A shopper who asks an assistant for a recommendation has skipped the category page, the filters and half the funnel. What reaches them is a short written answer with a handful of links — and your product is either in it or it is not.
Who is asking
Shopping questions come in two shapes. One is exploratory — a category, a budget, a constraint, no brand named. The other is confirmatory: the shopper already has a product in mind and wants to know whether it is any good, whether it will fit, or whether something cheaper does the same job.
The first shape decides whether you are considered at all. The second decides whether that consideration survives. Both are usually answered from pages you did not write.
Questions buyers actually ask
Not keyword variants — whole questions, the way a person types them into an assistant. These are the shapes we build tracking around.
- best running shoes for flat feet under 150
- is this brand's warranty actually worth anything
- what is the difference between the two models in this range
- does it fit true to size
- where can I buy this with fast delivery to my country
- is there a cheaper version that does the same thing
Which surfaces matter here
Retail questions get answered on every surface we track, and Google's is the one where the answer sits closest to a transaction: an Overview appears above the results a shopper was about to scroll through. Chat surfaces take the open-ended end — constraints, trade-offs, “what should I get for this budget”.
The practical difference is freshness. Price, stock and shipping change faster than any index refreshes, so a recommendation can be confidently wrong about your product in a way a category page never is.
Reference pages for these surfaces
Where these answers come from
The pages an assistant leans on in this category are rarely all yours. Each block below is a source family worth checking before you write another article.
Marketplace and stockist listings
If you sell through marketplaces or retailers, their listing for your product is often better crawled and better reviewed than your own page. It may be the version an answer is built from — your brand named, their URL cited.
Review and roundup publishers
Editorial “best of” pages answer the exploratory question directly. They are also where a product picks up a one-line characterisation that follows it around for years.
Video and community
Unboxings, fit reviews, and threads where people compare two products they actually bought. This is where “does it fit true to size” gets answered, and it is not your copy doing the answering.
Your own product pages
The one source you control end to end. Whether it can be used depends on unglamorous things: whether the answer is stated in text rather than baked into an image, whether price and availability are machine-readable, and whether the variant the shopper asked about has its own crawlable URL.
Support and policy pages
Returns, delivery windows, warranty terms and sizing guides answer a whole class of confirmatory questions — and they are frequently the thinnest pages on a store.
What the documentation says
Each line below is read from the linked document — a vendor's own guidance, a published standard, or a public register. Nothing in this section is our estimate.
Google documents Product structured data as the way to describe a product page in machine-readable form, including price, currency and availability, and says it can make the page eligible for product rich results in Search.
Source: Google — Product structured data (opens the source document in a new tab)Google's review snippet guidance excludes self-serving reviews: a review about a business, placed on that business's own site, is not eligible for a review rich result. Ratings about a product and ratings about the company selling it are not the same asset.
Source: Google — Review snippet structured data (opens the source document in a new tab)Google states that you do not need to create new machine-readable files, AI text files or markup to appear in AI Overviews or AI Mode, and that there is no special schema.org structured data for those features. Product markup is worth doing for its documented purpose, not for a promised AI boost.
Source: Google — AI features and your website (opens the source document in a new tab)schema.org defines the Product type and its properties — offers, brand, aggregateRating and the rest — the shared vocabulary that Google's product documentation builds on.
Source: schema.org — Product (opens the source document in a new tab)
What is not documented
Open questions, not omissions. Anyone answering these with a precise number for your category is guessing.
- No assistant vendor publishes how it chooses which product to name. Price, review volume, retailer authority and freshness are all plausible inputs, and not one of them is documented as such.
- We cannot see how fresh the price and stock data behind an answer is. An answer can quote a price you changed last week, and no vendor documents a way to force a refresh.
- Whether an answer drew on your page or on a marketplace listing of your product is visible only when that answer exposes its sources. On surfaces that do not, we can report the mention and not its origin.
- Being cited does not imply a sale. We measure whether you appear and where the answer came from; closing the loop from an assistant answer to revenue is not something we can honestly claim to do.
What GetCited.me measures here
We run your buying questions on a schedule across the surfaces your plan covers, and record whether your brand or product was named, which competitors were named alongside it, which URLs were cited, and the sentiment of the mention.
The citation breakdown is the part retail teams use most. It separates “the answer named us and linked our own page” from “the answer named us and linked a marketplace” from “the answer solved the problem with a competitor”. Those are three different problems with three different fixes.
The headline figure is the organic mention rate — questions that never name your brand — reported apart from questions that already do.
Launch (Free) tracks Gemini only, with a one-time grant of answer credits and weekly automatic tracking. ChatGPT, Claude, Perplexity and Google AI Overviews are on Growth and Scale. Card payments are temporarily disabled while payment-provider onboarding completes, so every account currently runs on Launch — the pricing page lists what each plan includes.
What you can actually do
Concrete steps, each one checkable with a free tool or explained in the glossary.
Free tools for this industry
No account, no card — results in the browser.
Related terms
Keep reading
The reference sets behind this page.