Key takeaways (TLDR)
- GEO (Generative Engine Optimization) is the practice of optimizing your content so AI platforms like ChatGPT, Perplexity, Claude, and Gemini cite your brand in their answers. GEO runs alongside SEO, not in place of it.
- GEO is “fan out” optimization. LLM responses synthesize 500+ word answers across multiple sources and topical angles, which means you need to influence the citation ecosystem holistically, not just target individual keywords.
- Brand mentions matter more than backlinks for GEO. Brand search volume shows a 0.33 correlation with LLM citations, while backlinks show weak or neutral correlation. This makes PR significantly more efficient for GEO than for traditional SEO, because a mention is enough. No link required.
- Each LLM has different source preferences. ChatGPT relies heavily on Wikipedia (47.9% of citations) and Reddit (11.3%). Claude leans toward Reddit, Quora, and third-party review sites. One-size-fits-all optimization doesn’t work.
- Measurement is early-stage but trackable. LLM referral traffic in GA4, branded search lift, citation frequency, and share of voice are the core metrics. They’re less precise than traditional SEO metrics. Start tracking now anyway.
- Most brands haven’t started. The competitive window is open. Citation authority compounds over time, similar to domain authority in traditional SEO. Early movers will have an advantage.
“So… how do I actually show up in ChatGPT?”
I’ve been asked some version of this roughly 50 times in the last three months. From CMOs, founders, and marketing directors who’ve spent a decade getting comfortable with Google and are now staring at an entirely new board.
My honest answer: we know more than we did six months ago, but less than we’d like. Anyone claiming they’ve cracked a formula is probably selling you something.
Here’s what we do know, based on testing GEO tactics across our client base, studying real GA4 data from 15 properties, and tracking citation patterns across 10+ LLMs using tools like ReachLLM.
What is GEO, and how does it relate to AEO?
You’re going to see both GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) thrown around right now. Sometimes interchangeably, sometimes not.
Here’s my read. GEO refers broadly to optimizing your content so AI platforms (ChatGPT, Perplexity, Claude, Gemini) can retrieve, cite, and recommend your brand when answering user queries. AEO is either a direct synonym, or a more tailored acronym referring specifically to optimizing for AI Overviews within traditional Google search. If you’ve been doing SEO for a while, you’ll recognize AEO as what we used to call “answer box” or “rich snippet” optimization, now wearing a new hat.
For better or worse, neither acronym is going away. Let’s just agree we’re talking about LLM visibility and traffic generation and move on with it.
The core idea: if traditional SEO was about earning a spot among 10 blue links, GEO is about earning a place in the answer itself. The user might never click through to your site. But your brand was in the conversation, and that’s a fundamentally different kind of visibility.
This is not a replacement for SEO. In our own data, traditional Google organic still drives orders of magnitude more traffic than LLM referrals. But the gap is narrowing, and the compounding effects of citation authority make this worth investing in now. In chess terms: the moves you make in the opening set up your position for the middle game.
What’s actually different about GEO vs. traditional SEO?
I built a comparison chart (you may have seen it on LinkedIn) to simplify this, but here’s the substance behind it.
| SEO | GEO | |
|---|---|---|
| How discovery works | Linear: query → ranked results → clicks | “Fan out”: LLM synthesizes multiple sources into a holistic 500+ word answer |
| What earns authority | Backlinks to your pages | Brand mentions across the web |
| What you’re optimizing for | One algorithm (Google) | Multiple LLMs with different source preferences (ChatGPT, Perplexity, Claude, Gemini) |
| Research approach | Keyword targeting based on search volume data | Topic and citation ecosystem mapping |
| Core KPI | Non-brand organic clicks | LLM referral traffic + branded search lift |
| Measurement maturity | Decades of established tooling | Early-stage, evolving fast |
Now let’s unpack what matters most.
SEO is linear. GEO is “fan out” optimization. In traditional search, a query leads to ranked results. You optimize a page, earn links, rank higher, get clicks. Relatively predictable. GEO works differently. When an LLM responds to a query, it doesn’t hand you a list of links. It assembles a holistic answer, often 500+ words, synthesized across multiple sources and topical angles. It gives you all the context, all the perspectives, and anticipates the question you’re probably thinking next, or even questions you didn’t ask but are probably also wondering about.
The implication for research and targeting is significant. Traditional keyword research asks: “What specific phrase is someone typing?” GEO requires a wider lens: “What is the entire citation ecosystem my company is playing within?” You’re not optimizing for a single query. You’re influencing the topical sphere holistically, because that’s how models build their answers. If your content only covers one narrow angle of a topic, you might get passed over for a source that covers the full picture.
