What Is GEO? A Plain-English Guide to Generative Engine Optimization

A specific set of content modifications boosts AI citation rates by up to 40% — adding statistics, citing sources, and adding expert quotations. That finding comes from a 2024 ACM SIGKDD paper by researchers at Princeton, IIT Delhi, and the Allen Institute for AI. The same paper introduced the term Generative Engine Optimization (GEO) and the benchmark used to measure it.

GEO is the discipline of structuring content so AI platforms cite your brand in their answers. It's the same discipline B2B practitioners call AEO (answer engine optimization). Two terms, one set of mechanics. The reason both exist is that the academic and practitioner communities arrived at the work from different directions and named it differently. This article walks through what the original paper measured, what it found, what it skipped, and how to apply the findings without re-running the study.

In brief: Generative Engine Optimization (GEO) is the practice of structuring content so large language models — ChatGPT, Perplexity, Gemini, Claude, Copilot — cite your brand in their answers. The term was coined in a 2024 Princeton / IIT Delhi / Allen Institute research paper and describes the same discipline practitioners call answer engine optimization (AEO).

What GEO Is (and Where the Term Came From)

A generative engine is any AI system that returns a synthesized answer instead of a list of links. ChatGPT, Perplexity, Gemini, Claude, Microsoft Copilot, Google's AI Mode and AI Overviews, Grok, Meta AI — all generative engines. Ask any of them "who are the top vendors of [your primary service] for [your industry]?" What comes back is a short list of named brands. There's no page two of an AI answer. Either your brand is in the list or it isn't.

GEO is the work of getting into that list, evaluated empirically.

The term has a citation. "Generative Engine Optimization" was introduced in "GEO: Generative Engine Optimization" by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, and Ameet Deshpande, published at the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining in 2024. The paper introduced the framework, defined evaluation metrics, built a benchmark called GEO-BENCH, and ran controlled experiments on which content modifications actually move citation rates. That research is the reason the acronym exists. Wikipedia's entry on generative engine optimization cites the paper directly. Most of the vendor content currently ranking for "what is GEO" does not engage with it.

One quick disambiguation. "Geo" also means: the Greek prefix for earth or ground (geography, geology); a label for geographic data formats in computing (GeoJSON, Well-Known Text); the NYSE ticker for The GEO Group, a private prison and detention services company. This article is about none of those.

What the GEO Paper Actually Tested

Aggarwal et al. built GEO-BENCH, a benchmark of diverse queries spanning multiple domains, then used it to measure how much different content modifications change a piece of content's visibility inside generative AI responses. They evaluated multiple optimization strategies under controlled conditions and reported the citation-rate change for each.

The strongest finding, on the paper's primary metric: adding statistics, adding expert quotations, and citing sources lifted citation visibility by up to 40%. That number is the one most subsequent vendor content quotes; what's less often quoted is the rest of the table.

A few of the less-discussed findings:

Keyword stuffing did not help. The paper tested keyword-density manipulation as an optimization and found it produced no positive effect on AI citation. The keyword-density playbook from traditional SEO does not transfer to generative engines. Content that reads as a list of repeated phrases is structurally less extractable, not more.

Fluency improvements helped, but less than substantive additions. Cleaning up grammar, simplifying sentence structure, and improving readability did move citation rates upward, just not as much as adding statistics, quotations, or citations. The most effective optimizations changed the substance of the content, not just its surface.

Citing other sources lifted citation of your own content. This is the counterintuitive one. Adding citations to outbound sources — pointing the AI at where your claims came from — increased the AI's likelihood of citing you. The mechanism is plausible: a paragraph attached to attributable claims looks more reliable to a retrieval system than the same paragraph without them. The implication runs against the SEO instinct to hoard outbound links.

The study used two metrics. Subjective impression asks whether the AI's response prominently mentioned a piece of content. Position-adjusted word count measures how many of the source's words made it into the response, weighted by where in the response they appeared. The 40% headline figure is on a position-adjusted metric, meaning the modified content didn't just get mentioned — it occupied more of the AI's answer.

Limits worth naming. The benchmark was tested on the generative engines available in 2024; the engine landscape has continued to shift. The optimization strategies were largely tested independently rather than stacked, so the paper doesn't fully report what happens when you apply three or four together. Coverage was broad but not exhaustive across domains. And as with any single study, the magnitude of any specific lift will vary across content types, query types, and engines. The paper's framework is more durable than any individual point estimate inside it.

