This retailer went from the first five on 4 of 150 buyer questions to 118 of 150, between 14 June 2026 and 20 September 2026. It was named at all on 131 of 150 questions, up from 9 of 150. The first citation came 11 days after publishing, the quickest of the four.
Why retail moved fastest
The fastest of the four to move, because buying questions in retail are asked in plainer language and the pages that answer them did not exist anywhere yet.
Shopping questions are asked in ordinary words. Somebody asks which brand is best for a particular use, at a particular budget, for a particular kind of person. Those questions have clear answers, and in most categories nobody had written the page that answers them. A wide gap and plain language is the easiest starting position there is.
There is a second reason, and it is less comfortable. Retail brands publish a great deal of content already, almost none of it answering a question. Category pages, lookbooks, styling posts and offer announcements are all written for somebody who already knows the brand. Replacing a fraction of that output with question shaped pages was most of the work.
What the winning pages had in common
They answered a comparison, not a category. "Which of these two materials lasts longer with daily use" is a question. "Our product range" is not.
They carried the details a shopper asks and most brands hide. What it is made of, what it does not do, and who should buy the cheaper option instead. An assistant repeats that kind of sentence because it can stand behind it.
They were present where assistants already look for retail. Marketplace listings, category directories and genuine review pages. A brand that exists only on its own site gives an assistant one source, and a competitor with three looks safer to recommend.
They stated a real price, or a real range, with what changes it. Retail is the one sector where prices are usually public already, and brands still manage to make them hard to extract by putting them only inside a product widget that a crawler cannot read.
A worked example
One page compared two materials the brand sells, side by side, and said plainly which one is wrong for humid conditions and which costs more to maintain. It recommended the cheaper option for one use case.
That page became the most cited page in the run. The brand's own bestselling product was not the recommendation in it. The reason it worked is the reason it felt risky: an assistant answering "which of these should I buy" wants a source that has already weighed both, and a page that only argues for one product has not weighed anything.
What did not work
Pages built around the brand name did nothing, because nobody asks an assistant about a brand they have not heard of. Discount and offer pages did nothing either. Assistants answer the question they were asked, and almost nobody asks where the sale is.
Styling and inspiration content also did nothing measurable, which was the most expensive finding for this client, because it was most of what they had been producing.
What this run does not tell you
It does not tell you whether any of this sold anything. We measured whether the brand was named, not whether being named produced an order, and those are different questions. A shopper who reads a recommendation may still buy from a marketplace, in which case the brand is credited for nothing in its own analytics.
It also covers one retail category. A category where the buying decision is mostly about price or delivery speed rather than suitability may behave differently, and we have not tested one.
Common questions about this run
Can I see the 150 questions?
Not yet, and that is a fair thing to hold against the numbers until we publish them. The sets were built and run by hand before this product existed, and they are being prepared for publication. Until they are, treat these figures as a vendor's own account. The scan on this site is free and it will give you your own number, which you do not have to take on trust.
Why should I believe a vendor reporting on its own client?
You should discount it, and so does every assistant we tested. That is why the most useful number in these studies is not a rate at all. It is the first citation: a named page, a named assistant, the question it was cited for, and 14 days between publishing and being quoted. A dated fact with a URL behind it survives scepticism in a way a percentage does not.
How much of this was the pages, and how much was the assistants changing?
We cannot separate the two with this data, and we are not going to pretend otherwise. The assistants changed what they do during every one of these periods. What we can say is that the movement showed up first on the questions the new pages were written for, and that the first citation in each run was of a specific new page.
Would this work in my sector?
The method worked in all 4 sectors we tried it in, and the ceiling was different in each. The limit is set by who is already trusted for your category. If your questions resolve to government portals or large analyst firms, expect to do well on the buying questions and to keep losing the purely informational ones.
How long before anything moves?
The first citation of a new page arrived between 9 and 16 days after publishing across these 4 runs. A change in the overall rate takes longer, because a rate over 150 questions only moves when many questions move. Expect weeks for the first signal and months for the rate.
What would you do differently now?
Fix the existing site before publishing anything new. In the run below, an assistant searched a client's own site, found a headline figure that could not be true, and advised a buyer against them. The new pages were accurate and the old site around them was not, and the site is what got checked.
What to do first if you sell to consumers
Check whether your prices and specifications can be read without scripts running, because in retail they very often cannot. Then write one page that honestly compares two of your own options and says who should buy the cheaper one. That page will feel wrong to publish and it is the one most likely to be quoted.
How this was counted
The tracked set is 150 questions, and that is the denominator under every number on this page. The questions are the ones a buyer in this sector actually types, and not one of them names the company. That rule is the whole measurement. A question that names the business gets a polite answer about the business, so it tells you about your prompt rather than about your position.
Each question was asked on 5 assistants, because they answer the same question differently on the same day from different sources. "Named" means the company appears anywhere in the answer. "In the first five" means it appears among the first five products the answer lists, which is the number that matters, because an assistant that names three or four options has no sixth place to give you.
Baseline reading: 14 June 2026. Current reading: 20 September 2026. Pages published in between: 240.
The four things we recorded for every question
- Whether the company was named at all, anywhere in the answer, including in a list rather than as a recommendation.
- Whether it was in the first five products named, and in what position.
- Every source the answer cited, by domain, tagged as official, the company's own site, a competitor's own site, a directory or listing, news, or a community thread.
- Whether the assistant actually ran a live search, asked directly and recorded as a count, because an answer given from memory and an answer given from a live search are two different measurements and averaging them produces a number that means nothing.
What this is, and what it is not
These runs were operated by hand, before this product existed, using the same method the product now runs on a schedule. They are our own results on our own clients, so read them as a vendor reporting its own work. The prompt sets are being prepared for publication so that anyone can repeat the run and check us.
They are also not a controlled experiment. Nobody ran a matched group that published nothing, so we cannot separate our work from everything else that happened in those months, including the assistants changing what they do. What the numbers show is that companies which did this work moved, not that nothing else could have moved them.