How UK retailers can show up accurately in AI shopping answers
AI shopping assistants can only describe your products as well as your product pages describe them. A practical guide for UK ecommerce teams on identifiers, specifications, structured data, clear prices and stock, crawlable pages and consistent naming.
When a shopper asks an AI shopping assistant for a quiet integrated dishwasher under £500, the answer is built from product data someone else has collected. If your pages are vague, inconsistent or hard to read, your model may be missing from that answer, or shown with the wrong price. The fixes are mostly basic catalogue hygiene, done properly.
Why accuracy matters more than presence
A shopping assistant has to do three things with your product: work out exactly what it is, check it against what the shopper asked for, and send the shopper somewhere they can buy it. Each step depends on data. A product that cannot be identified with confidence tends to be dropped. A product with an old price or the wrong colour can be shown, clicked and then abandoned, which is worse than not being shown at all.
None of this guarantees a place in any answer. Each assistant chooses its own sources and ranking. What you control is whether your data is clear enough to be read correctly when it is used.
1. Publish real identifiers
Identifiers are how one listing is matched to the same product elsewhere. The most useful is the GTIN, the barcode number assigned through GS1. Add the manufacturer part number (MPN) and brand alongside it, and keep your own SKU for internal use.
Show the GTIN on the page and in structured data for every variant, not just the parent product.
Never reuse a GTIN across different sizes or colours. Each sellable variant has its own.
For own-brand goods, register GTINs rather than inventing numbers.
2. Write specifications as data, not prose
Shoppers ask with constraints: noise under 44 dB, fits a 60 cm gap, works with an induction hob, size 9 wide fit. An assistant can only filter on those constraints if they appear as clear attribute values. A paragraph saying a kettle is 'generously sized' does not help; '1.7 litres' does.
Use a specification table with one value per row and units written out (cm, kg, litres, dB, W).
Use the same attribute names across the category, so 'Width' is not 'W' on half the range.
Include the attributes people decide on: dimensions, capacity, compatibility, materials, energy rating, what is in the box.
3. Add structured data that matches the page
Product markup using the schema.org Product type gives machines a reliable copy of the key facts: name, brand, GTIN, MPN, image, offer price, currency and availability. Google's product structured data guidance is a sensible baseline. The important rule is that the markup must agree with what a shopper sees. A mismatch between visible price and marked-up price is a quick way to lose trust.
Prices change, so anything that reads your price is taking a snapshot. Help it take a correct one.
Show one current price per variant, in pounds, with VAT treatment clear.
Make sale and was-prices obvious, and remove expired offers promptly.
State stock status plainly: in stock, out of stock, pre-order, or available to order.
Keep delivery cost and timing near the price, not hidden in the basket.
Good assistants show where a price came from and when it was checked. Our note on price source and checked time explains why that date matters.
5. Keep pages crawlable and stable
If key facts only load after a script runs, or sit behind a postcode prompt or cookie wall, many systems will not see them. Keep the essentials in the page HTML.
Render title, price, stock, identifiers and the specification table on the server.
Give each variant a stable URL that loads the right colour or size directly.
Redirect discontinued products sensibly rather than returning an error page that looks like a live product.
Check your robots rules do not block product pages by accident.
6. Name products consistently
The same pram should not be called three different things on your product page, your feed and your marketplace listing. Use a predictable pattern: brand, product line, model, key variant. For example, 'Brand X Aero 2 Pushchair, Black' rather than 'Aero pram – stunning black edition!'. Consistent names make matching easier and stop variants being mistaken for different products.
A quick audit you can run this week
GTIN on every variantVisible on the page and in structured data, unique per size and colour.
Brand and MPN presentMatching the manufacturer's own naming.
Spec table with unitsOne attribute per row, consistent names across the category.
Markup matches the pagePrice, currency and stock agree with what shoppers see.
Clear current priceOne price per variant, expired offers removed.
Plain stock statusIn stock, out of stock or pre-order, per variant.
Crawlable essentialsKey facts in the HTML, no prompt or login needed to see them.
Stable variant URLsEach link opens the exact variant described.
Consistent namingSame product name across site, feed and marketplaces.
Simlir is a structured product-data API that AI shopping assistants can call to find the right product and return clean product data with a retailer link. It returns products as clean JSON: title, identifiers, specifications, image, a dated price snapshot and the retailer link. The UK is our deepest market, and through our Shopify connection we can also return products from connected Shopify merchants in other countries. The clearer your identifiers, specs and prices, the more accurately your products can be represented. If you want to talk about how your range comes through, get in touch.
Frequently asked questions
Will fixing my product data guarantee my products appear in AI shopping answers?+−
No. Each assistant picks its own sources and ranking. Clear data makes it more likely your products are identified and described correctly when they are used.
Which fix should we do first?+−
Identifiers and prices. A unique GTIN per variant and a clear current price prevent the most damaging errors: wrong product and wrong price.
Does product structured data help with AI shopping answers?+−
It gives machines a reliable copy of key facts such as name, brand, GTIN, price, currency and availability. It only helps when it agrees with what shoppers can see.
Do we need a separate feed for AI shopping assistants?+−
Not necessarily. Accurate, crawlable product pages with matching structured data cover most of the basics. Keep any feeds you already send consistent with those pages.
