Which AI Assistants Recommend My Brand When Shoppers Ask Where to Buy It Through Retailers?
To find which AI assistants recommend your brand when shoppers ask where to buy it, run "where to buy" questions in each assistant and record which retailers are named, whether your product appears, and whether price and stock are right. Include questions such as "where can I buy [brand] [product] near me", "[product] in stock at [retailer]" and "cheapest place to buy [brand] [product]". For brands sold through retailers, the answers depend mainly on retailers' product data. Each assistant draws on different retailer data, as the table below shows. If retailers list your products with thin or wrong content, assistants either skip them or describe them badly.
Where does each assistant get retailer information?
| Assistant | Main source for "where to buy" answers |
|---|---|
| Google AI Mode, AI Overviews, Gemini | Google's shopping data, built from retailers' Merchant Center feeds and web pages |
| ChatGPT | Shopping searches that draw heavily on Google Shopping results, plus product feeds merchants share with OpenAI |
| Microsoft Copilot | Bing's shopping data and web results |
| Perplexity | Web results and shopping data from merchants and partners |
| Alexa for Shopping (Amazon) | Amazon's own catalogue and sellers |
| Retailers' own assistants | That retailer's catalogue only |
Because the data comes from retailers, a brand can be well described on its own site and still be missing from "where to buy" answers. That happens if retailers' listings are incomplete, out of stock or badly categorised.
How do you run the check?
- Choose 10–20 products that matter most, by revenue or strategic priority.
- Write questions for each: where to buy, near me, in stock, price, and "at [retailer]" for your main retail partners.
- Run them in each assistant your shoppers use, two or three times each, with the location set.
- Record the retailers named, whether your exact product appears, price and stock stated, and the source.
- Compare with reality: does each retailer actually stock the item in that area, at that price?
- Repeat monthly and after range changes or promotions.
Why does a brand drop out of "where to buy" answers?
- Thin retailer content: the retailer uses a short title and few attributes, so the product does not match specific questions.
- Missing identifiers: without GTINs or consistent model numbers, systems cannot connect the retailer's listing to your product.
- Stock and availability gaps: out-of-stock listings are dropped or described as unavailable.
- Wrong category: the retailer files the product in a category that does not match the question.
- Few reviews on retailer sites: assistants favour listings with review evidence.
- Unreadable "where to buy" page: your own store locator loads through scripts, so assistants cannot see which retailers carry you.
How do you fix it?
- Supply complete content to retailers: titles, descriptions, attributes, images and identifiers. Many brands use content syndication processes or platforms to keep this consistent across retailers.
- Check key retailers' listings for your top products regularly, and request corrections where content is wrong or missing.
- Invite every buyer to review on retailer listings through post-purchase programmes that retailers permit.
- Make your "where to buy" page crawlable: list retailers by country or region in HTML, with links to product pages, rather than relying only on a script-based widget.
- Keep identifiers consistent across your site, retailers and marketplaces.
- Work with retailers on availability data so assistants do not send shoppers to empty shelves.
How product data should be structured is covered in how ecommerce brands can structure product data for AI recommendations. When assistants name a reseller instead of the manufacturer, see why AI recommends a reseller instead of the manufacturer.
What does a typical finding look like?
Consider an illustrative kitchen appliance brand sold through three national retailers. In Google's AI Mode, "where to buy [brand] stand mixer near me" names two of the retailers with correct prices. In ChatGPT, the carousel shows the mixer from only one retailer, and at an old price. Copilot names none of them. The brand finds that one retailer's listing lacks the GTIN and uses a generic title, so Google Shopping, and through it ChatGPT, rarely matches it to the product. The third retailer's feed lists the mixer as out of stock in a colour it actually carries. Correcting the retailer content and stock data fixes most of the gaps within a few weeks, without any change to the brand's own site.
What do people ask about retailer recommendations in AI?
Can a brand control which retailers AI recommends?
Not directly. It can influence it through complete retailer content, accurate availability and a crawlable "where to buy" page.
Should brands submit their own product feeds to AI assistants?
If you sell direct, yes, where programmes exist. If you sell only through retailers, focus on the quality of retailers' feeds and listings.
Do retailers' own assistants matter?
Yes, for shoppers already on that retailer's site or app. Your listing on that retailer determines what its assistant says.
How often do "where to buy" answers change?
Often, as stock and prices change. Monthly checks catch the main patterns.
Published by The Kovmere Team · updated Sep 29, 2026 · More resources