GEO vs AEO vs SEO for e-commerce brands: what actually differs

The labels "GEO" and "AEO" can suggest a new discipline with new rules. Some of the work is new; much of it is search optimization under another name, with a different scoreboard. This post separates the two, so a store can decide what to change and what to leave alone.

Are GEO, AEO and SEO different things?

SEO is work that helps pages be found and chosen in search results. Google's guidance for AI features says the best practices for SEO remain relevant for AI Overviews and AI Mode.

AEO stands for answer engine optimization. Google's guidance lists it with GEO as terms used for work focused on improving visibility in AI search experiences, and says that from Google Search's perspective this is still SEO.

GEO stands for generative engine optimization. The term comes from a 2023 research paper by Aggarwal and colleagues, which proposed a framework for content creators to improve their visibility in generative engine responses and built a benchmark of queries across multiple domains to test it. The authors report that the efficacy of the strategies varies across domains, so a result from that benchmark is not a result for product shopping.

A more useful split than the three labels is three jobs that any visibility effort has to do:

1. Be readable. The assistant's search system can fetch and understand your pages.
2. Be retrieved. Your pages, or pages about you, come back when the assistant searches.
3. Be chosen. The written answer names your brand and presents it as a pick.

SEO is mostly about the first two for Google. The labels GEO and AEO add attention to the third, and to engines other than Google.

Where do they overlap, and where do they diverge?

They overlap on the basics. Google's guidance says that to be eligible for its generative AI features a page must be indexed and eligible to appear in Google Search with a snippet, and that to maximize visibility in them content must be crawlable. Google's guidance points to clear structure, accurate facts and helpful content for its own features; whether they help in other assistants is not something this index measures.

They diverge in five ways that matter to a store.

1. Different bots. Google says that how Search finds and processes your pages remains the core of how its AI systems access your data. OpenAI documents OAI-SearchBot for surfacing sites in ChatGPT search, and says that sites opted out of it are not shown in ChatGPT search answers, though they can still appear as navigational links. Perplexity recommends allowing PerplexityBot in robots.txt so a site appears in its search results. A robots.txt file written only with Google in mind can leave the others out by accident. That is a check, not a reason to allow everything: each bot has a different documented role.

2. Different retrieval. In the index both engines ran their own web search and wrote an answer from what came back. The engines in the October 2026 sample overlapped by 47.0% on average in the brands they recommended (excluding the alternative-to-a-brand question). That is per category: the brands both engines recommended divided by the brands either recommended (Jaccard), averaged over the 160 categories with equal weight. It is not answer-by-answer agreement. So being recommended by one engine does not carry over automatically.

3. A different scoreboard. A results page lists many links. In the same sample an answer recommended 2.3 brands on average (1,271 answers, excluding the alternative question, both engines together). Winning a slot means being in a list that short.

4. Off-site pages are more visible in the sources. A product page is one place a brand shows up. The pages in the source lists also include editorial reviews and retailer listings, as the next section shows.

5. Special files are unproven. Google says you do not need new machine-readable files, AI text files or special markup to appear in Google Search. Whether other assistants read files such as llms.txt is not something this index measures.

Which tactics belong to which channel?

Table: tactics by channel. "Observed presence" means a domain appeared in the source lists of the October 2026 sample, not that it influenced a recommendation.

What does the October 2026 data suggest?

The October 2026 edition holds 1,587 answers to prompts we wrote about 160 shopping categories: 800 from OpenAI's API with web search (gpt-6-luna) and 787 from Perplexity's Agent API with the Sonar model (perplexity/sonar) and its web search tool. They record sources differently, so the charts give each engine on its own. OpenAI's list is the pages its answer links. Perplexity's list is the pages its search retrieved, not all of which the answer uses. All three markets are included, and percentages are of each engine's answers.

An own-site match is a domain whose first label equals a recommended brand's name (samsung.com for Samsung); it misses country domains, parent companies and other store names. The retailer list is hand-made and incomplete.

On the editorial side, the most frequent domain in OpenAI's linked pages was rtings.com, in 5.9% of the answers that link any page. In Perplexity's retrieved sources it was nytimes.com, in 42.3% of its answers with any source: all Wirecutter pages, in 333 of 787 answers. The websites post lists both engines' top domains.

Read these as presence, not influence. A domain in a source list does not show that the page shaped the recommendation, and the index cannot say why a page was retrieved. With that caution, three things follow for the SEO-versus-GEO question:

• For OpenAI's answers, your own site is a plausible input. In 46.6% of OpenAI's 800 answers, a linked page was on a recommended brand's own domain (a strict match), and each answer links about two pages, so a brand's own pages are among the few pages a reader sees linked.
• For Perplexity's, off-site pages are well represented. A retailer domain was in the retrieved lists of about three answers in four, and editorial review sites are among its most frequent domains. Its lists hold about 27 URLs per answer against about 2 for OpenAI, so read the two shares column by column, not against each other. Classic on-site SEO does not reach those pages.
• What to measure separately is the off-site layer. In this sample, which pages show up in each assistant's source list (on-site pages, editorial reviews, retailer listings) differed by engine. Classic SEO also works on links, PR and listings, and the index does not show that any of these pages drives recommendations.

What should an e-commerce team change?

In priority order, with an evidence note on each:

1. Measure before changing anything. Pick the questions your shoppers might ask, ask each engine repeatedly, and record the share of answers that recommend you with an interval. See the free manual method and what is AI shopping visibility. Practice, not measured here.
2. Check crawl access for each bot, not only Google, and decide per bot. Product pages AI assistants can use covers what to keep readable as text. Documented by each vendor.
3. Fix product facts at the source. Price, availability, specifications, returns and sizing on the page, in structured data, in feeds and on retailer listings should agree. See structured data for AI shopping and product feeds. Documented for Google; unmeasured for the other assistants.
4. Earn independent reviews and keep retail listings accurate. These are the pages that fill the retrieved lists. The playbook covers where to start. Observed presence in source lists, not influence.
5. Publish criteria-led guides for "what should I look for" questions. See buying guides AI can quote. The evidence is weak (3.4% of answers recommended no brand); do it for readers, not for a measured assistant effect.
6. Do not mass-produce pages for query variants. Google's guidance says it is an ineffective long-term strategy. Documented by Google.
7. Re-measure monthly with enough answers to see a change. At 10 answers one answer moves a share by 10 points; at 40, by 2.5 points. The October 2026 edition had about 10 answers per category. Statistics, not a vendor claim.

For a Shopify store, the 30-day plan puts these into an order with time estimates.

What is not known?

• Whether any tactic changes recommendations. The index shows which domains appear in the sources, not what caused an answer. No tactic above comes with a guarantee.
• How the apps differ. The data is from developer APIs, not the consumer apps. Perplexity's documentation says its API uses the same search system as its interface with differences in configuration, so outputs may differ.
• Google. The index does not measure Google's AI Overviews or AI Mode.
• How representative one month is. The October 2026 sample is about 10 answers per category and asked each question once per engine.

Frequently asked questions

Do I need an agency that sells GEO or AEO?

Ask what it will measure and how many answers it will sample. Google's guidance tells site owners considering third-party AEO or GEO advice to review its guidance on evaluating third-party SEO advice. A good brief names the questions, the engines, the counts before and after, and what the agency cannot promise.

Should I add an llms.txt file?

Google says it does not need one for Google Search. The index has not measured whether any assistant uses it, so treat it as an unproven extra and not a replacement for crawlable, accurate product pages.