UK Ecommerce AI Search Visibility Study 2026

The short answer

AI search is creating a new layer of ecommerce discovery.

Consumers can now ask AI assistants which brands to consider, which products are best for a particular need and what they should buy without necessarily beginning their journey on a traditional search engine or retailer website.

But appearing in these answers is far from guaranteed.

IgniteStack analysed ecommerce visibility across high-intent product discovery, recommendation and comparison queries to understand which brands are being surfaced by AI, how large the visibility gap can become and which sources appear to influence those recommendations.

Our core benchmark covers:

• 141 tracked ecommerce prompts
• 828 AI-generated responses
• 3 detailed UK ecommerce category datasets
• Dozens of competing brands analysed
• Categories spanning fashion, food and baby products
• Supporting ecommerce snapshot data across beauty, skincare, cosmetics and additional consumer categories

The result is clear:

AI visibility is already highly concentrated. Two ecommerce brands competing for broadly comparable consumer attention can have radically different levels of exposure in AI-generated recommendations.

Across the core benchmark, brand mention rates ranged from 2.4% to 46.1%.

That is a 19x visibility gap.

Key findings

1. AI visibility varies by as much as 19x between ecommerce brands

Across our three detailed ecommerce datasets:

• Ecommerce brand: Bibado
  Category: Baby feeding
  AI mention rate: 46.1%
  Owned citation rate: 14.5%
• Ecommerce brand: Rollagranola
  Category: Food & breakfast
  AI mention rate: 15.0%
  Owned citation rate: 6.7%
• Ecommerce brand: Queens of Archive
  Category: Fashion
  AI mention rate: 2.4%
  Owned citation rate: 0%

The difference between the highest and lowest visibility rate is approximately 19.2x.

This matters because AI visibility is not simply a question of whether a model "knows" a brand.

A brand can have a functioning ecommerce website, established products, existing customers and conventional search visibility yet remain almost absent when consumers use recommendation-led AI queries.

The commercial challenge is increasingly becoming:

When an AI system is asked what someone should buy, does your brand enter the consideration set?

For some brands the answer is frequently yes.

For others it is almost always no.

2. Category leadership in traditional commerce does not automatically translate to AI leadership

The competitive spread within individual categories was substantial.

Baby feeding and weaning

Bibado achieved a 46.1% mention rate across the tracked dataset.

Its closest major competitors included:

• Brand: Bibado
  AI mention rate: 46.1%
• Brand: Munchkin
  AI mention rate: 37.6%
• Brand: Doddl
  AI mention rate: 24.2%
• Brand: Tommee Tippee
  AI mention rate: 21.2%
• Brand: bamboo bamboo
  AI mention rate: 18.2%
• Brand: Vital Baby
  AI mention rate: 12.1%
• Brand: Tidy Tot
  AI mention rate: 7.9%

Bibado was therefore recommended more frequently than significantly larger and more established consumer brands across this particular prompt universe.

This is an important characteristic of AI search.

Brand size alone does not determine recommendation visibility.

AI systems appear to reward a combination of category relevance, accessible information, third-party corroboration, contextual product fit and the strength with which a brand is associated with a specific consumer need.

For challenger ecommerce brands this creates an opportunity.

AI search can potentially compress some of the historical advantage enjoyed by the largest brands.

3. Strong ecommerce visibility can be built around a clear consumer problem

Bibado's strongest dataset was not constructed around branded searches.

It included high-intent questions such as:

• best baby-led weaning products
• best products for starting weaning
• best baby cutlery for self-feeding
• best suction plates for babies
• best baby tableware sets for weaning
• best weaning brands UK
• best products for encouraging independent eating
• what are the best products to make baby-led weaning less messy?
• best weaning brands for first-time parents
• what should I buy before starting baby-led weaning with my first baby?

These are not navigational searches.

They represent needs, problems and purchase decisions.

Bibado's 46.1% mention rate suggests that AI product discovery can favour brands whose proposition is strongly connected to the problem being described.

For ecommerce marketers this changes how category visibility should be approached.

Ranking for a product noun is no longer enough.

Brands should understand the entire prompt universe surrounding the buying decision, including:

Problem → research → consideration → comparison → recommendation → purchase

4. Fashion demonstrates how brutal the AI consideration gap can become

Queens of Archive recorded an overall mention rate of 2.4% within its tracked fashion dataset.

By comparison:

• Fashion brand: Rixo
  AI mention rate: 63.4%
• Fashion brand: Reformation
  AI mention rate: 29.3%
• Fashion brand: Ganni
  AI mention rate: 7.3%
• Fashion brand: Rat & Boa
  AI mention rate: 4.9%
• Fashion brand: Realisation Par
  AI mention rate: 4.9%
• Fashion brand: Queens of Archive
  AI mention rate: 2.4%

Rixo appeared around 26 times more frequently than Queens of Archive across the tracked responses.

