GEO vs SEO: Key Differences And Similarities? [Based on Research]

Search engine optimization has always centered on improving a site’s rankings, driving clicks, and converting that visibility into measurable web traffic. The practice is built around making content easier for Google, Bing, and other search engines to index and reward so that users searching with traditional queries find the most relevant page and click through.

Generative engine optimization, on the other hand, shifts the battleground. Instead of competing for a higher placement in a list of blue links, the goal is to be cited and linked within AI-generated answers from engines such as ChatGPT, Perplexity, Gemini, and Google’s AI Overviews. Success is measured less by position on a results page and more by whether the assistant references your content as part of its response.

Despite these differences in mechanics, the underlying objective is the same. Both approaches ultimately depend on surfacing information that is useful, credible, and trustworthy to the audience making the query. Whether the click happens on a search result or the mention appears in a generative answer, the principle of earning visibility by delivering the most authoritative solution does not change.

How GEO and SEO differ in practice?

Although GEO and SEO both aim to increase visibility, the mechanics of how success is measured and achieved differ in meaningful ways. Traditional SEO has matured over two decades, with clear signals such as rankings, click-through rates, and backlinks forming the foundation of its playbook. GEO, however, is still evolving, and the way generative engines surface and credit sources has introduced an entirely new layer of performance metrics. Understanding these differences is essential for any team that wants to adapt its search strategy rather than treating GEO as a passing trend.

SEO measures success in rankings and CTR, GEO measures it in citations and referrals

In SEO, performance has always been measured by how high a page ranks and how often users click through from search results. Google Search Console reflects this focus with metrics like impressions, average position, and click-through rate because the ultimate outcome of SEO is traffic delivered to a site. 

In other words, success is determined by whether your page earns a visible placement and whether users decide to click it.

GEO shifts that definition entirely. Engines such as ChatGPT, Perplexity, and Google’s AI Overviews do not deliver a stack of links but instead generate synthesized answers where sources are cited directly. Teams track two numbers that match this reality: how often the assistant includes your domain inside the answer (your citation share) and how many visits arrive from those answers (assistant referrals). 

Basically, SEO gives you a link, while GEO gives you a mention. 

Data shows how significant this difference is in practice. Pew Research found that when Google shows an AI Overview, users click a link in only 8% of visits; the rate is 15% when no overview appears. That means many users are satisfied without clicking at all, which reduces traffic but raises the value of being the source they do choose to explore further.

What makes GEO especially important is that the smaller number of clicks often convert at far higher rates. Internal benchmarks show that referrals from AI assistants convert at up to 23 times the rate of traditional organic search because the assistant has already guided the user through their research process before handing them off. In other words, GEO sends fewer visitors but delivers them at a much later and more decisive stage of the journey. 

Our research on 65,000 Perplexity answers shows that once a site consistently appears in the citation set for a topic, it tends to be cited across related prompts at a much higher rate. This compounding effect makes citation share in GEO closer to a momentum game, where early inclusion increases the likelihood of future mentions. SEO, by contrast, treats each query more discretely, with rankings earned or lost on a keyword-by-keyword basis.

SEO bots crawl to index content, while GEO bots crawl to ground AI answers

Search engine bots like Googlebot are designed to index the web. Their purpose is to fetch and render content so it can be ranked and shown in results. To do that well, Googlebot has become increasingly sophisticated: it can render JavaScript, execute dynamic elements, and combine rendered HTML with structured data like schema to understand a page’s context. This is why a site built on a heavy JavaScript framework can still rank — Google invests resources in rendering it properly.

GEO bots like GPTBot or PerplexityBot work differently. They are not indexing the web to maintain a ranked database of pages; they are harvesting text to ground answers or feed model training. These bots rarely execute JavaScript. They primarily consume static HTML, heading structures, and text content as-is. That means any information hidden behind dynamic rendering — like a pricing table loaded with JavaScript or an image carousel with no alt text — is invisible to them.

This divergence changes how optimization works. In SEO, structured data and links reinforce authority signals, while JavaScript rendering allows complex sites to remain discoverable. In GEO, semantics are the deciding factor. Bots privilege clean HTML, descriptive headings, and clearly written answers because these are easiest to extract and tokenize. Schema markup can help, but as Dejan SEO notes, whether LLMs actually see schema depends on how preprocessing tools pass the page content to the model. Even when schema is included, it is not the first reflex for an LLM — plain semantic text is.

Data backs this emphasis on semantics. Analyze’s research found that semantic URLs correlated with an 11.4% higher citation rate across Perplexity results. A page that explicitly communicates its purpose in the URL and headings — for example, /best-crm-for-startups — is easier for a model to ground than one with a generic string like /solutions123. In practical terms, semantic clarity does more for GEO performance than flashy design or complex JavaScript ever could.

