What it means
The term was popularised by a 2023 research paper, 'GEO: Generative Engine Optimization', which described generative engines as search engines that use generative models to gather and summarise information in answer to a query. The paper studied how changes to source content affect its visibility in those answers.
In industry use, GEO and AEO describe largely the same work. GEO tends to emphasise answers written by large language models and the sources they cite; AEO grew out of featured snippets and voice answers. Neither is a formal standard, so ask any vendor exactly what it measures and changes.
Why it matters
The label matters less than the measurement behind it. A GEO programme is useful only if it defines the buyer questions, engines and success metric in advance, for example how often the brand is recommended rather than merely mentioned.
Example
A team selling contract management software runs 30 unbranded buyer questions across three assistants every quarter. After publishing a clear comparison page and correcting outdated feature claims on review sites, it measures again under the same conditions instead of trusting one improved answer.
Common misunderstandings
- GEO does not replace SEO: many answer engines retrieve from search indexes, so crawlable, indexable pages remain a precondition.
- A higher mention count is not the goal on its own; being recommended for the right questions is.
- Gains reported by one study or tool are not a benchmark for every category or engine.