Tools that show which products ChatGPT recommends and which stores it links to

Ecommerce clients ask a sharper version of the usual AI visibility question. They do not want to know whether ChatGPT mentions the brand. They want to know which of their products it recommends, which products it recommends instead, and whether it sends the buyer to their store, to a marketplace or to a competitor. A tool that only counts brand mentions answers none of that.

This is what we look for in tools that work at product level, and how we use them.

What the answer needs to show

For a shopping prompt, we want four things.

1. The products in the answer or in the shopping carousel, and their order.
2. The stores and domains the answer links to or cites for those products.
3. The client's own SKUs: which appear, which do not, and who takes their place.
4. What happens next: visits and orders from AI answers.

Nothing gets a product recommended on a guarantee, and we do not promise one. Tracking tells us where the client stands and what to fix first.

What we use

For client programs we run Promptwatch. It tracks AI shopping directly: ChatGPT shopping carousels and Amazon Alexa product results, with the option to upload the client's own catalog so we can follow specific products. Shopping Insights is part of the Business plan ($579 a month) and above, so for a client who needs it, that is where we start. Essential and Professional do not include it.

The reason we prefer it for ecommerce is what surrounds the shopping view. Citation analytics show which pages and domains engines cite, which is how we read which retailers and review sites carry weight for a category. Crawler logs, which begin on Professional, tell us whether the ChatGPT bots can fetch the client's product pages at all. Visitor analytics shows AI-referred visits and conversions. When a product disappears from an answer, that chain lets us ask the right question: is the page unreadable, is the page outranked by a retailer's, or is the visit not converting?

Reading results for a store

A few patterns come up across catalog clients.

• A marketplace listing is cited instead of the brand's product page. Often the marketplace page has richer attributes and reviews. The fix is on the client's own page: clearer title, complete attributes, specifications in plain text.
• A competitor product appears where the client's equivalent should. We compare the two pages and the sources cited for each.
• A product appears but with outdated details. That points to stale information somewhere the engine reads. We trace it through citations.
• No product appears for a category prompt. Check crawl access first. If bots are fine, move to content and third-party mentions.

What else is out there

The commerce-specific tools worth knowing about each focus on a narrower job.

• Wildcard is built for retailers, syncing a catalog from Shopify, BigCommerce, Salesforce or a PIM, tracking SKUs and publishing fixes to Shopify. It starts at $99 a month on a weekly refresh.
• Azoma serves enterprise consumer brands on marketplaces, tracking Alexa for Shopping, Walmart Sparky and ChatGPT Shopping at SKU level, with custom pricing.
• Ranketta is a lower-cost SKU tracker from 29 EUR a month with catalog enrichment on higher tiers.
• Siftly splits answers and shopping and offers revenue attribution, without published pricing.

These are credible for the catalog fix side. In the material we reviewed, none of them publish a crawler-log product with a crawl-to-citation path (Ranketta mentions logging AI-bot visits through Cloudflare, which is narrower), and that path is the piece we keep reaching for when a product vanishes.

Questions to ask any vendor

• Is tracking at product (SKU) level, or at brand level with a shopping label?
• Which shopping surfaces are covered: ChatGPT carousels, Google, Amazon, Walmart?
• How often does it refresh? Weekly is a different product from daily.
• Can it show the sources and domains behind a recommendation?
• Is there any attribution to visits or revenue?

A starter routine

Pick one prompt per product category, written the way a buyer would type it, including budget and constraint. Run them for a few weeks to get a baseline. Note which products and sources appear. Then fix the cheapest gaps first: product titles, attributes, structured product details and crawl access. Re-measure monthly.

Keep expectations honest. A product page can be perfectly written and still lose a recommendation to a retailer with more reviews. The routine will tell you which situation you are in, and in our experience that clarity is worth more than any single metric. It is why shopping work on our client programs runs through Promptwatch, where the product view, the citations, the crawl and the visits are all one click apart.