What Is Generative Engine Optimization for Ecommerce?

Generative engine optimization (GEO) is the practice of improving how often and how accurately a brand, its pages and its products appear in answers written by AI systems such as ChatGPT, Google AI Mode, Gemini, Perplexity and Copilot. For an online store, that means being one of the products an assistant names when a shopper asks what to buy, with the right price and details.

The term comes from a 2023 research paper, and it has since become shorthand for a set of practices that overlap heavily with SEO. This explainer covers what GEO means for ecommerce, how answer engines choose their sources, how GEO differs from SEO and AEO, and what the evidence does and does not show, as of 22 October 2026.

What is generative engine optimization for ecommerce?

Generative engine optimization for ecommerce is the work of making a store's product data, product pages and reputation clear and trustworthy enough that AI assistants use them when they answer shopping questions.

The phrase was coined by Aggarwal and colleagues in a paper titled Generative Engine Optimization, accepted at the KDD conference in 2024. They described generative engines as systems that "use generative models to gather and summarize information to answer user queries", and framed GEO as a way for content creators to improve their visibility in those answers.

The paper was about articles and web content, not stores. The ecommerce use of the term is newer and less formal. Shopify describes GEO as optimizing your brand and products so they appear when consumers search with tools like ChatGPT, Google AI Mode, Gemini and Perplexity, and treats it as the same thing as answer engine optimization.

How does generative engine optimization work?

Generative engines answer a question by first retrieving information, then writing a response grounded in what they retrieved, so GEO works on what gets retrieved and how easily it can be used.

Google describes the retrieval step as grounding, which relies on its core Search ranking systems to fetch relevant, current pages from its index. Its AI features also use query fan-out, sending several related searches at once to gather more material for one answer. A page can therefore be cited for a question it never targeted.

Microsoft explains why accuracy matters in this step. In a May 2026 post on Bing's index, it wrote that in grounding, a stale fact produces a misleading response, and that declining to answer is a valid outcome when sources are missing or conflicting.

For a store, information reaches these engines by two routes.

• Pages the engine retrieves. Product pages, category pages, guides and third-party pages about the brand, read by crawlers such as Googlebot, OAI-SearchBot and PerplexityBot.
• Product data the store sends. Feeds and catalogs, such as Google Merchant Center and Shopify Catalog, which supply prices, stock and attributes without a crawl.

The second route is what makes GEO for ecommerce different from GEO for publishers. A product can appear in a ChatGPT or AI Mode shopping answer because of its feed, even when no page about it ranks in search.

GEO, SEO and AEO compared

There is no standard definition of GEO or AEO, and the companies that run search engines describe them differently.

Google's view is that both terms describe SEO. Its July 2026 guide states that optimizing for generative AI search "is optimizing for the search experience, and thus still SEO", and warns that many suggested hacks "aren't effective or supported". Microsoft Advertising, by contrast, presents GEO as how brands win the recommendation in AI-powered discovery, separate from SEO.

On Google, the work is largely SEO. On ChatGPT and Perplexity, which run their own crawlers and product systems, the same foundations apply but the inputs and reports differ.

What GEO changes for an online store

For a store, GEO shifts attention from single pages toward the completeness of product data, the facts on product pages and what other sites say about the brand.

Product data comes first, because assistants can only compare what the data states. Google's guide says Merchant Center feeds can help products be visible in AI responses as well as other search results. OpenAI's product feed specification lists the fields ChatGPT reads, such as brand, availability, GTIN and product category, and states that eligibility does not guarantee display.

Product pages carry the facts shoppers check. Materials, sizing, dimensions, care, shipping and returns should be stated plainly on the page, where both crawlers and shoppers can read them.

Outside mentions track with AI visibility. In an Ahrefs study of 75,000 brands, YouTube mentions and branded web mentions correlated most strongly with AI visibility, while backlinks showed a weak link. Ahrefs sells a visibility tracker, and correlation does not show cause. Google cautions that "inauthentic mentions" are less helpful than they seem, which points to earning reviews and coverage rather than buying them.

These inputs are easier to manage as one program with SEO and product feeds, so a fix to product data serves Google, ChatGPT and the product page at the same time.

What the evidence shows and where it stops

The evidence for GEO comes from one lab benchmark, a handful of academic preprints and vendor correlation studies, which together suggest directions rather than proven methods.

The original paper reported that GEO methods could raise visibility by up to 40%. That figure used the authors' own visibility measure, the share of an answer's words drawn from a source, on answers written by GPT-3.5 from the top five Google results, over a benchmark of mostly informational queries. A test on Perplexity topped out at 37%, and keyword stuffing gave "little to no improvement". None of it was tested on shopping answers or product feeds.

Studies of what AI cites in shopping answers disagree. A University of Toronto preprint found AI search leaned heavily toward earned media, with 92.1% of citations for US consumer electronics coming from third-party sources. A vendor study by LLM Pulse found brand, manufacturer and long-tail sites took 64.7% of citations in generic shopping answers. The two used different categories and methods, so neither settles the question.

Answers also change from one run to the next. In a SparkToro and Gumshoe study, the odds of ChatGPT or Google's AI repeating an identical brand list for one prompt were below 1 in 100, so GEO results have to be read as a share across many runs. An AI visibility audit for ecommerce brands shows how to set that baseline, and technical AEO for ecommerce sites covers the code side.

Frequently asked questions

Is GEO replacing SEO?

No. Google says optimizing for its AI features is still SEO, and its AI answers draw on the same index. GEO adds product data, outside mentions and AI-specific measurement on top.

Do we need an llms.txt file for GEO?

Not for Google. Google's guide says its Search ignores llms.txt files, and that they neither help nor harm visibility.

How long does GEO take to show results?

No independent study gives a timeline. Judge results as a share across many repeated runs, tracked over time.

Can a brand pay to appear in AI shopping answers?

Paid placements now exist alongside organic results on some platforms, including product ads in ChatGPT built from feeds. In OpenAI's feed, ad eligibility is set separately from organic eligibility.