Mentions vs. links (and why your PR team should be excited). Backlinks are the currency of traditional SEO, and earning them is brutal. Getting a high-authority site to link to your company is hard. Getting them to link directly to that specific article you’re trying to juice? Even harder. Every SEO and PR team knows this pain.
GEO changes the equation. LLMs don’t need a backlink to recognize your authority, just a brand mention. Research analyzing over 7,000 citations across 1,600 URLs found that classic SEO metrics like backlinks don’t strongly influence AI chatbot citations. Meanwhile, brand search volume shows a 0.33 correlation with LLM citations, making it the strongest predictor of AI visibility identified so far.
Here’s the part that’s underappreciated: this dramatically improves the value-for-effort on PR. Getting a journalist at a top-tier publication to include a hyperlink to your site? Heavy lift. Getting them to simply mention your company or product in the context of a relevant topic? Much easier ask. From a GEO perspective, the mention is the signal. No link required.
If you have a PR team that’s been grinding on link acquisition and feeling like they’re pushing a boulder uphill, this should be genuinely encouraging.
One algorithm vs. many (and this is actually good news for smaller companies). SEO means understanding Google (and maybe Bing, if you’re feeling generous). GEO means understanding that ChatGPT, Claude, Gemini, and Perplexity all have different source preferences and citation behaviors. An analysis of 30 million citations shows distinct patterns: ChatGPT pulls 47.9% of its citations from Wikipedia and 11.3% from Reddit. Claude leans toward Reddit, Quora, small industry sites, and third-party reviews. Behemoth industry sites get cited across the board, while smaller sites see uneven coverage depending on the model.
Here’s where it gets interesting if you’re not a Fortune 500. Our research is finding that certain LLMs, particularly Perplexity and Gemini, are significantly more willing to cite smaller, niche-specific sites. Perplexity in particular loves how-to guides, comparison listicle content, and expert roundups from smaller publishers. In a Yext study of 6.8 million citations, niche sources made up 24% of all Perplexity citations, the highest of any model. Blogs alone account for roughly 38% of Perplexity’s citations.
What does this mean practically? If you’re a mid-market company competing against industry giants, GEO may be a more level playing field than traditional SEO ever was. In Google, you’re fighting for 10 spots against sites with decades of domain authority. In Perplexity and Gemini, you can earn citations by being the most helpful, most specific resource on a niche topic, even with a relatively young domain. The barrier to entry is depth, clarity, and topical focus, not raw authority.
That favors companies that know their subject matter deeply and publish that expertise in structured, actionable formats.
Keywords vs. topics. SEO relies on empirical search volume data to target specific keywords. GEO shifts focus toward topics and coverage depth. You’re optimizing for a concept, not a phrase. LLMs don’t care that you used the exact keyword 14 times. They care that you covered the subject thoroughly enough to be worth citing.
Measurement shifts. SEO measures non-brand clicks. GEO shows up through LLM referral traffic and branded search lift, creating attribution gray areas that can make a data person (hi, that’s me) a little twitchy.
What GEO tactics are actually working right now?
I’ll be transparent: this section is a snapshot, not a stone tablet. What works in GEO shifts as the models change. We’ve had tactics perform well for three months, then stop. It has, at times, felt like whack-a-mole. But patterns are emerging. Here’s where we’re placing our bets.
Structure content for extraction, not just reading
LLMs don’t read your blog post the way a human does. They scan for passages to extract and reassemble into an answer. The first 200 words of any article need to directly answer the primary query, not build suspense. We call this the “TLDR-first” approach. (Yes, we’re eating our own dog food with this article. Scroll up.)
Practically: write direct answers early, use question-format headers that mirror actual queries, and keep paragraphs tight and fact-dense. The Princeton GEO study found that structural optimization strategies can boost AI visibility by roughly 40%. Content that’s easy to parse is content that gets cited.
Lead with original data and specifics
Probably the single highest-leverage GEO tactic we’ve found. LLMs strongly prefer content with specific, citable data points. Content featuring original statistics sees 30-40% higher visibility in LLM responses compared to general observations. “Content refreshes drove a 34% increase in non-brand traffic within 14 days” is far more likely to be cited than “content refreshes can improve traffic.”
If you’re running experiments (and you should be), publish the results. If you have proprietary data, use it. Original research and specific statistics are citation magnets for LLMs and backlink magnets for SEO. Two birds, one stone.
