What the index measures — and why AI shopping matters
The UK Ecommerce AI Visibility Index measures how often five AI engines name specific UK retailers for real shopping prompts, scored on presence, position and sentiment. It matters because AI assistants increasingly shape which stores a shopper even considers before they open a browser tab. As AI tools become a routine step in how people research purchases, a retailer that is absent from that conversation is invisible at the exact moment a purchase decision forms.
AI visibility, in this context, means how often and how prominently an AI engine names a retailer when a shopper asks where to buy something — distinct from how well that retailer ranks in classic search or how good its prices are. Because AI answers move as models retrain and re-crawl the web, a single snapshot goes stale quickly. That is why this is built as an index rather than a one-off report: the same method, run every quarter, so the table shows trend rather than a single moment in time.
This edition is , covering . NeuralGen publishes the full prompt list and scoring method below so any retailer, agency or journalist can check or reproduce it.
The UK Ecommerce AI Visibility Index: this quarter’s table
The table below ranks named UK retailers by their AI Visibility Index score across all five engines, with a movement column versus . A higher score means a retailer is named more often, higher up in the answer, and described more favourably. Presence is not the same as endorsement — the index is a visibility reading, not a recommendation of quality, price or service.
| Rank | Retailer | Presence (% of answers) | Avg. position | Sentiment | Index score | Movement vs |
|---|---|---|---|---|---|---|
| 1 | <!-- DATA: retailer --> |
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| 4 | <!-- DATA: retailer --> |
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| 5 | <!-- DATA: retailer --> |
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| … | <!-- DATA: extend to top 10–20 --> |
Sample: <!-- CONFIRM: sample size + quarter date range -->. This table measures AI visibility, not price, service or product quality.
Index by category: fashion, electronics, home, beauty
AI shopping recommendations differ sharply by category, so the index reports a leading retailer and a notable pattern for each of the four categories tested. A specialist retailer can outscore a well-known generalist within its own category, which is one reason the index is broken down rather than reported as a single national ranking.
| Category | Most-named retailer | Presence | Notable pattern |
|---|---|---|---|
| Fashion | <!-- DATA --> |
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| Electronics | <!-- DATA --> |
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| Home | <!-- DATA --> |
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| Beauty | <!-- DATA --> |
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<!-- CONFIRM: category splits after study runs -->
This quarter’s movers
This section highlights the biggest quarter-on-quarter changes in the index — retailers whose score rose or fell since , and any new entrants. Movement matters more than a single score, because it shows whether AI engines are concentrating their recommendations on fewer stores or spreading them more widely over time.
- Biggest riser:
<!-- DATA: retailer + points/percentage --> - Biggest faller:
<!-- DATA: retailer + points/percentage --> - New entrants:
<!-- DATA: retailer(s) -->
If this is the first edition, there is no prior quarter to compare against, and this edition serves as the baseline <!-- CONFIRM: baseline quarter? -->. Movement columns will populate from the second edition onward.
Methodology
NeuralGen runs real shopping prompts across five AI engines from a UK location and logs every named retailer, its position and its sentiment, then rolls these into an index score. The method is designed to be reproducible and is published in full below, including the prompt list, so the index can be checked rather than taken on trust.
- Engines (5): ChatGPT, Perplexity, Claude, Google Gemini, Google AI Overviews.
- Prompts: real shopping questions across fashion, electronics, home and beauty (for example, “best place to buy [product] in the UK”, “where should I buy [product] online”).
<!-- CONFIRM: publish full prompt list --> - Location: United Kingdom.
<!-- CONFIRM: locale/settings --> - Dates: . Each prompt run times per engine to account for variation between sessions.
- Scoring: presence + position + sentiment combine into an index score (0–100).
<!-- CONFIRM: weighting and how scores roll into the overall index --> - Tooling: AI-answer capture via NeuralGen’s SE Ranking account plus manual verification.
<!-- CONFIRM -->
Why do AI engines favour some retailers?
AI engines lean on strong, consistent retailer entities and trusted third-party sources — review platforms, marketplaces, product-comparison sites and press coverage — plus clean, well-structured product pages and schema. Scale helps but is not everything: a specialist retailer with rich reviews and tight product data can outscore a larger generalist within its category.
Three things tend to correlate with a retailer appearing often in AI shopping answers. First, entity strength — a consistent business name, clear product range and a presence across the sources an AI engine reads, not just its own site. Second, structured product data — schema markup that lets an engine parse prices, availability and specifications reliably. Third, third-party corroboration — reviews, comparison articles and press mentions that give a model confidence in recommending a name it has seen described consistently elsewhere. See entity SEO for AI search and schema markup for AI engines for the mechanics, and where AI answers come from for how engines select sources in the first place.
Presence is not endorsement: how to read the index
A high index score means high AI visibility — not the best price, the best service or the best product. Read the table as a map of where AI attention currently sits, not a shopping recommendation.
AI engines can repeat outdated stock information, miss newer or smaller stores that haven’t yet built up citations, and vary their answer depending on exact phrasing, date and any personalisation applied to the session. None of that makes the index wrong; it makes it a measurement of visibility with the same limits as any measurement of a moving target. Treat movement between quarters as the more reliable signal, and treat any single quarter’s rank as directional rather than definitive.
What this means for ecommerce brands
If shoppers ask AI where to buy a product and your store is absent from the answer, AI is routing that attention — and likely some of that sale — to competitors you can measure but not yet match. The practical fix is entity, product-schema and citation work, not simply a bigger advertising budget, because the problem is one of visibility to a model, not visibility to a search engine’s ranking algorithm.
NeuralGen’s AI SEO agency service starts by measuring exactly where a retailer stands across the five engines this index tracks, then prioritises the fixes most likely to close the gap.
When is the next update?
The index updates quarterly. The next edition will republish the table with fresh testing and a new movement column, tracking whether AI is concentrating its retailer recommendations on fewer names or broadening them over time.
Any single quarter is a snapshot, not a verdict — the value of this page compounds as more editions accumulate and the movement data becomes meaningful. This page was last updated and will be refreshed on the same URL each quarter rather than republished under a new one, so bookmarks and citations stay current.