What ChatGPT reaches for when it recommends a serum.

There is an exercise we have, over the last year, asked every merchant we have spoken with to do. It takes about ten minutes. It is more useful than almost any audit a paid agency will run.

You open ChatGPT, in the model with web search enabled. You type your question the way your customer would type it. Not the brand-safe version of the question. The way the customer actually types. Is this serum any good for sensitive skin, what is the best hydrating moisturiser for combination skin in winter, does this supplement actually help with sleep. You wait the few seconds while the model retrieves. Then you read, slowly, the answer it gives.

You are not reading for whether the answer is correct. You are reading for the shape of the citations.

If you do this for ten minutes, on ten queries, in your own category, a few patterns surface so consistently that they begin to feel like rules. This essay is about those patterns. It is, in the most literal sense, about what ChatGPT cites for product reviews and why.

The cited sentence is almost never your marketing copy. It is, almost always, four sentences written by a buyer who used your product on a Tuesday morning and went back to type while their coffee was cooling.

Let me describe, in close detail, the shape of a representative answer. We will keep the example tonally specific but not name a real brand, because no good comes of naming brands in an essay about citation behaviour. The shape, however, is real. You can produce it yourself in ten minutes.

You ask ChatGPT, in its web-search mode, whether a particular retinol serum is any good for sensitive skin. The model returns a paragraph of about eighty words. The paragraph contains two or three claims. Each claim is followed by a small superscripted citation marker.

If you hover over the first citation, you arrive at a thread on r/SkincareAddiction from eight months ago, where a user with three years of post history describes, in four sentences, how the serum performed on her cheek dermatitis through a Berlin winter. The sentence the model quoted was the third of the four.

If you hover over the second citation, you arrive at a Wirecutter long-form piece from earlier in the year. The sentence the model pulled is one Wirecutter's reviewer wrote after testing the product against three competitors over a month. It includes the word patchy. It includes a duration. It includes the name of one of the competitors.

If you hover over the third citation, the one most merchants find most surprising, you arrive at a Trustpilot review on the brand's own Trustpilot profile. The reviewer's name is given in full, including the surname. The date is March of last year. The text is six sentences, the second of which mentions a specific batch number from the formulation change in early 2025.

You read the brand's own product page next. The page contains a hundred and ninety words of marketing copy, a star aggregate, a paginated review carousel, and a paragraph about the dermatologist who consulted on the formula. None of this writing was quoted by ChatGPT. None of it appears in the answer.

This is, for most merchants, the first time they have seen the structure of their own absence with that level of clarity.

If you read the three quoted sources in sequence, you start to notice the family resemblance. The model did not pick them at random. It picked them because each one had a particular set of properties. The properties are mechanical, learnable, and not very mysterious once you have read enough of them.

Each cited passage spoke in the first person. Not the editorial first person. The first person of someone describing their own experience. I have, I tried, I noticed, my skin felt. The Reddit user used it in the first sentence. The Wirecutter reviewer used it in passing, midway through her paragraph. The Trustpilot reviewer used it three times in the six sentences.

Each cited passage was dated. The Reddit post had its date visible on the page, as did the Wirecutter article, as did the Trustpilot review. The dates were close enough to the present that the model treated them as current. None of the three were from 2022.

Each cited passage was specific in a way that copy almost never is. Cheek dermatitis through a Berlin winter. Tested against three competitors over a month. Batch number from the formulation change in early 2025. These are details that an editor would call entity-dense. They are details a copywriter would, with good intentions, smooth out, because the smoothing makes the prose feel more universal. The smoothing is exactly what makes a sentence less quotable to a reading engine.

Each cited passage had at least one verification marker. The Reddit user's three years of post history. Wirecutter's editorial process. The Trustpilot review's full surname and the platform's verified-buyer protocol. None of these are perfect signals. Each one is a signal the model can weight.

Each cited passage came from a page the crawler could actually read. The Reddit page returned its text in the initial HTML response. The Wirecutter article was a server-rendered page with the prose in the source. The Trustpilot review was, similarly, present in the initial HTML. The brand's own carousel, by contrast, lazy-loaded its reviews in JavaScript after the page had rendered. The crawler that pulls text for ChatGPT does not always wait for the carousel.

These five properties, in combination, are not a coincidence. They are the shape of writing the engine has learned, across its training corpus and its retrieval index, to prefer. If you put a passage with these properties next to a brand-marketing passage that lacks them, the engine reaches for the first one almost every time.

The pattern is not category-specific. We have run the same exercise across skincare, supplements, kitchenware, fashion, candles, pet food, and small electronics. The shapes are uncannily consistent.

Take a homewares example. You ask, this time using Perplexity rather than ChatGPT, whether a particular ceramic salt cellar is the right size for a small kitchen. The model returns four sentences. Two of the citations are from the brand's own Trustpilot. One is from a 2024 design-blog round-up. One is from the brand's product page, which is unusual; product pages get cited in about 4% of retail AI Overviews per BrightEdge's holiday 2025 audit, and this is one of them.