Why "GEO" and "AEO" Describe the Same Discipline

If you've read anything else about AI marketing in the last year, you've seen "GEO" and "AEO" used as if they might be different things. They aren't. Same mechanics, same outcomes, two communities of origin.

GEO is the academic and research term. Aggarwal et al. (2024) named it. Wikipedia uses it. Google AI Overviews surface it. Search engine vendors and analyst firms tend to prefer it because it aligns with the peer-reviewed literature.

AEO is the practitioner term. B2B marketers, agencies, and operators on the implementation side — the people running audits, shipping schema, tracking citations — mostly call it AEO. The keyword data backs this up: "aeo agency" gets searched by people hiring agencies; "generative engine optimization" gets searched by people writing think pieces about the category.

Contentful's article on GEO (June 2025) states the alignment plainly: "GEO is also known as answer engine optimization (AEO)." Ann Smarty's "SEO vs GEO: Stop Choosing Sides!" makes the same point at length. Chris Donnelly's LinkedIn piece on the three terms treats SEO, AEO, and GEO as a stack — SEO is the foundation; AEO and GEO are the citation-focused layer on top.

The terminology choice is mostly a signal of which audience the writer is talking to. If you're reading an analyst report, an academic post-print, or vendor positioning aimed at search-engine technical audiences, you'll see GEO. If you're reading agency content, B2B operator pitches, or practitioner conferences, you'll see AEO. Pick the one your buyers use and don't lose a meeting over the choice. The work is identical either way. What Is AEO? — the practitioner-side companion to this article, also in this AEO cluster — covers the same mechanics through the B2B marketing lens.

Why GEO Showed Up in the Conversation in 2025-2026

Three shifts collided in the last twelve months. The same three created the AEO category at the same time, because GEO and AEO are siblings reacting to identical market conditions.

The audience moved. Seventy-eight percent of companies now use AI, up from 55% in 2023 — McKinsey's State of AI. ChatGPT alone has approximately 900 million weekly active users. Perplexity serves 45 million monthly active users. More than 1 billion people worldwide are using AI tools regularly. B2B buyers are in that number, doing purchase research inside generative engines — HubSpot reports that 42% of its own buyers consult answer engines before they ever land on a vendor website.

The clicks disappeared. 58.5% of US Google searches end without a click to any website (SparkToro's 2024 zero-click study). When Google AI Overviews appear on a query, the zero-click rate jumps to 83%. The rank-click-visit model that B2B marketing was built around stops working when the answer is delivered before the click.

The infrastructure materialized. HubSpot launched a dedicated AEO product in April 2026. Forrester published a playbook telling marketers to "format content in short, simple answers full of unique quotes and stats" — which lines up cleanly with the Aggarwal et al. statistics-and-quotations finding. Several AI visibility monitoring startups (Profound, Sitefire, Geneo, RankLens, GetGPTScore, MentionedBy.ai) launched in the same window. The category has tooling, vendor coverage, and analyst frameworks. It's no longer experimental.

The demand signal in search data confirms it. Monthly searches for "what is geo" grew from roughly 1,300 in April 2025 to 12,100 in August 2025, settling at 6,600 on the most recent reading (Igility DataForSEO analysis, April 2026). "What is geo in marketing" is up 1,340% year over year. "Geo vs aeo" is up 9,900% year over year from a tiny base — the curve a term produces when it's going from zero to something.

How LLMs Actually Decide What to Cite (the Short Mechanism)

The full mechanism — what an LLM does to pick which brands appear in its answer — is detailed in the AEO pillar guide and the practitioner walkthrough at What Is AEO?. The summary, for readers who want the structural picture before the long version:

Extractable statements outperform prose. This is the Aggarwal et al. finding. Self-contained sentences with statistics, named sources, and attributable claims get extracted into AI responses far more reliably than dense paragraphs of unattributed assertion. Up to 40% lift on the paper's primary metric.

ChatGPT and Copilot retrieve from Bing, not Google. Seer Interactive ran 68 identical queries through ChatGPT and mapped them against Bing's and Google's top results. Bing won the alignment in 87% of cases. ChatGPT triggers what Seer calls "fanout queries" — three to ten related searches against Bing's index — and the brands ranking in those fanout queries are the brands ChatGPT cites. A company that dominates Google for its category but hasn't thought about Bing can be invisible inside ChatGPT.