Build your “citation footprint” through brand mentions
This is where GEO starts to feel more like PR than SEO. LLMs evaluate your brand’s authority partly through how often and where you’re mentioned across the web. Sites appearing across four or more AI platforms are 2.8x more likely to be cited by ChatGPT than single-platform sources.
That means earning mentions in high-authority publications, contributing to industry conversations on Reddit and Quora (where LLMs actually do pull from), and building presence across the platforms where different models source information. Our research shows each major LLM has distinct preferences: Claude and Gemini lean toward Reddit and Quora, ChatGPT relies heavily on Wikipedia and established industry publications, and behemoth industry sites get cited across the board.
The takeaway: your GEO strategy needs a distribution layer that traditional SEO never required.
If you’re a smaller company, lean into what LLMs reward
This deserves its own callout because I think a lot of smaller teams read GEO advice and assume it’s only for enterprise brands with big PR budgets. That’s not the case.
Publish comparison and how-to content in your niche. Perplexity and Gemini disproportionately cite this content from smaller sites. If you’re a cybersecurity firm, a detailed “how to evaluate SIEM vendors” guide has a real shot at citation, even if your domain authority is a fraction of CrowdStrike’s. The key is specificity and structure: answer the question directly, include real evaluation criteria, and format it so an LLM can extract passages cleanly.
Become the canonical resource for a narrow topic. You don’t need to cover everything. You need to be the most thorough source on something. If an LLM sees your brand consistently referenced across Reddit threads, industry forums, and niche publications for a specific topic, that concentrated signal compounds. Niche brands with topical depth have achieved outsized AI visibility relative to their size because LLMs reward expertise concentration over domain-wide authority.
Participate where LLMs are already looking. Reddit, Quora, niche forums, and industry review platforms are all citation sources for LLMs. Contributing genuinely helpful answers on these platforms is a free, high-leverage GEO tactic regardless of company size. Show up as a knowledgeable contributor, not a brand billboard.
Don’t sleep on technical accessibility for AI crawlers
If AI crawlers can’t access your content, none of the above matters. Review your robots.txt to make sure you’re not blocking agents like ChatGPT-User or ClaudeBot. Keep your site fast, architecture clean, and pages mobile-optimized. 65% of AI bot traffic targets content updated within the last year, so freshness and accessibility go hand in hand.
There’s also an emerging standard called llms.txt: a markdown file at your site’s root that helps LLMs navigate your key content. Think of it as a curated sitemap for AI systems. The jury’s still out on impact (Google’s John Mueller has been skeptical), but implementation cost is minimal. We’ve started deploying it across client sites.
How often should you refresh content for GEO?
This carries over from SEO but matters even more in GEO. AI engines weigh recency when selecting sources. A 2024 guide with no updates will lose ground to a 2026 article on the same topic, even if the older version is objectively better. Add updated data, new insights, and a clear “Last updated” timestamp to cornerstone content. (Again, practicing what we preach. Check the top of this post.)
If you’ve read our take on the three velocity lanes of SEO, you know we believe existing content refreshes are your best near-term leverage. That’s doubly true for GEO.
Think in entities, not just pages
LLMs think in entities: your brand, your people, your products, and how those connect to topics in their knowledge space. Consistent naming, clear product descriptions, and structured data (schema markup) help models understand and attribute information to your brand.
If the LLM understands your expertise but can’t confidently attribute it to your brand, you get implicit mentions instead of named citations. That’s a missed opportunity.
How do you measure GEO performance?
Measurement is the biggest gap in most GEO strategies right now. I think it’s more valuable to be upfront about that than hand-wave it away.
Here’s what we’re tracking:
| Metric | What it measures | Where to find it | Honest maturity level |
|---|---|---|---|
| LLM referral traffic | Sessions from ChatGPT, Perplexity, and other AI platforms | GA4 referral reports | Numbers are small but growing. Track the trend, not the absolute volume. |
| Branded search lift | Uptick in branded Google searches after LLM citations | Google Search Console | Real signal, but messy to isolate from other brand-building efforts. |
| Citation frequency | How often your brand appears in AI answers for target topics | Manual audits + tools like ReachLLM | The GEO equivalent of rank tracking. Less precise. Citation patterns can drift 40-60% month over month as models update. |
| Share of voice | Percentage of relevant AI responses mentioning you vs. competitors | Emerging platforms (Profound, Otterly, Ahrefs Brand Radar) | Early-stage tooling, improving fast. Useful for directional benchmarking. |
I won’t pretend these metrics are as clean as what we’re used to in traditional SEO. They’re not.
