AEO vs SEO vs GEO for ecommerce: what is different and what D2C brands should do

SEO, AEO, GEO, AIO, LLMO. Every month brings a new acronym for getting found, and most explanations are written for publishers and SaaS companies, not for stores. For a D2C brand the question is practical: when a shopper asks Google or ChatGPT what to buy, what decides whether your product shows up, and does that need a different team, a different budget or a different plan?

This guide explains what each term means, what is the same and what is different for ecommerce, with examples, and what a D2C team should actually do about it.

The short version

• SEO gets your pages ranked in search results, so shoppers click through to your store.

• AEO (answer engine optimisation) gets your brand and products named in the answer an AI engine gives.

• GEO (generative engine optimisation) is a second name for largely the same practice, used more by researchers and agencies. AIO and LLMO are further names for it.

• The foundations overlap. Google says pages need nothing beyond normal Search eligibility to appear in AI Overviews and AI Mode. Crawlable, clear, accurate pages win in both.

• The unit of success changes. SEO counts rankings and clicks. AEO counts mentions, recommendations, citations and whether what AI says about your products is true.

• For ecommerce the big differences are product facts, merchant feeds, policies AI can quote and mentions on the sites AI trusts.

• Run it as one programme, not three. Keep the SEO basics, add the answer layer and measure both.

What each term means

SEO: search engine optimisation

Making your pages rank in a list of results. For a store that means collection and product pages ranking for searches like "linen shirts for men", and earning the click. Success is measured in rankings, impressions, clicks and revenue from organic search.

AEO: answer engine optimisation

Making your brand and products part of the answer when a shopper asks an AI engine, such as ChatGPT, Google AI Overviews, Perplexity, Gemini, Claude or Copilot. The shopper often gets a shortlist of two to five products with reasons, not a list of links. Success is being on that shortlist, described correctly.

GEO: generative engine optimisation

The same goal, named after the generative models that write the answer. The term comes from academic research on how to make content more likely to be cited in generated answers, and agencies use it widely. Where people draw a line, GEO tends to mean the content and citation side: being quoted in the pages and listicles AI draws on. AEO tends to mean the answer itself. In practice most teams use the two interchangeably.

AIO and LLMO

AIO is used for both "AI optimisation" and "AI Overviews optimisation". LLMO means large language model optimisation. Both describe the same work as AEO and GEO. If an agency sells them as separate services, ask what they would do differently. Usually the answer is nothing.

What stays the same

More than most AEO and GEO pitches admit. Google's own guidance says a page only needs to be indexed and eligible to show with a snippet to appear as a link in AI Overviews or AI Mode, and that no special optimisation or schema is required. ChatGPT relies on its own crawler, OAI-SearchBot, and on outside search results. So the basics carry across:

• Crawlable pages. If search and AI crawlers cannot reach your product pages, or your facts load only with JavaScript, you are invisible to both.

• Clear, specific content. A product page that states material, fit, size range, price and returns in plain text ranks better and gets quoted more.

• Authority. Links and mentions from trusted sites help in search, and the same sites are often the sources AI engines cite.

• Speed and freshness. Fast pages and current facts help everywhere.

If your SEO is weak, AEO will not rescue it. Google AI Overviews, and the share of ChatGPT answers built on search results, still start from search indexes.

What is different for ecommerce

1. A shortlist replaces the results page

Search gives ten blue links and room for everyone. An AI answer names a few products. Position 6 in Google still gets some clicks. Not being named in an answer gets you nothing.

2. The answer describes your product, so it can be wrong

A search result shows your own title and description. An AI answer writes its own summary of your product, often from cached pages and third-party data. It can quote last season's price, a size you no longer stock or a returns policy you do not offer. SEO never had to check that. AEO does.

3. Product data does more of the work

Shoppers ask AI for attributes: "under $80", "for oily skin", "hallmarked", "machine washable". If the attribute is not stated on your page or in your feed, the engine cannot match you to the request. Merchant feeds matter more too. Google AI Overviews and Gemini draw on Google's Merchant Center data and Shopping Graph, Copilot on Microsoft Merchant Center, and ChatGPT accepts product feeds from US merchants.

4. Policies become content

"Which brands offer free returns?" and "Does this brand do cash on delivery?" are common AI prompts. A policy in plain, quotable sentences can win the answer. A policy hidden in a PDF cannot.