Structured data is the citation mechanism. Pages with schema markup are 36% more likely to appear in AI-generated summaries, and structured pages earn 2.8x higher AI citation rates than unstructured ones. Sixty-five percent of pages cited in Google's AI Overviews include structured data. Both Google and Microsoft officially confirmed in March 2025 that their AI systems use schema markup during response generation.

Authority compounds across the broader web. AI systems weight authority signals across the sites they already trust. A brand with strong G2 reviews, a Wikipedia page, analyst coverage, and named authors with verifiable credentials gets cited at a materially higher rate than a brand with great on-site content and no off-site presence. The engines triangulate.

Four decisions, all structural. The mechanisms — sitemaps, schema markup, named-author bylines, third-party reviews — have been around for a decade. Only the retrieval index and the crawler list are new.

GEO vs AEO vs SEO (the Short Version)

The detailed head-to-head belongs in its own piece — look for AEO vs SEO vs GEO: The 2026 Comparison in this same series. The three-line version:

Rank vs. cite. SEO optimizes for position in a list of blue links. GEO and AEO optimize for inclusion in an AI-generated answer. A #1 Google ranking and a ChatGPT citation are different outcomes. The same content can earn one without earning the other.

Click vs. decision. A Google search returns ten options for the user to evaluate. An AI answer returns a recommendation. ChatGPT's top recommendation becomes the user's top pick 74% of the time, per Position Digital — not "influences." Becomes.

One engine vs. eight platforms. Google is one retrieval system. GEO/AEO spans ChatGPT (Bing-sourced), Perplexity (own index plus multiple sources), Gemini (Google plus Knowledge Graph), Claude (training data plus some web), Google AI Overviews (Google index), Microsoft Copilot (Bing plus Microsoft Graph), Grok (X plus web plus Bing), and Meta AI (web plus Meta content). Each platform weights different signals. A page that ChatGPT cites can be invisible to Perplexity.

GEO does not replace SEO. It's a layer on top of SEO foundations. Strong Google rankings (and especially strong Bing rankings) still feed GEO outcomes. Schema markup helps both disciplines simultaneously. The additional investment goes into multi-platform visibility tracking, Bing-specific optimization, off-site corroboration, and the structural content changes that make pages extractable as answers rather than just crawlable as documents.

The day-to-day work differs. SEO ships pages optimized for keyword targeting, internal linking, page speed, and Core Web Vitals — all aimed at moving up Google's ranking algorithm. GEO ships the same foundations plus schema markup that defines entities and relationships, citation-ready snippets extractable as answers, named author bylines for E-E-A-T signal, off-site corroboration across G2 / Wikipedia / analyst coverage, and Bing-specific submission so ChatGPT and Copilot can index the content at all. The tooling overlaps but only partially. A team running pure SEO without the GEO layer can rank #1 on Google and still be invisible inside ChatGPT's recommendation list.

What a GEO Audit Reveals (Three Recurring Findings)

A GEO audit is a diagnostic of how well AI systems can find, understand, and cite your content. Run across enough sites, three categories of finding dominate.

Crawler access gaps. B2B sites often block AI crawlers at the CDN or robots.txt level without realizing it — a default Cloudflare Bot Fight Mode rule, a stale Disallow: / line, a WAF challenge that returns a JavaScript check instead of a 200 response. (Bot Fight Mode is on by default, which is a defensible security choice; the engineers who turned it on usually aren't the same people who later care whether ChatGPT can read the marketing site.) GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and Meta-ExternalAgent all need explicit allowances at both the robots.txt level and the upstream bot-management layer. If the crawler can't reach the page, no amount of structured data helps.

Content format gaps. Pages that read fine to a human are often unextractable to an AI. Missing "In brief" summaries, entity definitions buried five paragraphs deep, hedging language ("some experts believe," "may be"), FAQ sections written as prose rather than structured Q&A — all depress citation probability. The Aggarwal et al. statistics-and-quotations finding measures exactly this category of gap. Forrester's playbook leads with "short, simple answers full of unique quotes and stats" for the same reason.

Platform-specific blind spots. A brand can be visible in one AI system and invisible in the next. The most common single finding in B2B audits: a brand that ranks well on Google, has never submitted its sitemap to Bing, and is therefore invisible to ChatGPT and Microsoft Copilot simultaneously. The fix is structural — submit the sitemap to Bing Webmaster Tools — and citation probability compounds across the Bing-fed platforms for months.