The searches included commercially relevant terms such as:

• best vintage inspired dress brands
• best floral dress brands
• best wedding guest dress brands
• independent British dress brands
• best midi dress brands
• premium dress brands UK
• best independent fashion brands UK
• best occasion dress brands
• which British brands are known for floral dresses?
• recommend independent British occasionwear brands

This exposes one of the biggest risks of AI search for ecommerce businesses.

A consumer may describe almost exactly what a brand sells yet the AI system may still repeatedly recommend competing brands instead.

In traditional search this might represent a ranking problem.

In AI search it is potentially a consideration problem.

The brand may never enter the answer at all.

5. Being mentioned and being cited are different advantages

Our study also found a clear distinction between brand visibility and owned-source visibility.

A brand may be recommended by an AI assistant without the brand's website being used as evidence for that recommendation.

Across the core benchmark:

• Brand: Bibado
  Mention rate: 46.1%
  Citation rate: 14.5%
• Brand: Rollagranola
  Mention rate: 15.0%
  Citation rate: 6.7%
• Brand: Queens of Archive
  Mention rate: 2.4%
  Citation rate: 0%

This distinction matters.

A mention tells us:

The AI system considers the brand relevant.

A citation tells us:

The brand or its content is being used as evidence.

The strongest AI-search strategies therefore need to solve both problems.

Brands need to become:

1. recommendable
2. citable

For ecommerce websites this means product pages alone are unlikely to be sufficient.

Brands need useful information that answers the questions consumers ask before deciding what to buy.

6. Third-party sources have significant influence over ecommerce discovery

One of the clearest findings from our wider ecommerce snapshot analysis was the importance of sources outside the brand's own website.

We examined four representative ecommerce AI visibility audits across:

• skincare
• beauty
• bath and body
• food

Together these supporting audits contained 267 AI responses.

Across the top ten source domains returned for each audit there were 486 source mentions.

Reddit alone accounted for 109 of those 486 top-source mentions, equivalent to 22.4%.

Importantly this does not mean Reddit represented 22.4% of every citation in the complete dataset. It means Reddit represented 22.4% of the mentions recorded across the top-ten source lists in these four representative audits.

Even with that qualification the signal is substantial.

Reddit appearances across the four audits

• Ecommerce category: Cosmetics
  Reddit mentions among top sources: 52
• Ecommerce category: Bath & body
  Reddit mentions among top sources: 36
• Ecommerce category: Skincare
  Reddit mentions among top sources: 13
• Ecommerce category: Granola / breakfast
  Reddit mentions among top sources: 8
• Ecommerce category: Total
  Reddit mentions among top sources: 109

Reddit was the most frequently occurring source domain in three of the four examples.

But Reddit was not the only important external influence.

Frequently surfaced source types also included:

• national publishers
• lifestyle publications
• specialist review websites
• beauty publications
• food publishers
• YouTube
• Instagram
• specialist retailers
• independent category websites

Examples appearing in the analysed source sets included BBC Good Food, The Guardian, The Independent, Good Housekeeping, Cosmopolitan, Who What Wear, Which?, Allure and specialist vertical publications.

The implication is significant.

Your ecommerce website is only one part of your AI visibility footprint.

AI reputation is distributed across the wider web.

7. Invisibility is common even for established ecommerce businesses

Four representative supporting ecommerce audits generated:

• 267 AI responses
• 24 target-brand mentions

That is equivalent to target brands appearing in roughly 9% of the analysed responses across this supporting sample.

Individual brand response rates ranged from:

2% to 13%.

The brands were not necessarily unknown businesses.

They included established consumer brands with real product ranges, ecommerce websites, media exposure and recognisable propositions.

The issue was not whether information about the businesses existed online.

The issue was whether the businesses were sufficiently associated with the non-branded consumer questions being asked.

That distinction is fundamental.

Traditional brand awareness asks:

Have people heard of us?

AI visibility asks:

Does an AI system believe we belong in the answer?

8. Competitors can own dramatically more AI visibility without owning the entire market

Rollagranola provides another useful example.

Across its tracked dataset:

• Brand: Bio & Me
  AI mention rate: 30.6%
• Brand: Lizi's
  AI mention rate: 17.0%
• Brand: Rollagranola
  AI mention rate: 15.0%
• Brand: Eat Natural
  AI mention rate: 14.8%
• Brand: Artisan Granola
  AI mention rate: 13.3%
• Brand: Husk & Honey
  AI mention rate: 10.6%
• Brand: Jordans
  AI mention rate: 8.1%
• Brand: Surreal
  AI mention rate: 4.6%

Rollagranola already had meaningful AI visibility.