SEO volatility comes from algorithm updates, while GEO risk comes from hallucinations and misattribution

One of the defining frustrations of SEO is its dependence on algorithm updates. A single Google core update can reshuffle rankings overnight, dropping traffic for sites that previously held stable top positions. 

HubSpot, for example, reported losing a significant portion of its blog traffic after recent updates despite no change in the quality or frequency of its publishing. 

That volatility came not from HubSpot’s strategy but from Google adjusting how it evaluates topical authority and relevance. For large publishers, this creates sharp swings in lead generation that are difficult to forecast or control.

Here’s another 

GEO introduces a different kind of instability. Generative engines rely on large language models to synthesize answers, which means content can be misattributed or ignored altogether. Unlike an algorithm update, this instability is not scheduled or announced — it emerges from the probabilistic way LLMs generate text. A brand may produce the most accurate resource on a subject yet still be omitted from an AI Overview, while a weaker competitor gets the citation.

The reputational risk is equally serious. Assistants sometimes hallucinate features, attach them to the wrong brand, or amplify a competitor’s positioning in ways that distort reality. For example, a generative answer could attribute advanced integrations to your software that do not exist, or worse, it could describe your competitor’s features as if they were your own, misleading users who trust the assistant’s authority. Our research at Analyze has captured these inconsistencies in Perplexity, where near-identical prompts returned conflicting citation sets — sometimes crediting the correct brand, other times excluding it entirely.

SEO rewards authority and backlinks, while GEO favors visibility footprint and freshness

Traditional SEO is built on authority signals. Google’s ranking systems weigh factors like domain authority, backlinks, and topical depth to determine which site deserves the top position. The principle is reinforced by Google’s own EEAT framework — expertise, experience, authority, and trustworthiness — which guides how both algorithms and quality raters evaluate content. 

In practice, this means a site like HubSpot, with millions of backlinks and a high DA, can hold strong positions even when competitors publish fresher or more detailed content. Here’s an example where Hubspot ranks for "birthday gifts.”

Authority and link equity create resilience in rankings, which is why SEO teams invest so heavily in digital PR, content hubs, and link-building campaigns.

GEO introduces a different logic. Generative engines do not operate on a static link graph or reward sheer backlink volume. Instead, their outputs are grounded in what is fresh, broad, and directly useful to the user’s prompt. 

Our research at Analyze shows this clearly: in a study of 65,000 LLM citations, domains with broader topical coverage — publishing across more related subtopics — earned consistently higher inclusion rates than single-page authorities. In addition, recency played an outsized role. In our analysis, we also found that newer pages were disproportionately cited, even when older authoritative resources existed, showing that freshness often overrides domain age or link equity in GEO.

Another factor is how broadly your brand is mentioned across the wider web. Even if your own site does not dominate organic rankings, being overwhelmingly cited on third-party websites increases the likelihood that generative engines will surface you as a trusted source. LLMs are trained to detect recurring entities and brands across diverse inputs, which means that citations and mentions beyond your domain can strengthen your GEO footprint.

Here’s an example where Shopify overwhelmingly dominates LLM responses for this prompt, “Best tools for managing multi-channel retail sales as of 2025.”

But this is not random as almost all sources used to provide a response for that prompt mentioned Shopify, even though Shopify is not leaning towards the multi-channel retail sales category.

SEO traffic follows keyword clusters, while GEO traffic flows from prompts and context windows

SEO maps traffic to keyword clusters. A query like “affordable CRM for new companies” belongs to the “CRM for startups” cluster, which also includes related searches such as “CRM tools for small businesses” or “best free CRM.” Google aggregates signals across that cluster and rewards sites that demonstrate authority, relevance, and semantic coverage.

This screenshot illustrates this dynamic. 

HubSpot appears at the top not just because of backlinks but because its page is semantically optimized around “free CRM” and related modifiers. Even though user-generated content from Reddit and Quora dominates the results beneath, HubSpot secures the prime spot by aligning its semantic footprint with Google’s keyword clustering system. This is SEO in action: authority reinforced by topical alignment creates durable rankings within intent-based clusters.

GEO works differently. For instance, you can see how a prompt like “What are the most user-friendly ecommerce platforms for first-time users in 2025?” is handled by a generative engine. 

Instead of clustering keywords, the assistant interprets the full prompt, pulls context from its training and grounding sources, and synthesizes an answer. Shopify, Wix, and Squarespace are surfaced not because they rank in a keyword cluster, but because they are consistently present in the model’s context windows across adjacent discussions of ecommerce usability. The citations — like Forbes, TechRadar, and SaleHoo — show how the assistant grounds its response in third-party content rather than following a static ranking system.