The audit applied to a live healthcare engagement is documented in the Helmer Scientific case study — compliance architecture plus clinical-register content plus Clutch third-party attestation, applied as a multi-platform AEO build.

Your Next Step

If you've come this far, you're working from primary research rather than vendor positioning. The natural next step depends on what you're trying to do.

For the full multi-platform implementation methodology that translates the Aggarwal et al. findings into a working build across all eight AI engines: Is AI Eating SEO? The B2B Guide to Answer Engine Optimization. The pillar covers schema markup specifications, crawler-access checklists, off-site corroboration patterns, and the eight-platform measurement framework.

For the practitioner-side walkthrough of the same mechanics — same data, same mechanism, framed for the operator audience: What Is AEO? A Plain-English Guide for Marketers (publishing soon as part of this same AEO series).

For the empirical run on your own corpus — Aggarwal et al.'s methodology applied to your domain, scored across all eight AI platforms, benchmarked against your specific competitor set: request an AEO audit from Igility. The output is a ranked list of the modifications that move citation probability most for your content, not a generic score.

For ongoing implementation of the methodology across content marketing, schema, and off-site corroboration: Igility's SEO/AEO services. The audit identifies the gaps; the engagement closes them.

Frequently Asked Questions

What does GEO stand for?

GEO stands for Generative Engine Optimization. The acronym was coined by Aggarwal et al. in their 2024 ACM SIGKDD paper of the same name, which introduced the framework, defined evaluation metrics, and built the GEO-BENCH benchmark used to measure how content modifications change AI citation rates.

What is GEO vs SEO?

SEO optimizes for position in a list of blue links — Google ranks ten options and the user evaluates them. GEO optimizes for inclusion in an AI-generated answer — ChatGPT, Perplexity, Gemini, and Copilot return a recommendation rather than a list. The same content can earn a #1 Google ranking and zero ChatGPT citations, or vice versa. GEO is a layer on top of SEO foundations, not a replacement: strong Google rankings (and especially strong Bing rankings) still feed AI citation, but the additional investment goes into structured data, multi-platform visibility tracking, off-site corroboration, and content that's extractable as answers rather than only crawlable as documents.

Is GEO the same as the GEO Group, geography, or geosynchronous orbit?

No. "Geo" also refers to the Greek prefix for earth or ground (geography, geology), geographic data formats in computing (GeoJSON, Well-Known Text), the NYSE ticker for The GEO Group (a private prison and detention services company), and geosynchronous Earth orbit in aerospace contexts. This article is about Generative Engine Optimization — the AI-citation discipline introduced by Aggarwal et al. (2024).

Why do academics call it GEO instead of AEO?

The academic literature settled on "Generative Engine Optimization" because the foundational 2024 ACM SIGKDD paper by Aggarwal et al. used that term. Once a peer-reviewed paper names a discipline, downstream academic and analyst content tends to align with that terminology. The practitioner side — agencies, operators, marketing teams — converged on "answer engine optimization" (AEO) independently, drawing the analogy to the existing acronym SEO. Both terms describe the same work: structuring content so AI systems cite the source in their generated responses. The naming difference is mostly a signal of which audience the writer is targeting.

What did the original GEO paper actually find?

The Aggarwal et al. paper built a benchmark called GEO-BENCH, applied it across multiple domains, and tested how much different content modifications changed source visibility inside AI-generated responses. The strongest finding: adding statistics, adding expert quotations, and citing sources lifted citation visibility by up to 40% on the paper's position-adjusted metric. Less-cited findings: keyword stuffing produced no positive effect; fluency improvements helped but less than substantive content additions; and adding outbound citations to your own content lifted the probability that AI would cite you in turn. The paper used two evaluation metrics — subjective impression and position-adjusted word count — and the 40% figure refers to the latter.

Did the GEO paper test keyword stuffing? Did it work?

Yes, and no. Aggarwal et al. tested keyword-density manipulation as one of the optimization strategies in GEO-BENCH and found it produced no positive effect on AI citation rates. The traditional SEO playbook of repeating target keywords does not transfer to generative engines. Content that looks like a keyword list reads as structurally less extractable to a retrieval system, not more. Practitioners who treat GEO as "AI SEO" — and apply keyword-density tactics to the new context — are working against the empirical evidence.

Are there limitations to the Aggarwal et al. research?

