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Online shops in AI chatbots: how to stand out
AI chatbots recommend products that they can describe in detail: price, specifications, availability, reviews and comparisons with other products. An online shop’s visibility in AI therefore depends on whether this information is included in the product’s HTML page and in the structured data. It also matters whether the product appears in comparisons, rankings and reviews outside the shop. You can check this by asking the chatbots the questions a customer would ask before making a purchase, without mentioning the shop’s name.
- How do AI chatbots choose which products to recommend?
- What does an AI chatbot need from a product page?
- Structural data and product feeds: what matters?
- Where else, apart from the shop, does the product need to be displayed?
- How can you check whether AI chatbots are recommending your products?
- How does Aelo help online shops?
- FAQ
- Sources
Below, we explain how chatbots choose products, what they need from a shop, and how to measure the results.
How do AI chatbots choose which products to recommend?
Just like brands: they combine knowledge gained from training with web pages retrieved at the time of the response. When the chatbot uses a search function, it breaks down the customer’s question into several sub-queries, including those relating to price, specifications, reviews and availability. In an AirOps study from March 2026, 95% of such queries had no search volume in keyword tools. ChatGPT cited only 15% of the pages it had fetched. The product includes this information in its response when a page provides an answer to these more specific questions. The mechanism is described in the text How AI chatbots choose the brands they recommend.
The format of the responses is also changing. From October 2026, ChatGPT’s responses will more often take the form of side-by-side comparisons and cards, as we discuss in the article GPT-6 in ChatGPT: what it means for brand visibility. In this scenario, what matters is whether the model contains product data that can be compared with that of competitors: price, dimensions, composition and delivery time.
What does an AI chatbot need from a product page?
Facts set out in writing that can be quoted without taking up the whole page. Adjectives such as „top quality” add nothing to the model. Numbers, specifications, dimensions and price do.
PRODUCT SHEET
Six things the AI chatbot reads
Each one answers one of the model’s auxiliary queries.
01Price and availabilitycurrent price, cost and delivery time, and stock levels shown in text on the website
02Parameters in figuresdimensions, weight, composition, standards and certificates rather than adjectives
03Who is this product for?uses; who it is suitable for, and who it is not suitable for
04Customer reviewsreviews displayed on the listing page and on independent websites, showing the number and average rating
05ComparisonHow does this product differ from similar ones in your shop and those sold by your competitors?
06Content in HTMLprice, specifications and reviews in the page code, rather than loaded via a script upon page load
A bullet point is most often used to distinguish recommended products from those that are merely listed.
aeloapp.ioThe last point is of a technical nature. In Vercel’s analysis, none of the major AI bots executed JavaScript. If the price or reviews only appear on the page after the script has loaded, the bot will not see them. You can check this by disabling JavaScript in your browser or by viewing the page source. Read more about the technical aspects in the article How to get AI to recommend your company.
The same effect is achieved by including specific details on company websites that are not online shops. In the case study of a lime manufacturer, the changes covered the specifications table, the statement of compliance with the standard and the pricing section on existing subpages. References to the website in AI responses rose from 6% to 94% phrases in 11 weeks. The process is described in the case study From 0 to 17 mentions on AI in five weeks and his Part Three: On Citations.
Structural data and product feeds: what matters?
Product structured data, including the product listing and reviews, describes the product in a format that search engines can read without having to guess: name, brand, price, currency, availability and reviews. Google describes these in its product structured data documentation. It also states in its AI features documentation that there are no additional technical requirements for AI Overviews and AI mode beyond standard SEO. Correct product data therefore simply amounts to good SEO for your shop. However, the reason why SEO alone is not enough is explained by Visibility in AI and SEO.
The two rules that are most often broken are: structured data must match what is visible on the website, and the price in the product feed must match the price on the product page. A discrepancy between them signals inconsistency, and AI models choose the sources they can trust. Consistent data about the company and its offerings across various points on the web is a requirement that recurs in every one of our assessments. This is described in the text Knows the brand, but doesn’t recommend it.
SHOP VISIBILITY IN AI
Three workplaces
The AI chat combines information from all three.
1st PLACE
Product sheetPrice, specifications, reviews and comparisons presented as text in HTML.RESULTThe model has facts it can cite.
