What should an ecommerce team look for in a tool that tracks product recommendations in AI shopping results?
An agency checklist for ecommerce teams choosing a tool to track product recommendations in AI shopping results: product-level data, surfaces, citations and attribution.
We work with ecommerce teams, and the first thing we tell them is that a brand-level score is the wrong unit. A store sells products. The useful question is which products get recommended, for which prompts, against which competitors. Here is the checklist we use when we pick tooling for a store.
Product-level, not only brand-level
A visibility score that says "your brand appears in 28 percent of answers" hides the useful detail. We want to know whether the hero product appears for "best X for Y", whether a competitor's cheaper item takes your place, and whether the answer changes by country. So we look for tools that group prompts by category, intent and use case and track named products.
In Promptwatch, which we run client programs on, AI Shopping covers ChatGPT shopping carousels and Amazon Alexa product results, and you can upload your own catalog. The shopping views include tracked products, product position analytics and top merchant domains. Shopping Insights is available from the Business plan, so we tell smaller stores up front which parts they can and cannot use.
Which shopping surfaces
We ask which surfaces a tool actually captures. Carousels in ChatGPT, Alexa results and sponsored placements all behave differently. Ads Radar in Promptwatch (Business plan and above, and every agency plan) captures sponsored ads inside ChatGPT Search, Google AI Overviews and Google AI Mode answers to tracked prompts: advertiser, copy, image or price, and target URL. It does not cover Perplexity, and it does not report on your own OpenAI campaigns, so we say that in the proposal. For competitive stores, seeing who bids on your prompts is useful context.
Sources the engines trust
Product recommendations lean on reviews, comparison articles, forums and merchant pages. Citation analytics shows which domains and pages get cited for the category, and the Reddit, YouTube and LinkedIn reports show where community content drives answers. For many stores the work turns out to be getting included in the two or three sources the engines keep citing, not rewriting product pages.
Crawler access to product pages
Large catalogs hide access problems. Bot protection, faceted navigation and JavaScript rendering can stop an AI crawler from reading product pages. Agent Analytics provides IP-verified crawler logs with error tracking and failure explanations, so we can see which product and category URLs OAI-SearchBot, ChatGPT-User or PerplexityBot fetched, and which failed. Crawler logs start on the Professional plan at $245 a month.
Content and data quality
Engines read product titles, descriptions and attributes. Thin or inconsistent data hurts. Site audits (21 checks, with weekly runs) catch technical issues, and Content Refresh suggests diff-style updates for existing pages. Content gap analysis shows which buying questions have no good page, such as "X versus Y" comparisons and buying guides. We keep a human in the loop on any text that states specs, prices or claims.
Revenue attribution
We ask the same question of every tool: can it show AI-referred sessions and conversions on the store? Promptwatch visitor analytics tracks AI-referred traffic and conversions from ChatGPT, Perplexity, Gemini, Claude, Copilot and others through a script, GTM template or the WordPress plugin. With 360 Insights joining crawler logs, citation clicks and visits per page, we can tell which cited pages produce orders.
Local demand
For stores with regional demand or physical locations, state and city tracking matters, because an answer can differ by place. That is a Business plan feature and on every agency plan, and Business also adds local GEO and SEO coordinate targeting.
Honest limits
Shopping results change often and vary between runs. We cannot promise that a product will be recommended, and no tool can. We report share of voice by category over weeks, show the sources and technical issues behind changes, and recommend actions.
How to shortlist
Ask each vendor five questions. Does it track products, not just brands? Which shopping surfaces does it capture, and how? Does it show sources and crawler activity? Does it connect to your analytics or store data? What does it cost at the tier that includes those features? The answer to the last one is often where a shortlist shrinks.