Contents
The definitionWhat drives citationA worked exampleThe citation auditQuestionsRelated service
Shopify Agentic Commerce →The definition
Generative engine optimisation is the practice of building the signals that make an AI model name your brand when it answers a question in your category. Where SEO earns a position on a results page the user chooses from, GEO earns a place in the answer itself, among the handful of sources a model cites or the two or three brands it recommends.
The term has a specific origin: a Princeton-led research paper posted in November 2023 and presented at KDD 2024, which coined the name, built a benchmark of ten thousand queries, and measured which content changes moved visibility in generative answers. The headline finding was a lift of up to 40 percent from the right changes, and the detail of which changes worked is more useful than the headline.
GEO is one of the three surfaces a discovery-ready store prepares for, and most of it is an extension of the same foundational work as SEO, applied with an understanding of how models rather than ranking systems evaluate authority and relevance.
The shift from ranking to recommendation
Traditional SEO produces a list of results and the user selects one. AI models produce a recommendation, so the selection happens on the user's behalf. The recommendation layer went mainstream in two waves: Google announced AI Overviews for all US users in May 2024, and ChatGPT search launched in October 2024, was extended to every logged-in free user in December, and opened to everyone without sign-up in February 2025.
The behavioural evidence says the layer changes what users do. Pew Research Center tracked real browsing and measured the click-through on visits with an AI summary at 8 percent, roughly half the 15 percent seen without one. The click volume SEO was built to capture shrinks on answered queries, and what replaces it is presence in the answer.
The distinction changes the nature of visibility. Getting onto a results page requires relevance for a specific query, while getting into an AI recommendation requires that the model has formed a view of the brand as the appropriate answer for a whole category of questions. That is a different type of signal, and it takes a different approach to build.
The commercial stakes rose again when the answers gained checkouts. OpenAI launched Instant Checkout in September 2025 with Shopify merchants among the first wave, and the durable pattern across platforms is discovery in the AI surface with purchase on the brand's own store, which makes the recommendation the new front door.
What the paper measured
The GEO paper tested nine content interventions against its benchmark and measured how each changed a page's visibility in generated answers. Three worked consistently: adding citations to credible sources, adding quotations from relevant authorities, and adding statistics where the content could support them. Each lifted answer visibility by meaningful margins, and they worked best in combination.
Equally instructive is what did not work. Keyword stuffing, the reflex tactic imported from old SEO, produced no gain in the paper's testing, and neither did superficial fluency edits. The pattern behind the results is that generative engines reward content that demonstrates its claims, because verifiable, attributed, specific material is what a model can safely build an answer from.
We treat the paper's findings as directional rather than gospel, since it tested informational queries in a research pipeline, at a remove from live commercial answers. Its core direction, evidence-dense content wins citations, has held up in everything we have observed since.
What drives AI citation for a brand
Entity clarity. Does the model know, with confidence, what the brand is, what it sells and who it is for? That certainty is built through consistent signals across the brand's own domain, third-party mentions, directory listings and structured data, and it is the precondition for everything else, because a model does not recommend what it cannot confidently identify.
Authoritative specificity. Models reward content that answers specific questions with real depth, so a page that thoroughly answers 'what is the difference between a waterproof and a water-resistant boot for trail walking' will be cited more often than a page with the same keywords and shallow content. Answer-first structure matters here for the same reason it matters in featured snippets: retrieval systems weight the opening of the content heavily.
Source authority. Models weight signals from sources they consider authoritative, which is where press coverage, citations in established publications and backlinks from strong domains come in. This is the earned media component of GEO, it is why the practice overlaps so heavily with traditional authority building, and it is the slowest signal to build, which makes it the one to start earliest.
A worked example: making one claim citable
The paper's three winning interventions are easiest to see applied to a single sentence. A typical brand claim reads: "Our merino base layers are exceptionally warm for their weight." A model can do nothing with that, because it is unverifiable and every competitor says something identical.
The evidence-dense version: "Merino fibre insulates at a finer diameter than synthetic fills, which is why an 18.5-micron merino base layer at 200gsm outperforms a polyester equivalent of the same weight in still-air warmth, a property documented across textile research and the reason expedition outfitters have specified merino since the early twentieth century." Specific numbers a buyer can check, a mechanism, an attributable history: three things a model can lift into an answer with your brand attached.
