Generative Engine Optimization (GEO)

GEO and AEO describe the same territory from different angles: GEO is the research community's term, AEO the industry's. The GEO study's finding is the practical takeaway — generative engines favour content that looks like evidence. Pages carrying concrete numbers, quotable statements, and named sources get synthesized into answers; vague brochure copy does not.

What the study found more precisely is worth keeping straight, because the headline number gets quoted loosely. The researchers tested a set of content modifications against generative answers and found the largest, most consistent gains came from adding citations to authoritative sources, including direct quotations, and adding statistics. Keyword stuffing and other classic SEO manipulations performed poorly or hurt. The lift varied substantially by query category — it was not a uniform 40% across everything.

The mechanism is intuitive once stated. A generative system assembling an answer has to decide which retrieved passages are safe to repeat. A sentence containing a specific figure, attributed to a named source, with a date, is low-risk to include and easy to attribute. A sentence claiming a company is "committed to excellence" carries no information and cannot be checked. The model is not rewarding good writing; it is preferring text that survives being lifted out of context.

For a local business this translates directly: publish real prices, real timelines, specific service details, and verifiable claims. Say "most residential re-roofs we do run four to seven days" rather than "fast turnaround." Say "licensed in Arizona since 2009, ROC #123456" rather than "fully licensed and insured." Each specific fact is a handle a machine can grip.

It is also why we publish our own research with the methodology attached — evidence is the currency of this channel, and a number nobody can trace is not evidence.