Knowledge
Rikard Jonsson
Rikard Jonsson is Founder & CEO of Hey Sid and a five-time entrepreneur with a background in B2B SaaS, sales, and brand building. He believes B2B marketing is overcomplicated and writes about going back to basics: visibility, positioning, and consistent presence among the accounts that matter.
Generative Engine Optimization (GEO) for B2B SaaS: Get Recommended by ChatGPT, Claude and Perplexity
Quick answer: Generative engine optimization is the practice of making your company the source AI assistants cite and recommend. Peer-reviewed research found that adding quotations, statistics, and citations to content lifted visibility in generative engines by 30 to 41%. The larger finding is that brand mentions across the web correlate with AI visibility around three times more strongly than backlinks do.
What generative engine optimization is
Generative engine optimization, usually shortened to GEO, is the work of getting your company named, cited, and recommended inside answers produced by AI assistants rather than inside a ranked list of blue links.
The shift matters for B2B because of where buyers now start. When a prospect asks an assistant "what are the best account-based marketing platforms for a mid-sized European company," the answer they receive is a short list of named vendors. If you are not on it, you were not considered, and no amount of ranking third on Google for the same query changes that.
Two structural differences separate GEO from traditional search work.
There is no position one. Generative models are non-deterministic. Ask the same question five times and you can get five different answers with different sources. Visibility is therefore a matter of frequency, how often you appear across many runs of a query, rather than a fixed rank you can screenshot.
Visibility does not transfer between engines. Research into citation behaviour through early 2026 found that different models apply different selection criteria, and the same query put to different systems can return non-overlapping sets of sources. Being the top recommendation in Perplexity tells you very little about your standing in ChatGPT.
The Two Retrieval Paths
Most GEO advice fails because it treats all AI assistants as one system. They are not, and the distinction determines which tactics can possibly work.
Live retrieval | Training data | |
|---|---|---|
Systems | Perplexity, Google AI Overviews and AI Mode, assistants using web search | The base knowledge in ChatGPT, Claude, Gemini |
How you get cited | The engine searches and retrieves your page at query time | Your content was published, indexed, and recognized before the training cutoff |
What matters | Indexation, relevance to the sub-query, page structure, authority | Long-run presence, repeated mentions across many independent sources |
Speed of effect | Weeks | Months to years |
What you control | A great deal, and quickly | Very little, slowly |
Closest analogue | Traditional SEO | Public relations and brand building |
The practical consequences are worth stating plainly.
If your target engines are retrieval-based, GEO is closer to SEO than the marketing around it suggests. Being crawlable, being relevant to the narrower sub-queries an engine generates, and structuring answers so they are easy to lift all help, and they help within weeks.
If your target is the model's underlying knowledge, no amount of on-page work in the next month will change what it already learned. The only lever is being discussed, repeatedly, in places the next training run will absorb. That is a slower and less controllable programme, and it is why GEO tips lists that promise fast results in ChatGPT are usually describing the retrieval path while implying the other.
Most real B2B queries touch both. A buyer asking an assistant to recommend vendors gets an answer shaped by what the model already believes, then sometimes supplemented by live retrieval. Which is why the two workstreams below run in parallel rather than in sequence.
What the generative engine optimization research shows
The foundational study on this, published at KDD in 2024 by researchers from Princeton, Georgia Tech, and the Allen Institute, tested content strategies across 10,000 queries in 25 domains and validated the findings on Perplexity. It remains the most-cited empirical work in the field.
The measured visibility lifts were:
Quotations: around 41%. Adding relevant quotations from credible sources.
Statistics: around 32%. Replacing vague claims with specific figures.
Citations: around 30%. Citing sources for claims made.
Fluency: around 28%. Clearer, more readable writing.
Read those together and a pattern emerges that runs against a decade of SEO habit. What lifted visibility was not keyword placement or link volume. It was the markers of a credible, verifiable, well-sourced document. Generative engines appear to favour content that looks like it can be trusted and quoted.
