How do eCommerce brands get recommended by ChatGPT, Gemini and Perplexity?
When a shopper asks ChatGPT for the best trail running shoe or the lightest e-bike for commuting, the answer names a handful of brands. The rest do not exist in that conversation.
That shortlist is not random. It is built from what the engine can find, what it can understand, and what it can confirm from more than one source. The work of getting onto it is called answer engine optimization (AEO), or generative engine optimization (GEO). We treat it as one of the five things we run for a brand, alongside the store, the marketplaces, paid media and the P&L.
How is this different from SEO?
Classic SEO competes for a position on a page of ten blue links. The buyer still clicks, compares and decides.
An AI answer does the comparing for them. It reads a set of sources, forms a view, and names a few brands with a reason attached. You are not competing for a ranking. You are competing to be the brand the engine is confident enough to name.
The foundations overlap. Crawlable pages, clear structure and real authority still matter. What changes is the unit of competition: a single, well-supported answer to a specific question, instead of a page tuned to a keyword.
What do AI engines look for before they recommend a brand?
In our work, three things decide it.
Findability. The engine has to be able to reach and read your pages. That means no blocked crawlers in robots.txt, product and category pages that render their content without heavy scripts, and a clean sitemap. An llms.txt file that summarizes what you sell and who it is for helps too.
Clarity. The engine has to understand exactly what you are. Pages that state plainly what the product is, who it is for, what it costs and how it compares get used. Pages that lead with brand poetry and hide the specifics do not.
Corroboration. The engine wants the same facts from more than one place. Your site says you are the lightest folding bike under $1,500. Do your Amazon listing, your reviews, a trade publication and a Reddit thread say something similar? Consistency across sources is what turns a claim into something an engine will repeat.
Where do AI answers get their information?
Each engine blends two sources: what it learned in training, and what it retrieves live when the question is asked. Perplexity and ChatGPT search lean heavily on live retrieval and show their citations. Gemini draws on Google’s index.
The practical takeaway is that the cited sources are visible, so you can study them. For most product categories the answers pull from a mix of brand sites, marketplace listings, review sites, editorial roundups, and community discussion like Reddit. If your brand is absent from all of those, no amount of on-site work will fix it on its own.
What can a brand do this quarter?
Start with the questions, not the pages.
- List the 30 to 50 questions buyers actually ask before buying in your category. Include the comparison questions (“X vs Y”), the fit questions (“best for small apartments”) and the objection questions (“is it worth the price”).
- Ask them. Run each question through ChatGPT, Gemini and Perplexity and record who is named and which sources are cited. This is your baseline, and it is usually sobering.
- Write the answers. Build pages or sections that answer each high-value question in the first two sentences, then back it up with specifics. Short answer first, detail second.
- Make the facts match everywhere. Product names, specs, pricing and claims should agree across your store, Amazon, Walmart and any retailer pages.
- Earn the outside mentions. Pitch the roundups that keep getting cited, answer honestly in the communities where your buyers ask, and ask customers for reviews that mention the specific use case.
- Re-run the questions monthly. Citation share moves, and you want to know which work moved it.
How do you measure AEO?
The core metric is citation share: across a fixed set of buyer questions, how often is your brand named, and how often is your site one of the cited sources, compared with your top competitors.
Track it by engine and by question type. Pair it with the downstream signals you can see: referral traffic from chatgpt.com and perplexity.ai in your analytics, branded search volume, and what new customers say when you ask how they heard of you.
It will not be as tidy as a keyword ranking report. It is still far better than guessing. For the whole discipline in one place, see our guide to the Answer Layer.
Frequently asked questions
Is AEO the same as GEO?
They describe the same goal from slightly different angles. AEO (answer engine optimization) focuses on being the answer to a question. GEO (generative engine optimization) focuses on being included in AI-generated responses. In practice the work is the same.
Can you pay to be recommended by ChatGPT?
Not in the organic answer. Some AI platforms are testing ad placements, but the recommendation itself is earned from the sources the engine reads and trusts.
Does AEO replace SEO?
No. Most of what makes a page good for AI answers, clear structure, real specifics and genuine authority, also makes it good for search. AEO adds the question-first writing, cross-source consistency and citation tracking on top.
How long does it take to see results?
Engines that retrieve live, like Perplexity, can reflect new pages within weeks. Changes to what a model learned in training take longer and depend on its update cycle. Plan in quarters, and measure monthly.