5. Other people's pages decide more

AI engines lean on review sites, comparison articles, forums and marketplaces to decide who to recommend. GEO work, such as earning a place in the listicles and guides an engine cites, matters more than in classic SEO, where your own page can rank alone.

6. Results move and depend on who asks

The same prompt can name different brands an hour later, and answers can change with the shopper's location, plan and chat history. That changes measurement: trends over weekly runs, not one screenshot.

The same shopper question, three ways

Take one shopper prompt: "best linen shirt for men under $80". This is an illustrative example of how each discipline would approach it.

• SEO: rank the "men's linen shirts" collection page in Google for "linen shirt men", with a title, description and on-page copy built around that search. Success is position and clicks.

• AEO: make sure each linen shirt page states fabric, weight, fit, sizes, price and returns in plain text and Product schema, and that the price in the feed matches the store. Success is being one of the shirts ChatGPT or AI Overviews recommends, at the right price.

• GEO: find the buying guides and "best linen shirts" articles the engines cite for that prompt, and earn a place in them through reviews, samples and PR. Success is being cited by those sources, so the engines repeat you.

A D2C team needs all three for the same shopper. That is why splitting them into separate projects rarely works.

What a D2C team should do

1. Keep the SEO basics. Indexable product and collection pages, clean Merchant Center data, internal links and speed. This is still the base every AI engine builds on.

2. Let the AI crawlers in. Check robots.txt and your firewall for OAI-SearchBot, PerplexityBot and Claude-SearchBot. Training crawlers such as GPTBot are a separate decision.

3. Write product facts for answers. State the attributes shoppers ask for, in text, on every product page. Add Product schema and keep it in sync with price and stock.

4. Make policies quotable. One plain sentence each for delivery, returns, exchanges and COD.

5. Earn mentions where AI looks. Track which domains AI cites for your category's prompts and work on getting into them.

6. Measure both. Search Console for rankings and clicks, which includes AI Overviews traffic inside the Web search type, plus a fixed set of shopper prompts run weekly in AI engines to measure mentions, share of voice and accuracy.

Our guide to AEO for D2C brands covers the answer layer in depth, and running AEO like a performance channel covers the weekly scorecard.

Where Eldor fits

Eldor is built for the part classic SEO tools do not cover for stores. It tracks real shopper prompts on AI engines every week, by market (US, Europe, India) and by plan (logged out, free, paid), in clean sessions with sponsored and organic results kept apart. It checks every answer about your products against your live store and fixes the product data behind wrong answers, by enriching attributes and rewriting thin product pages.

The free Eldor AI audit is the quickest way to see where you stand. It runs 25 shopper prompts on ChatGPT and Google AI Overviews, checks 100 of your products against your live store and gives you a fix plan ranked by revenue at stake.

FAQ

What is the difference between AEO and SEO?

SEO gets your pages ranked in search results so shoppers click through. AEO gets your brand and products named in the answer an AI engine gives. They share foundations such as crawlable pages and clear content, but AEO measures mentions, recommendations and accuracy rather than rankings and clicks.

Is GEO the same as AEO?

Mostly. Both mean getting your brand into AI-generated answers. Where people separate them, GEO leans towards being cited in the content AI draws on and AEO towards the answer itself. Most teams use them interchangeably.

Is AEO replacing SEO for ecommerce?

No. Google AI Overviews and many ChatGPT answers are built on search indexes, so weak SEO limits AEO. AEO adds a layer on top: product facts, feeds, policies and mentions that AI can use.

What is AIO in SEO?

AIO is used for "AI optimisation" and for "AI Overviews optimisation". Both describe the same work as AEO and GEO: making your content and products part of AI-generated answers.

Do I need special schema for Google AI Overviews?

Google says no special schema is required to appear in AI Overviews or AI Mode. Product schema still helps engines read your price, availability and attributes, so keep it complete and in sync with your store.

How do I measure AEO results?

Run a fixed set of shopper prompts on each AI engine every week and record whether your brand is named, linked or recommended, who is named instead, which sources are cited and whether what is said about your products is true. Use Search Console for the SEO side.

Sources: Google Search Central, "AI features and your website"; OpenAI, "Overview of OpenAI crawlers"; Perplexity documentation, "Perplexity crawlers"; Anthropic support, "Does Anthropic crawl data from the web?"; OpenAI developer documentation, product feed specification; Microsoft Advertising, agentic commerce; Aggarwal et al., "GEO: Generative Engine Optimization" (2023).