The second finding worth building a strategy on comes from Ahrefs' study of roughly 75,000 brands, which reported that brand mentions correlate with AI visibility far more strongly than backlinks do, at 0.664 against 0.218. The mechanism is not mysterious: language models train on text, not on link graphs. A model learns that a company exists and matters because independent sources keep talking about it, whether or not those mentions carry a link.
For B2B teams, that reframes the work. The highest-value GEO activity may be earning unlinked mentions in analyst commentary, review platforms, industry forums, and comparison content, rather than acquiring links.
Why B2B SaaS should care now
Gartner's widely cited projection put traditional search volume falling by around 25% by 2026, with further decline expected in organic traffic through 2028. Whether or not those figures land precisely, the direction is visible in most B2B analytics accounts already.
The B2B-specific argument is sharper than the general one. B2B buyers do most of their evaluation before contacting a vendor, and a shortlist is typically formed during that anonymous phase. An assistant that names three vendors in response to a category question is performing shortlist construction directly. Being absent from that answer removes you from consideration at the exact moment consideration happens.
There is also a compounding effect that favours acting early. Being cited makes you more likely to be mentioned, which makes future models more likely to know you, which makes you more likely to be cited.
What to do about it
Two workstreams, run together.
Make individual pages citable. Answer the question in the first paragraph rather than after 400 words of preamble. Use specific figures instead of adjectives. Quote and cite credible sources. Structure content with clear question-shaped headings and self-contained answers that can be lifted without surrounding context. Keep pages current, since engines favour recent material. Implement structured data so machines can parse the page reliably.
Make the brand known. Pursue unlinked mentions in the places your category is discussed: review platforms, analyst and consultant commentary, comparison and alternatives content, industry publications, and communities. Publish original research, because original data is what other people cite, and being the source of a statistic is the most durable route into both retrieval and training data.
One guardrail. Google is explicit that mass-produced low-value pages violate its spam policies, and the same content is unlikely to earn citations anyway. GEO done well reduces content sprawl rather than adding to it: fewer, better-sourced, genuinely useful pages beat volume.
Measuring it, and being honest about the limits
This is where most GEO programmes get into trouble internally, and the difficulty is real rather than a tooling gap.
You can measure whether you appear in AI answers. You can measure referral traffic from assistants, which most analytics tools now separate out. What you cannot easily do is connect an AI recommendation to a deal, because the buyer who was told about you by ChatGPT often arrives later as a direct visit or a branded search.
In B2B, nobody defends a budget with impressions, and share of voice inside a chatbot is a close cousin of an impression. Value becomes real the moment you can show which target accounts moved closer to a decision, and GEO is unusually bad at demonstrating that link.
The workable approach is to measure GEO as a leading indicator alongside a lagging one. Track citation frequency and share of voice across the engines your buyers use, track assistant referral traffic separately in analytics, and then watch branded search volume and direct traffic, which is where AI-driven awareness tends to surface. Treat the first as evidence the programme is working and the second as evidence it matters.
The tools
A category has formed quickly, with more than $300 million raised across it between mid-2025 and spring 2026. Most of these platforms do a similar core job: submit a set of prompts to public answer engines, parse the responses for brand mentions and citations, and report the results over time.
Profound: the category leader on data scale, covering around ten engines including ChatGPT, Google AI Overviews and AI Mode, Gemini, Perplexity, Copilot, Claude, Grok, Meta AI, and DeepSeek, with crawler analytics and prompt-volume data. Enterprise-oriented.
AthenaHQ: citation tracking combined with AI content brief generation, which suits teams that want measurement and content workflow in one place.
Goodie AI: mid-market positioning with multi-model visibility scoring and an attribution layer, pitched as an addition to existing SEO workflows.
Otterly.AI: the most accessible entry point, European in origin with strong adoption in DACH markets and good handling of non-English prompts, which matters if your buyers search in Swedish, Norwegian, or German.
On pricing, published figures conflict enough to be unreliable. The same tool is reported at very different entry prices across roundups, and several vendors gate real pricing behind a call. Treat any published figure as a starting point to verify. Entry-level monitoring genuinely starts around $29 a month; serious multi-engine tracking generally starts in the low hundreds.