2nd PLACE
Structural data and feedsProduct, offer and ratings in the code, and a feed compliant with the specification.RESULTSearch engines identify the product without having to guess.
3rd PLACElasts the longest
External sourcesPrice comparison sites, rankings, reviews and media sources from which the model retrieves product lists.RESULTThe model recognises validation from outside the shop and is more likely to recommend the product.
Without a third-place finish, the product is sometimes mentioned, but rarely recommended.
aeloapp.ioWhere else, apart from the shop, does the product need to be displayed?
The sources from which the AI chatbot draws its lists of products for comparison: price comparison sites, rankings and round-ups, reviews in trade media and on forums where customers ask for recommendations. The simplest way to know where to start is to check which sources the chatbot cites when recommending competitors’ products. This is a list of places where your competitors are present, but your product is not. The article explains why this works Why does AI recommend the competition?.
Paid advertising cannot replace this. Adverts in ChatGPT are labelled, displayed separately and, according to OpenAI, do not influence the content of the responses. See the text for details Advertisements on ChatGPT in Poland.
How can you check whether AI chatbots are recommending your products?
Ask the questions a customer would ask before making a purchase, without mentioning the name of the shop or the product. For example: which product from a given category to choose for a specific purpose, what to buy within a given budget, or how two popular models differ. Ask each question in several AI chatbots, in a new conversation, and note down which products and shops were mentioned, as well as the sources cited by the model. The method is described on the website How to find out what AI says about your business.
One test isn’t enough, because the chatbots respond slightly differently each time. The article explains why this is the case Why does AI give a different answer every time?. It makes sense to carry out a survey using a fixed set of questions, repeated at regular intervals, as described in the entry frozen query set and on the website How often should visibility be measured?. There are two metrics that matter, the same ones as in brand visibility in AI: reference product or shop in the reply and citation the shop’s web pages as a source.
If the chatbots don’t recognise the shop at all, start with the page ChatGPT doesn’t know my company. If they provide incorrect information about it, such as out-of-date prices, this text will help AI is providing false information about the company.
How does Aelo help online shops?
The visibility of an online shop in AI should be measured based on queries about products, not just the brand. We measure whether ChatGPT, Claude, Gemini and Perplexity mention your shop and products, which sources they cite, and who they recommend instead of you. The measurements are carried out by the module Visibility, product page updates and off-site activities are sent to Tickets, and the content is prepared by Studio. The text describes the whole process How Aelo works, and we compare other measuring tools on the website AI visibility tools.
You can find out more about working with shops on the website Aelo for e-commerce, and you’ll take the first measurement on Free account.
FAQ
Do AI chatbots recommend products from small online shops?
Yes, particularly when the questions are limited to the application, budget or region. With such questions, there are fewer candidates, and the decision is based on specific product details and confirmation from external sources, rather than the size of the shop.
Is structural data sufficient for a product to appear in AI responses?
No. Structured data helps search engines understand the product, but the AI chatbot also needs facts from the page’s content and confirmation from comparisons, rankings and reviews outside the shop.
Is it possible to pay for an AI chatbot to recommend my product?
No. Adverts in ChatGPT are labelled, displayed separately and, according to OpenAI, do not influence the content of the responses. Whether the model recommends a product depends on what it knows about it and what it finds online.
Why is the AI chatbot showing an out-of-date price for my product?
This is because they are using an older version of the website or an external source, such as a price comparison site with an out-of-date feed. Check that the price on the product page, in the structured data and in the feed is the same, and update any external sources that the product references.
Sources
- AirOps, report on searches, follow-up queries and citations in ChatGPT (March 2026): https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt
- Vercel, analysis of AI robot behaviour (December 2024): https://vercel.com/blog/the-rise-of-the-ai-crawler
- Google Search Central, product structured data: https://developers.google.com/search/docs/appearance/structured-data/product
- Google Search Central, AI features and your website: https://developers.google.com/search/docs/appearance/ai-features
- Aelo reports for Wapiennik „Węże” dated 24 June and 10 September 2026 (published with the client’s consent)
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Author
Patryk Pietrzyk
Co-founder and CTO of Aelo, he is responsible for technology and app development. He runs the Blue Code software development studio. At Aelo, he decides how the system collects and processes responses from AI models.
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