But Bio & Me appeared more than twice as frequently.

This demonstrates why simply asking ChatGPT about your own brand is a poor way to measure AI-search performance.

The meaningful question is relative:

How much of the relevant consumer consideration space do we own compared with the alternatives?

For this reason IgniteStack measures AI share of voice across a deliberately constructed universe of commercial and informational prompts rather than relying on isolated branded queries.

9. Product attributes only become useful when AI systems connect them to the brand

The underlying response analysis also reveals another issue for ecommerce businesses.

Brands often possess strong differentiators that AI systems rarely repeat.

For example Rollagranola's dataset tracked attributes including:

• natural ingredients
• artisanal
• gluten-free
• nutritious
• paleo-friendly
• vegan
• keto-friendly
• nootropic
• adaptogenic

The brand was strongly associated with some attributes such as natural ingredients and artisanal positioning while other commercial differentiators appeared much less frequently.

This creates an important distinction between:

What a brand says it stands for

and

What AI systems reliably understand the brand to stand for.

The same applies to sustainability, quality, ingredients, product efficacy, heritage, craftsmanship, inclusivity and value.

A differentiator that AI systems cannot consistently retrieve or corroborate may have limited influence over AI-mediated discovery.

What determines ecommerce AI visibility?

Our research suggests there is no single optimisation lever.

The brands appearing most consistently tend to create stronger signals across multiple layers.

1. Category association

AI systems need enough evidence to connect the brand with the product category and consumer need.

This sounds obvious but it is one of the biggest weaknesses we find.

A fashion brand may describe its collections beautifully without ever creating enough machine-readable evidence that it should be considered for:

"best wedding guest dresses"

or:

"independent British occasionwear brands".

2. Answerable owned content

Brands need pages capable of answering specific buying questions.

Examples include:

• best product for a particular use case
• product selection guides
• comparison pages
• sizing guidance
• ingredient explainers
• buying guides
• problem-and-solution pages
• category education
• product compatibility information
• expert advice
• alternatives and trade-offs

This does not mean publishing hundreds of thin pages targeting prompt variations.

The objective is to create canonical, useful resources that comprehensively resolve genuine customer decisions.

3. Third-party corroboration

Our citation analysis reinforces the importance of external validation.

This may come from:

• editorial coverage
• specialist publishers
• product reviews
• relevant forums
• Reddit discussions
• expert recommendations
• comparison websites
• retailer listings
• creator content
• industry publications

For GEO this represents one of the biggest differences from conventional on-site optimisation.

Brands cannot fully control the information environment AI systems use to evaluate them.

4. Clear product entities

AI systems need to understand:

who the brand is
what it sells
who those products are for
what makes them different
when they should be recommended

Weak or inconsistent product information makes those associations harder to establish.

Structured data, consistent naming, strong category architecture and well-defined product attributes can all contribute to better entity understanding.

5. Evidence

Statements such as:

"premium"

"sustainable"

"best quality"

"clinically proven"

"expert designed"

or:

"award winning"

become much more useful when independently verifiable.

AI systems operate in an environment where corroboration matters.

The strongest ecommerce brands therefore need more than claims.

They need an evidence layer.

SEO vs GEO for ecommerce

GEO should not be treated as a replacement for SEO.

There is significant overlap.

Strong technical foundations, crawlable pages, descriptive content, authority and quality external references remain valuable.

The difference is in the outcome being optimised.

Traditional SEO asks:

Can the page rank?

Ecommerce GEO asks:

Can the brand or product become part of the generated answer?

That introduces additional optimisation targets including:

• brand inclusion
• recommendation frequency
• recommendation position
• source citations
• cross-platform consistency
• product attributes
• competitor share of voice
• third-party authority
• response accuracy

The goal is no longer simply a blue link.

The goal is consideration inside the answer itself.

What ecommerce brands should measure in AI search

We recommend monitoring at least six metrics.

AI Mention Rate

The percentage of relevant AI responses that mention the brand.

AI Share of Voice

The brand's visibility relative to tracked competitors across the same prompt universe.

Average Recommendation Position

Where the brand tends to appear when multiple brands or products are recommended.

Citation Rate

How frequently the brand's own domain is used as a cited source.

Source Share

Which third-party websites and communities most frequently influence the category.

Prompt Coverage

How much of the commercially relevant prompt universe contains the brand.

No individual metric gives the complete picture.

A brand may have strong mentions but weak citations or strong visibility in informational questions while remaining absent from high-intent product recommendations.

The ecommerce AI visibility framework

Based on our findings we group ecommerce AI-search work into six layers.

1. Measure

Map the prompt universe and establish visibility by category, product, use case and buying stage.

2. Diagnose

Identify where competitors are being recommended and why.

3. Build

Create the owned pages and information architecture needed to answer high-value customer decisions.

