GEO

GEO was named by the Princeton-led research group whose paper (Aggarwal et al., KDD 2024) tested nine optimization tactics across roughly 10,000 queries. The durable finding: adding quotations, statistics and source citations to content improved its visibility in generative answers by 22–41%, while fluency tweaks and keyword stuffing underperformed. Evidence, not phrasing, is what generative engines reward — which gave the discipline both its name and its first replicable tactic.

As practiced, GEO spans both halves of how engines choose sources. On-site: extractable answer-first pages, evidence density, structured data, crawler access — the fast-moving work often labeled AEO. Off-site: earned presence on the review sites, communities and reference sources engines synthesize recommendations from, plus entity consistency everywhere those engines look. The off-site half is slower — months, not weeks — and is also the moat: a competitor can copy your page structure in a week but can't copy your citation footprint.

Terminology status, honestly: GEO won the naming contest. AEO remains useful for the on-site subset, while LLMO and AIO survive mainly in vendor copy — the practices underneath overlap substantially. Example of GEO working end to end: a regional lender publishes original first-time-buyer cost data, earns coverage in two industry outlets, and three months later Perplexity's 'best mortgage lender for first-time buyers' answers begin citing the data and naming the lender. That whole arc — create citable evidence, earn the footprint, measure the change — is our generative engine optimization service.