When you read which sentence the model pulled from the brand's product page, the finding is instructive. The model did not pull from the marketing-copy paragraph at the top, which describes the salt cellar's hand-thrown provenance, its kiln, its glaze. The model pulled from a small block of text further down the page, which contained the cellar's exact dimensions, weight, and a sentence noting that it fits a north-facing kitchen, written by the brand in its own voice as a usage note.

That sentence had the same five properties as the Reddit and Wirecutter quotations from the skincare example. First person, in the sense of being a specific account. Dated, in the sense of being recent. Specific. Verified, in the sense of being attributable to the brand on its own page. And, crucially, present in the initial HTML, not behind a JavaScript carousel.

The marketing copy at the top of the same page had none of these properties. It was, technically, the same brand voice. It was not the voice the engine read as evidence.

It is worth, briefly, working through the mechanism behind this, because it is the part most merchants find counter-intuitive and also the part that has the largest implication for how they should spend their next quarter.

Marketing copy is, structurally, optimised against the properties the engine is reading for.

It is written in third person, or in a brand-voice second person, almost never in the first person of someone with a body. There is a person and there is a product. The two are not the same.

It is rarely dated. Marketing copy is written to be evergreen. The date is the enemy of the evergreen voice, because a dated piece of copy will feel stale in eighteen months. The smoothness is, structurally, the absence of recency.

It is generalised. It speaks to many buyers at once, which means it smooths the language to fit many cases. The smoothing is the opposite of entity-density. The fewer the specifics, the wider the audience, the lower the citation likelihood.

It is unsigned. Marketing copy is written by a copywriter on the brand's behalf, but it does not carry the copywriter's name. There is no verifiable individual attached to the claim.

It is, structurally, expected to be present in the rendered page, where the rest of the marketing furniture lives. It is therefore subject to the same JavaScript-rendering pipeline as the rest of the page, which means the crawler that does not wait for JavaScript does not always see it. The marketing copy that does survive is often the part above the fold, which is the part most likely to be third-person and least likely to be cited even when seen.

All five of the properties that make marketing copy work for its original purpose, brand consistency, work against it for the purpose of getting cited. The engine has learned, over a training corpus measured in trillions of tokens, that this shape of sentence is more often selling than reporting. It treats it accordingly.

A buyer's review, on the other hand, has all five properties almost by default. It is written in the first person. It is dated. It is specific. It is verifiable. It is, if rendered properly, present in the HTML. The merchant has not had to write any of it. The buyer has done the writing on their own time, for free.

The asymmetry is large enough to be the whole strategy.

If you accept the diagnosis, the prescription follows almost on its own.

The next page your brand publishes, if it is a page intended to be cited in AI search, should look almost nothing like a marketing page. It should look like an editorial assembly of customer evidence, dated, signed, verbatim, present in the HTML, with the brand acting as a curator rather than an author. The brand's voice surfaces only in the framing sentences between the customer quotations. The customer quotations carry the citation weight.

This is uncomfortable for most marketing teams, because it places the brand's name on a page where the brand is not the loudest voice. It is also, mechanically, the page format the engine is most likely to lift from.

The replies your brand publishes, on review pages and on its own product pages, should be dated, signed, and specific. Not we are sorry to hear that. The full reply that names the batch, the variant, the formulation change, the corrective action. Each reply is a small piece of editorial content that ladders to a verifiable customer sentence. Done at cadence for a year, the replies accumulate into a dated, signed, evidentiary body of brand-customer dialogue that no competitor can fake.

The data you surface, on every product page, should be entity-dense in plain text. Dimensions, batch dates, ingredient changes, weight, country of manufacture, the dermatologist's name and the year of the consultation, written into the prose, not buried in a styled icon panel. The model reads the prose. The icon panel, as often as not, does not survive the crawl.

The carousel behind which your existing reviews currently sit should be replaced, or at least supplemented, by a server-rendered list of the same reviews in plain HTML on a crawl-friendly subpath, with the verbatim text, the date, the verified-buyer status, and the variant all present in the source.

If you have done these four things, you have done more for your visibility inside ChatGPT, Perplexity, Claude, and the Google AI surfaces than ninety percent of the merchants in your category, who will, in the meantime, still be running schema audits and refreshing their meta descriptions.

the engine the answer engine reads argues, at greater length, why this is the correct frame for understanding the new search. first person dated signed looks more specifically at the verification side of the equation. the citation economy argues that, on the open web, the citation is replacing the click as the unit of value, and what that means for who gets paid.

There is a temptation, when you start doing this exercise on your own products, to feel mildly affronted by the engine's choices. You will look at the Reddit thread it cited and think, but my page says the same thing. You will look at the Trustpilot review and think, but that customer is one of hundreds, and we have far better quotes from the carousel on our own site.

This is the right instinct followed to the wrong conclusion. The model is not telling you that the Reddit user knows more than your brand does. It is telling you that the Reddit user wrote, in the form the engine has learned to weight as evidence, a sentence that does the work. Your better quotes, the ones in your own carousel, are, in the engine's eyes, often invisible, because they are paginated behind a JavaScript button the crawler does not click.

The fix is not to argue with the engine. The fix is to put your better quotes where the engine can read them, in the form it expects. The customer who wrote four sentences after their morning skincare routine has done the hard part for you. You only have to render their writing in a way the new search can see.

That is, almost entirely, the work.