Yes, and the paper names them. The GEO-BENCH was tested on the generative engines available in 2024; the engine landscape has continued to shift since publication. The optimization strategies were tested mostly independently rather than in combination, so the paper doesn't fully report what happens when you stack three or four strategies. Domain coverage was broad but not exhaustive. As with any single study, the magnitude of any specific lift will vary across content types, query types, and engines. The paper's overall framework — that some content modifications measurably move AI citation rates and others don't — is more durable than any individual point estimate inside it.

Is GEO measurably different from AEO?

In the controlled-research sense, no. They describe the same discipline and the same measurement framework. The Aggarwal et al. methodology is what the practitioner-side AEO community is implicitly applying when it ships schema markup, citation-ready snippets, and off-site corroboration. The work overlaps completely. Where the two terms diverge is audience and rhetoric: GEO content tends to engage with research; AEO content tends to engage with implementation. Both lead to the same set of modifications.

How do I apply the GEO paper findings without re-running the study?

Translate the findings into a content checklist. On the highest-traffic pages of your site, add at least one named statistic per major claim. Add at least one expert quotation, attributed by name and source, in each long-form piece. Cite outbound sources for every factual claim that isn't trivially verifiable. Use FAQPage schema and Article schema to mark up the structured sections. Verify your site in Bing Webmaster Tools. The full multi-platform methodology — including the off-site corroboration layer the paper didn't directly study — is in the AEO pillar guide. Igility's AEO audit applies the methodology empirically against your specific corpus.

References

Academic & Primary Research

• Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). "GEO: Generative Engine Optimization." Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona. The foundational paper introducing GEO and GEO-BENCH; primary finding that statistics, expert quotations, and citing sources lift content visibility in AI responses by up to 40% on a position-adjusted metric.
• Nogami, M. & Tannenbaum, B. (2026). "Bing, not Google, shapes which brands ChatGPT recommends." Search Engine Land, April 6, 2026. Seer Interactive research: 87% of ChatGPT brand recommendations align with Bing's top results; "fanout queries" mechanic across 3-10 related searches.

Search & AI Adoption

• Fishkin, R. (2024). "2024 Zero-Click Search Study." SparkToro. 58.5% of US Google searches end without a click.
• Search Engine Land (2026). "Google AI Overviews: Surge, Pullback, and What the Data Shows." 83% zero-click rate on AI-Overview queries.
• DemandSage (2026). "ChatGPT Statistics 2026." ~900M weekly active users, 2B+ daily queries.
• DemandSage (2026). "Perplexity AI Statistics 2026." 45M+ monthly active users.
• DataReportal (2026). "Digital 2026: One Billion People Using AI."
• McKinsey & Company (2025). "The State of AI in 2025." 78% of companies use AI (up from 55% in 2023).
• Position Digital (2026). "100+ AI SEO Statistics for 2026." ChatGPT's top recommendation becomes the user's top pick 74% of the time.

Structured Data & AI Citations

• xseek.io (2025). "How Does Structured Data Boost AI Search Visibility." 65% of AI Overview citations include structured data; pages with schema markup are 36% more likely to appear in AI summaries; 2.8x higher citation rates for structured pages.
• WPRiders / SearchVIU (2025). Google and Microsoft (March 2025) officially confirmed schema-markup use during AI response generation.

Industry Analyst & Vendor Research

• Forrester (2025). "How To Master Answer Engine Optimization." Lead recommendation: "Format content in short, simple answers full of unique quotes and stats."
• HubSpot (2026). AEO product launch (April 14, 2026). 42% of HubSpot buyers use answer engines during purchase research.
• Contentful (2025). "What is Generative Engine Optimization (GEO) and how does it differ from SEO?" June 11, 2025. Explicit alignment: "GEO is also known as answer engine optimization (AEO)."

Community Positioning

• Smarty, A. (2026). "SEO vs GEO: Stop Choosing Sides!" LinkedIn, April 2026.
• Donnelly, C. (2026). "SEO, AEO and GEO aren't the same thing." LinkedIn, March 2026.
• Wikipedia. "Generative engine optimization." Cites the Aggarwal et al. paper as the category origin.

Igility Keyword Research

• DataForSEO keyword analysis, April 2026. "What is geo": 4,400 monthly searches, +560% YoY (peaked at 12,100 in August 2025). "What is geo in marketing": 390 monthly searches, +1,340% YoY. "Generative engine optimization": 4,400 monthly searches, +319% YoY (KD 54). "Geo optimization": 880 monthly searches, +519% YoY. "Geo vs aeo": 390 monthly searches, +9,900% YoY.