How GEO and SEO overlap in their reliance on credibility?

The contrasts between SEO and GEO show how differently each environment processes rankings, citations, and traffic. Yet the further you examine them, the more overlap you find in what actually drives visibility. Both systems, despite their technical differences, elevate sources they can trust, extract from, and reuse with confidence. That common ground makes credibility, clarity, and consistency just as critical to GEO as they have long been for SEO.

This starts with formatting and structure. Search engines and generative engines rely on them to interpret and reuse information, even if they do not process those elements in exactly the same way.

Formatting and structure matter, even if LLMs don’t always read schema

Search engines and generative engines both rely on how content is structured, but they use that structure differently. Googlebot and Bingbot are designed to parse HTML, schema markup, and structured data in order to generate rich snippets, knowledge panels, and better contextual matching in search results. That’s why FAQ blocks, schema.org markup, and clear heading hierarchies often translate directly into richer visibility on a results page.

Crawlers for generative engines like GPTBot, ClaudeBot, or PerplexityBot do not operate with the same guarantees. These systems usually ingest the raw HTML of a page and tokenize it for grounding, but whether structured data makes it into the model’s context window depends on preprocessing. If the pipeline preserves schema, the model can use it — LLMs are trained on code and markup, so they can understand it. If the schema is stripped away, the model falls back on the visible text and semantic clarity of the page.

This creates a subtle but important overlap. Formatting and structured elements help both environments, but their reliability differs. A well-formed FAQ block will almost always surface in Google search as a snippet because the engine actively interprets schema. That same block may or may not influence a generative answer depending on whether the preprocessor passes it through. What consistently matters, however, is semantic clarity. Clean headings, concise definitions, and text structured in extractable ways give both traditional and generative systems material they can trust and reuse.

The data supports this emphasis. Our analysis shows that semantic URLs alone can increase citation likelihood by 11.4%, highlighting how structure at the most basic level improves extractability. When LLMs assemble answers, they are not searching for a keyword cluster but for reliable building blocks. Pages that provide those building blocks in clear, structured form are more likely to be cited, just as they are more likely to win snippets and sitelinks in traditional search.

Topical breadth increases visibility in both environments

Search systems reward sources that cover a subject as an ecosystem rather than a lone page. A site that maps the terrain—definitions, frameworks, how-tos, pricing, comparisons, alternatives, integrations, and troubleshooting—builds many entry points that align with how people ask questions. Those entry points help retrieval systems match more queries, which increases the chances that your pages appear, get cited, and earn visits.

Breadth strengthens understanding at the crawler and model stages. When multiple pages reinforce the same entities, terms, and relationships, crawlers resolve ambiguity faster and assign clearer relevance. Generative systems benefit from the same density because retrieval retrieves several semantically related documents before the model composes an answer. If your domain consistently supplies clean, on-topic documents across adjacent angles, your content surfaces more often in those retrieved sets, which raises the probability of citation inside the final answer.

Coverage also compounds over time. Once a domain gets pulled into answers for one slice of a topic, proximity makes it a convenient candidate for the next related prompt. Our studies showed that sites with broad coverage across a topic cluster were included more frequently across neighboring prompts, which created a momentum effect that pure authority scores could not explain. The mechanism is practical rather than mystical: more well-structured pages create more vectors for retrieval, which creates more opportunities to be chosen.

Internal structure turns breadth into a coherent signal rather than scattered posts. Clear hubs, descriptive anchors, and short summaries at the top of each page help both ranking systems and generative engines understand how pieces connect. That connective tissue reduces duplication, clarifies scope, and lets models lift precise statements or steps without guessing.

User intent remains the ultimate filter for both

Every retrieval system, whether a traditional search engine or a generative assistant, is constrained by user intent. Algorithms and models can only surface what aligns with the problem a person is trying to solve. Authority, freshness, and formatting matter, but they are secondary to the question of relevance.

Search engines operationalize this through keyword matching and intent modeling. A query like “CRM pricing for small businesses” is recognized as commercial intent, which prioritizes product and comparison pages over thought-leadership blogs. Generative engines apply a parallel process with prompts. When a user asks “What are the most user-friendly ecommerce platforms for first-time users in 2025?”, the model retrieves documents that best match the latent intent — beginner-friendly software evaluation — and cites those sources in its synthesized response.

