertising
• Comparison content
• New product development

Your customers may be telling AI exactly why it should recommend you.

Make sure that information is accessible.

10. Measure AI Recommendations, Not Just Google Rankings

You can’t improve what you don’t measure.

Traditional SEO tracking typically asks:

Where do we rank for this keyword?

AI visibility requires another layer.

Create a fixed group of real buying prompts and test them repeatedly across:

• ChatGPT
• Gemini
• Google AI Mode / AI Overviews
• Perplexity
• Microsoft Copilot
• Other emerging AI shopping experiences

Track:

Brand Mention Rate

How often does your brand appear?

Recommendation Rate

How often does AI actively recommend you?

Citation Rate

How often is your website used as supporting evidence?

AI Share of Voice

How often do you appear compared with competitors?

Recommendation Position

Are you the first recommendation or an afterthought?

Attribute Association

What does AI believe your brand is known for?

AI Referral Traffic

How much traffic arrives from AI platforms?

AI Revenue

How much of that traffic actually converts?

This is how SAMA Labs evaluates AI visibility for ecommerce clients.

You can see examples of the dashboards, measurement methodology and actual client outcomes in our AI Search Results & Case Studies.

Our process triangulates AI visibility using recurring prompt tracking, GA4 referral attribution and ecommerce platform data rather than relying on a single vanity metric. Recent SAMA Labs client examples include brands moving from little or no measurable AI presence to meaningful assistant mentions, referral traffic and tracked ecommerce revenue.

AI SEO Isn’t Replacing SEO — It’s Expanding It

One of the biggest misconceptions around GEO is that traditional SEO no longer matters.

That’s wrong.

Google explicitly states that its generative AI search experiences remain grounded in its traditional Search infrastructure and that existing SEO best practices continue to matter.

You still need:

• Crawlability
• Useful content
• Internal linking
• Authority
• Strong technical architecture
• Product data
• Merchant Center
• Great user experience
• Accurate information

What’s changing is the end goal.

Traditional SEO asks:

Can this page rank?

AI Search Optimization adds:

Can this brand be understood, verified and recommended?

For ecommerce businesses, that distinction is enormous.

What Ecommerce Brands Should Focus on First

If you’re starting from scratch, don’t try to tackle everything at once.

Start in this order:

1. Technical accessibility
2. Product and feed data
3. Entity clarity
4. Product-page depth
5. Unique non-commodity content
6. Comparison and buying content
7. Reviews and evidence
8. Third-party authority
9. AI prompt monitoring
10. Continuous iteration

At SAMA Labs, our AI Search Optimization framework audits these layers together because they influence one another.

Better content won’t help much if machines can’t access your site.

Perfect schema won’t make generic products suddenly recommendable.

And a great website alone may not be enough if every trusted third-party source recommends your competitors.

The objective is to improve the entire information ecosystem surrounding your brand.

The Goal: Become the Brand AI Can Confidently Recommend

The future of ecommerce search isn’t simply about getting someone to click your blue link.

The buying decision can begin — and increasingly be narrowed down — inside an AI conversation.

The winning brands will make it incredibly easy for those systems to answer five questions:

What is this product?

Who is it for?

Why is it different?

Can I verify those claims?

Should I recommend it to this particular shopper?

That’s the foundation of ecommerce AI SEO and GEO.

And we’re still early.

If you want to understand how ChatGPT, Gemini, Perplexity, Copilot and Google’s AI experiences currently see your ecommerce brand, explore SAMA Labs AI Search Optimization.

For real examples of how we measure mentions, referral traffic and ecommerce revenue, visit the SAMA Labs AI Search Results & Case Studies.

SAMA Labs helps ecommerce brands become easier for AI systems to find, understand, cite and recommend.