What the terms mean
AEO — answer engine optimisation. Being the source that gets used when a question is answered directly: featured snippets, AI Overviews, voice assistant responses, the knowledge panel. The user gets their answer without clicking anything.
GEO — generative engine optimisation. Being cited by generative systems — ChatGPT, Perplexity, Gemini, Copilot — when they compose an answer about your category in your area. Different mechanics, same consequence.
Neither replaces local SEO. Both increasingly sit in front of it.
Why local is affected first and hardest
Local questions are exactly the shape these systems answer best. "Who's open now." "Who does emergency work near me." "Which one is cheaper." Short questions with factual answers, where the user wants a recommendation rather than ten links.
And the systems answering them lean heavily on structured, corroborated data — which is to say, the same profile completeness, listing consistency and review signals that local marketing has always been about. The work overlaps more than the terminology suggests.
What actually influences it
Consistency across sources
Generative systems corroborate. A fact that appears identically across your profile, your website, and several independent directories is treated with more confidence than one that appears once or appears three ways. This is citation hygiene, with higher stakes than before.
Explicit, extractable answers
Content that states things plainly gets used. A page that says "we offer emergency service 24 hours a day in [area], typically arriving within [timeframe]" is extractable. A paragraph of positioning language around the same fact is not.
Structured data
Schema markup is how you hand a machine the facts without asking it to infer them. LocalBusiness, Service, FAQ, Product, OpeningHoursSpecification. Under-implemented by most local businesses and increasingly consequential.
Reviews as evidence
Generative answers about local businesses draw on review content — not just the rating, the text. What customers repeatedly say about you becomes what the machine says about you. That makes review volume and substance a content strategy, not only a reputation one.
Entity clarity
These systems reason about entities — a business, its category, its location, its relationships. Ambiguity hurts. Duplicate profiles, inconsistent naming, unclear category assignment and conflicting addresses all make you harder to model confidently, and a system that isn't confident recommends someone else.
How we approach it
- Establish the factual base. Profile and listing consistency first. Nothing else works on top of contradictory data.
- Make the facts extractable. Plain statements of what you do, where, when and for whom — on pages, in structured data, in the profile.
- Build corroboration. The same facts, consistently, across independent sources.
- Test what the machines say. Ask the assistants the questions your customers ask, in the places they ask them, and see what comes back. This is measurable, and most businesses have never checked.
- Watch for drift. These systems update. What they say about you in March is not what they'll say in September.
The honest position: this field is young and anyone claiming a reliable playbook is ahead of the evidence. What we can say is that the inputs which improve answer-engine presence overlap heavily with the inputs that have always driven local visibility — and that testing what the assistants currently say about you is cheap, informative, and almost nobody is doing it.