4. Corroborate

Strengthen the external evidence AI systems can discover across editorial, community and specialist sources.

5. Structure

Ensure products, categories, attributes and brand entities can be consistently understood.

6. Monitor

Track recommendation visibility as AI answers, citation patterns and competing brands change.

What this study means for ecommerce leaders

The strategic implication is straightforward.

AI search creates another consideration layer between a consumer recognising a need and reaching a website.

If someone asks:

"What are the best products for starting baby-led weaning?"

"What are the best independent British dress brands?"

"Which granola brands are actually healthy?"

or:

"What are the best affordable skincare brands in the UK?"

the response may influence which businesses the consumer investigates next.

Some brands are already being inserted into these decisions repeatedly.

Others are largely absent.

The difference between 46.1% visibility and 2.4% visibility demonstrates how wide that gap can become.

For ecommerce brands the question is therefore changing from:

"Do we rank for the keyword?"

to:

"When AI decides which brands deserve consideration, are we one of them?"

Methodology

The UK Ecommerce AI Search Visibility Study 2026 uses proprietary IgniteStack analysis of high-intent consumer discovery prompts and AI-generated responses.

Core benchmark

The principal benchmark consists of three detailed UK ecommerce datasets covering:

• baby feeding and weaning
• fashion and occasionwear
• food and breakfast products

Together these contain:

141 tracked prompts and 828 AI responses.

Prompts were selected to represent real discovery, recommendation, comparison, problem-solving and purchase-consideration behaviour rather than relying solely on branded searches.

Supporting ecommerce analysis

Additional directional findings were drawn from IgniteStack ecommerce visibility snapshots across consumer categories including skincare, cosmetics, bath and body and food.

Four representative supporting audits contained 267 AI responses.

These smaller datasets are used to identify broader patterns such as source influence. They are not treated as directly comparable league-table datasets because prompt composition and collection dates differ.

Metrics

Depending on dataset availability our analysis included:

• brand mention rate
• competitor mention rate
• citation rate
• relative visibility
• recommendation position
• response rate
• source-domain appearances
• brand attributes
• prompt-level performance

AI outputs can change over time. The figures in this report represent observed results within the analysed datasets rather than permanent rankings.

Frequently asked questions

What is ecommerce AI search visibility?

Ecommerce AI search visibility measures how frequently a brand or product appears when consumers ask AI systems product discovery, recommendation, comparison and buying questions.

Rather than measuring only search-engine rankings it measures whether the brand becomes part of the AI-generated answer.

What is GEO for ecommerce?

Generative Engine Optimisation, or GEO, is the process of increasing how accurately and frequently a brand, product or website appears within AI-generated answers.

For ecommerce this can involve product and category content, technical structure, entity optimisation, third-party authority, digital PR, reviews, community presence and content designed around real purchasing decisions.

How is GEO different from SEO?

SEO primarily focuses on improving visibility within traditional search results.

GEO focuses on visibility within generated AI answers.

The disciplines overlap heavily but AI search introduces additional metrics such as brand inclusion, recommendation position, citation share and competitor AI share of voice.

Can ecommerce brands rank in ChatGPT?

ChatGPT does not operate exactly like a conventional search-engine ranking page.

Brands can however appear within its recommendations and responses when the system considers them relevant to the user's request.

This makes measuring prompt-level brand inclusion more useful than thinking only in terms of traditional ranking positions.

Why are third-party websites important for AI visibility?

AI systems can use information from across the web when forming answers.

Our ecommerce analysis repeatedly surfaced publishers, specialist websites, Reddit, YouTube and other third-party sources alongside brand-owned content.

This means digital authority beyond the brand's own website can influence AI discovery.

Does a large ecommerce brand automatically have better AI visibility?

No.

Our analysis found substantial differences between brand size and AI visibility.

In one category a specialist ecommerce brand achieved a higher tracked mention rate than several significantly larger consumer brands.

Relevant category association, content, corroboration and source authority can all influence the visibility landscape.

How can I measure my ecommerce brand's AI visibility?

Start by building a prompt universe covering the questions customers ask throughout research and purchase.

Then measure your brand and competitors across metrics including mention rate, share of voice, average recommendation position, citation rate and source influence.

Isolated branded searches are not sufficient to understand true commercial visibility.

About IgniteStack

IgniteStack is an AI search visibility consultancy helping brands understand and improve how they appear across AI-led discovery.

We analyse the prompts customers use, benchmark brands against competitors, identify the sources shaping AI recommendations and build strategies across GEO, AEO, SEO, content and digital authority.

Want to know how visible your ecommerce brand is in AI search?

Get an IgniteStack AI Visibility Snapshot to see where your brand appears, which competitors are being recommended instead and where the biggest opportunities sit.