But here’s the uncomfortable truth nobody talks about enough: we have no equivalent of Google Search Console for LLMs. There’s no public data on what users are typing into ChatGPT or Claude. We don’t know prompt volume or exact queries. And it may be a long time before any LLM provider gives us that visibility, if they ever do.
That doesn’t mean you wait. It means you build your own proxy dataset now, before your competitors do.
Here’s how we approach it. Pull from three sources to reverse-engineer the prompts your ICP is likely inputting into LLMs at the start of their purchase journey:
Your high-volume keywords. Take the keywords that already matter for your business and reframe them as conversational prompts. “Best SIEM tools for mid-market” becomes “What’s the best SIEM platform for a company with 500 employees and a small security team?” That’s closer to how people actually talk to ChatGPT.
Reddit and forum threads. This is gold. Find threads where real people discuss your product space in their own language, not marketing language, but how they actually describe problems and evaluate solutions. That’s the voice your ICP brings to their LLM conversations.
Customer support tickets and sales calls. Your own customers are telling you exactly how they think about your space. The questions during onboarding, the language in support tickets, the objections on sales calls. All of it feeds into understanding what real prompts look like.
Use those three inputs to generate 10-15 target prompts that represent real purchase-journey queries in your space. Then start monitoring them across ChatGPT, Claude, and Gemini on a regular cadence. That’s your GEO baseline.
You’ll want a tool to do this efficiently. Manually querying each LLM for 15 prompts monthly is doable but tedious, and you’ll want historical tracking to spot trends. We use ReachLLM for tracking citation patterns across models, but there are plenty of solid options now: Profound, Otterly, Ahrefs Brand Radar, and Semrush’s Enterprise AIO all offer LLM visibility monitoring. The tooling is maturing fast. Pick one, get your prompts loaded, and start building your dataset. You can always switch tools later. The data you collect now is what matters.
Is it perfect? No. Is it better than flying blind and waiting for the LLMs to hand us a dashboard? Significantly.
The companies that start building this muscle now will have months of trend data by the time competitors realize they should be paying attention. Don’t wait for perfect instrumentation. Start measuring with what you have.
What don’t we know yet about GEO?
Transparency about uncertainty is more valuable than false confidence, especially in a space this new.
We don’t fully understand how different LLMs weight different content types. Our preferred content source matrix is based on observation, not confirmed platform documentation. It changes.
We don’t know how durable citation authority is. In SEO, a strong backlink profile compounds over years. Does citation frequency in LLMs compound the same way? Early indicators suggest yes, but we lack sufficient longitudinal data.
We don’t have a clean attribution model for GEO’s impact on pipeline. Branded search lift is promising, but isolating GEO’s contribution from other marketing remains imprecise.
And we don’t know what the next model update will change. Every major release from OpenAI, Google, or Anthropic can shift citation behaviors. That’s the nature of optimizing for systems that are themselves evolving.
Where should you start with GEO if you haven’t yet?
If you’re thinking “we haven’t done any of this,” don’t panic. You’re not behind; you’re early. Most brands haven’t started. Here’s a simple prioritization:
Month 1: Audit existing content for GEO readiness. Are your best pages structured for extraction? Do they lead with direct answers and contain specific, citable data? Restructure your top 10 pages by impressions.
Month 2: Set up measurement. Configure LLM referral tracking in GA4. Build your 10-15 target prompts using the keyword/Reddit/customer voice framework above. Run your first citation audit across ChatGPT, Claude, and Gemini. Establish a baseline.
Month 3: Start your distribution layer. Identify which platforms each LLM prefers for your industry. Plan a brand mention strategy covering earned media, community participation, and strategic partnerships. Implement llms.txt.
Ongoing: Test, learn, deliver, repeat. Run isolated content experiments. Publish the results. Refresh cornerstone content quarterly with updated data and timestamps.
The bottom line
GEO is not replacing SEO. The two systems run alongside each other, and they overlap more than the industry wants you to believe. The foundational work of creating authoritative, well-structured, data-rich content serves both.
But GEO does require new thinking about distribution, measurement, and what “visibility” means when there are no rankings to chase. The brands that build citation authority now will have a compounding advantage as AI-driven discovery grows.
We’re still early. The rules aren’t fully written. But that’s also what makes this interesting.
If you’d like to talk through what GEO looks like for your specific situation, book a free strategy call with me here. No pitch, just perspective. 🧪