What is GEO? A working definition for marketing teams
Generative Engine Optimization in plain terms: what changes when buyers ask AI instead of searching, what drives inclusion in answers, and how to start measuring.
Buyers have started asking AI assistants the questions they used to type into Google. "Best CRM for a small agency." "Alternatives to Intercom." "Is X trustworthy?" The answer comes back as a short, confident paragraph that names two to five brands — and most teams have no idea whether they are in it.
Generative Engine Optimization (GEO) is the practice of earning a place in those answers. This post gives you a working definition and a starting point for measuring where you stand.
Search rankings and AI answers are different games
SEO optimizes for a ranked list of links. GEO optimizes for inclusion in a synthesized answer. The differences are structural, not cosmetic:
| Classic search | AI answers | |
|---|---|---|
| Result | 10 blue links | One answer naming 2–5 brands |
| Visibility unit | Your page's position | Whether you're named or cited |
| Signal source | Links, keywords, site authority | Citable sources, third-party corroboration |
| Feedback loop | Rank trackers, Search Console | Nothing built in — you have to ask the engines |
A page that ranks #3 can still be invisible in the AI answer for the same question. The reverse also happens: brands with modest search rankings get named consistently because the sources engines trust — review sites, comparison pages, community threads — mention them.
What actually drives inclusion
Across ChatGPT, Gemini, Perplexity, and Google's AI surfaces, the same handful of factors shows up when a brand gets named:
- Citable pages. Engines quote pages that answer a question directly — comparison pages, pricing pages, honest "best X for Y" content. Marketing copy that talks around the question doesn't get cited.
- Third-party corroboration. An engine is far more likely to name you when independent sources — reviews, directories, Reddit threads, industry lists — agree you belong in the category.
- Entity clarity. The engine has to know what you are. Consistent naming, a clear category description, and structured data reduce the chance you're skipped or confused with someone else.
- Freshness where it matters. For "best X in 2026" prompts, engines lean on recently updated sources.
None of these are tricks. They are the same trust signals a careful human researcher would use — engines have just made them machine-readable at scale.
What doesn't move the needle
Keyword stuffing, invisible text aimed at crawlers, and mass-generated pages tend to do nothing — or get your sources classified as low-quality. Engines synthesize from pages they consider trustworthy; volume without trust is noise.
How to measure where you stand
You can't improve what you don't observe, and AI answers give you no analytics by default. The working method:
- Write down the 20–50 questions your buyers actually ask when they're choosing in your category.
- Run them against the engines your buyers use — on a schedule, not once. Answers change week to week.
- Record three things per answer: were you named, were you cited as a source, and who else was named.
- Track your share of voice against those competitors over time, and note which pages the engines cite — those are the surfaces worth improving first.
Doing this by hand for one engine is a spreadsheet afternoon. Doing it weekly across five engines is why measurement tooling exists — but the method matters more than the tool.
Where to start
Start with one question: when a buyer asks AI about your category, are you in the answer? If you don't know, that's the gap. Pick your ten most valuable buyer prompts, run them against ChatGPT and one other engine, and write down who gets named. Everything else in GEO — content fixes, source outreach, structured data — follows from what you find.