For a wider view of the B2B stack, see our guide to the best B2B marketing tools and platforms.
Where Hey Sid fits
An honest note: Hey Sid is not a GEO tool and does not sell AI search visibility. It appears here because the two disciplines address the same underlying problem from different directions.
GEO works on being recommended when a buyer asks an assistant about your category. Hey Sid works on being recognized by the specific people in your target accounts before they ask anyone anything, through person-based advertising and thought leadership aimed at named individuals, with outreach into the same audience.
They are complementary rather than substitutable, and the overlap is real: the unlinked brand mentions and published expertise that improve AI visibility are produced by the same thought leadership work that warms a buying committee.
Hey Sid suits mid-sized B2B companies with long consultative cycles and a defined account list. It does not suit teams wanting a self-serve tool, high-volume transactional motions, or companies without a defined ICP, and it will not get you cited by ChatGPT. If the recognition problem is yours, see how it works or book a demo.
Common mistakes to avoid
Treating all assistants as one system. Retrieval-based and training-based citation need different work on different timelines.
Chasing links when mentions matter more. The evidence points to unlinked brand mentions outweighing backlinks for AI visibility.
Publishing volume to win citations. Thin mass-produced pages breach search spam policies and do not get cited.
Expecting a fixed rank. Non-determinism means visibility is frequency across many runs, not a position.
Assuming visibility transfers. Different engines cite different sources; measure each one you care about.
Reporting share of voice as the outcome. It is a leading indicator. Pair it with branded search and direct traffic.
Ignoring non-English prompts. If your buyers ask questions in Swedish or German, English-only monitoring misses your actual exposure.
Conclusion and next steps
Generative engine optimization comes down to two things the research supports: make individual pages genuinely citable, with quotations, statistics, sources, and clear structure, and make your brand widely discussed, because mentions matter more than links to models trained on text.
Split the work by retrieval path, run both workstreams in parallel, measure frequency rather than rank, and pair citation metrics with branded search so the programme can be defended internally.
For the practical implementation framework, see our companion guide to LLM optimization. For how the individual engines differ and how to track them, see our guide to AI search optimization.
If your constraint is being recognized by specific buyers rather than by chatbots, explore how Hey Sid works or read more in our resources.
FAQ
What is generative engine optimization?
Generative engine optimization is the practice of making your content the source that AI assistants cite and recommend in their answers, rather than competing for a ranked list of links. It combines making pages easy to quote and verify with building the brand presence that models learn from, since visibility depends on both retrieval and training data.
How do ChatGPT, Claude and Perplexity choose which sources to cite?
It depends on the mechanism. Perplexity and Google's AI experiences retrieve pages at query time, so indexation, relevance, and structure matter much as they do in search. ChatGPT, Claude, and Gemini also draw on training data, where being cited depends on having been widely published and discussed before the model was trained.
Does GEO replace SEO for B2B?
No, they overlap and compound. Retrieval-based engines rely on the same indexation and authority signals SEO builds, so strong SEO helps GEO directly. What changes is the emphasis: content structured for citation, specific verifiable data, and unlinked brand mentions matter more than they did for ranking alone.
What improves AI search visibility?
Peer-reviewed research found the largest lifts came from adding quotations, statistics, citations, and clearer writing, in the range of 28 to 41%. Separately, analysis of around 75,000 brands found brand mentions correlate with AI visibility roughly three times more strongly than backlinks, which points to earning mentions rather than links.
How do you measure generative engine optimization?
Track how often your brand appears across a set of representative prompts in each engine your buyers use, since visibility is frequency rather than rank and does not transfer between engines. Add assistant referral traffic in analytics, then watch branded search and direct traffic, which is where AI-driven awareness usually shows up.
Sources
https://www.omnibound.ai/blog/generative-engine-optimization-statistics
https://www.enrichlabs.ai/blog/generative-engine-optimization-geo-complete-guide-2026
https://www.dailygeoinsights.com/llm-citation-source-selection-research/
https://directiveconsulting.com/blog/blog-llm-ai-geo-strategy-guide/
https://www.datadab.com/research/ai-visibility-tools-compared