Run that transformation across a store's buying guides and category explainers and you have applied the paper's findings without a single new page. The discipline is having the evidence, which for most premium brands already exists in product development and never made it into the copy.
Applying GEO to a Shopify store
- Buying guides and category explainers carry the citation load, because they are the pages that answer the questions models get asked. Evidence-dense, answer-first, one query cluster each.
- Collection page FAQs feed both the browsing customer and the machine readers, marked up where visible.
- The About page and brand story anchor the entity: founding facts, provenance claims and credentials stated plainly, because these are the sentences models paraphrase when describing the brand.
- Structured data and the agentic feeds keep the machine-readable facts consistent with all of the above, per the one-fact-everywhere rule.
In the order of work, the entity and structured-data pieces come first, the evidence-dense content second, and the earned authority runs continuously alongside, because its lead time is the longest.
How GEO differs from SEO in practice
| Dimension | SEO | GEO |
|---|---|---|
| The unit of success | A ranking position for a query | A citation or recommendation in an answer |
| What gets evaluated | A page, for relevance and authority | A brand entity, across everything the model has read |
| Where the work lands | Your own domain first | Your domain plus the third-party sources models trust |
| Measurement | Rankings, clicks, Search Console | Answer presence sampled by query set, brand mention accuracy |
| Feedback speed | Weeks, with tooling | Slower and noisier, sampled rather than tracked |
The overlap is deliberate and large: architecture, structured data, specific content and earned authority feed both. What GEO adds is the brand-level lens, since models answer category questions with brands rather than URLs, and the third-party layer, because much of what a model believes about a brand was learned somewhere other than the brand's own site.
The citation audit
The starting point for GEO work is understanding where the brand currently appears, on which platforms, and for which queries. Our audit covers ChatGPT, Perplexity, Google AI Overviews and Kimi, using ten buying queries in the brand's commercial category and comparing citation rates against three competitor brands.
The mechanics matter for repeatability. The same queries, phrased the way buyers phrase them, run on a schedule, with the answers recorded: which brands are named, how each is described, and which sources the answer cites. The description language is diagnostic in itself, because a model describing a brand in its own words is usually paraphrasing the brand's most authoritative coverage, which tells you where the model learned it.
That establishes the baseline, and the gap between where the brand appears and where its competitors appear is the territory the GEO programme goes after, with the competitor diagnosis determining which signal gap to close first.
What GEO will not do
It will not produce guaranteed placements, because nobody controls a model's answers, and anyone selling guaranteed AI citations is selling something they do not have. The honest offer is a shifted probability: evidence-dense content, a clean entity and real authority make citation more likely across thousands of answer generations.
It will not replace the traffic AI answers absorb, and the referral clicks from AI surfaces are currently small for most brands. The value today is presence at the research moment and accuracy when the brand is discussed, with the transaction still closing on the store, which is why GEO work and store fundamentals are one programme rather than competitors for budget.
And it is not a one-off project. Models retrain, answer formats change quarterly, and the query set that matters moves with the category. GEO is a monitored, maintained practice, which is the same thing that has always been true of search.
Questions we hear about GEO
No, and be wary of anyone selling them as separate retainers. The foundations are shared, and what matters is that whoever owns your search also owns the brand-level and third-party work GEO adds, because splitting them splits the entity they are both building.
No, and nobody honest can. Answers vary by phrasing, user and model version. What the work changes is the probability and the accuracy of your appearances, both of which are measurable across a repeated query set.
A fixed query set run on a schedule across the platforms your buyers use, recording brand presence, description accuracy and cited sources. Alongside it, watch branded search volume and direct traffic, because AI recommendations often convert as a later branded visit.
It supports the entity clarity models need, and it is not a citation shortcut. Structured data makes the brand and its products unambiguous to machines, which raises the confidence a model has when naming you. The citation itself is earned by content and authority.
Content and entity fixes can surface in answers within weeks because retrieval-based systems read the live web. Authority signals move over months. Treat a quarter as the honest review cycle and a year as the horizon for category-level change.
Sources & references
- Aggarwal et al., GEO: Generative Engine Optimization (KDD 2024)(arxiv.org)
- OpenAI, Introducing ChatGPT search(openai.com)
- Google, Generative AI in Search: Let Google do the searching for you(blog.google)
- Pew Research Center, Google users are less likely to click on links when an AI summary appears(pewresearch.org)
- OpenAI, Buy it in ChatGPT: Instant Checkout and the Agentic Commerce Protocol(openai.com)