The logic is consistent across both systems. Authority without relevance gets ignored, and structure without alignment to user needs gets bypassed. A domain can publish hundreds of technically perfect, semantically rich articles, but if they don’t address real questions buyers are asking, neither Google’s clustering nor an LLM’s retrieval will consider them eligible. Conversely, even a less authoritative source can win exposure if it directly answers the precise problem framed in the query or prompt.

How to create a good GEO strategy based on research from the Analyze team?

Strong SEO fundamentals continue to matter, but our research shows that the same practices which make content rank also make it more likely to be cited in generative engines. GEO doesn’t replace SEO; it extends it. A strategy that emphasizes semantic clarity, topical breadth, and fresh, structured content is rewarded across Google, Bing, Perplexity, and ChatGPT alike. Our analysis of 65,000+ citations confirms that the overlap is not theoretical — the same signals that drive search rankings directly influence generative visibility.

Prioritize visibility through consistent refreshes and Q&A integration

The first lever is freshness. Generative engines disproportionately cite recently updated pages, and Google continues to prioritize content that signals it has been revisited. A practical strategy is to refresh cornerstone pages quarterly with updated statistics, new examples, and restructured sections. Layering in Q&A blocks turns those updates into extractable snippets that both search engines and LLMs can reuse. In our Perplexity study, domains that consistently refreshed content and added FAQs captured significantly higher inclusion rates across prompts.

Create mid-funnel and bottom-funnel assets in extractable formats

Most companies overweight top-of-funnel blogs but neglect MOFU and BOFU formats. Yet our ChatGPT research showed that lists, how-tos, and structured guides are cited far more than generic thought-leadership pieces. A “5-step CRM implementation checklist” or “Top 10 integrations for first-time ecommerce founders” not only drives conversions but also slots neatly into a generative answer. Content should be built in blocks that models can lift directly: numbered steps, bullet comparisons, or concise takeaways.

Build for AI Overviews by strengthening SEO foundations

Google’s AI Overview accounted for more than half of GEO citations in our dataset, which means you cannot treat it as a separate channel. Schema markup, semantic HTML, and a strong internal linking structure remain essential because they are the same inputs Google uses to populate its AI layer. Optimizing for snippets, sitelinks, and structured data in SEO is simultaneously optimizing for inclusion in AI Overviews. A single missed markup opportunity can cost both ranking and generative visibility.

Avoid thin formats that rarely earn attention

Not all content is worth the effort. News updates, press releases, and thin product blogs almost never appeared in our citation studies. They are either too short-lived or too self-promotional to be reused in generative systems, and they rarely sustain rankings in organic search. Those resources are better allocated to durable formats like refreshed guides, comprehensive FAQs, and structured comparisons that continue to earn visibility over time.

Tie it together with a topical map

The most effective strategies connect these pieces through breadth and depth. Covering related subtopics, interlinking them semantically, and revisiting them on a set cadence compounds visibility. It creates a topical footprint large enough to dominate keyword clusters in SEO and context windows in GEO. Over time, this breadth becomes a reinforcing loop: higher rankings in search feed into more AI citations, and those citations compound your presence in adjacent prompts.

What does all of this mean for your entire content strategy — and why our position at Analyze is that good SEO is good GEO

Every few weeks, someone declares that AI is killing Google and that GEO is the new SEO. The problem is, those takes have never been grounded in data. The latest research from SparkToro and Datos shows the opposite: 95% of Americans still use traditional search every month, and 86% remain heavy users. In fact, Google’s heavy-user share grew from 84% in 2023 to 87% in 2025. At the same time, AI adoption is expanding — 20% of Americans are now heavy AI tool users, and nearly 40% use them at least once a month. 

The story here is not replacement, but expansion. People are searching as much as ever while also consulting ChatGPT, Claude, Perplexity, and Copilot for different moments of the journey.

That reality reinforces our position at Analyze: good SEO is good GEO. The practices that earn rankings — semantic clarity, structured content, topical coverage, authority, and freshness — are the same practices that increase the likelihood of being cited in generative engines. GEO does not negate SEO; it amplifies the impact of those who execute SEO well. A semantically clear, well-structured guide that ranks on Google is also the kind of asset that ChatGPT cites, Perplexity grounds, and Google AI Overview features in its summary. You don’t choose between one or the other — you align your content so it wins in both environments.

The strategic implication for marketers is straightforward. Stop treating SEO and GEO as competing channels. Treat them as overlapping systems that reward the same foundation: credible, structured, problem-solving content. Brands that focus only on GEO risk ignoring where the majority of traffic still flows, while brands that focus only on SEO risk missing the compounding visibility that comes from being cited in generative engines. The companies that will win the next five years are those who embrace the full picture: building content ecosystems where search rankings feed into citations, and citations reinforce authority. That is why at Analyze we argue that good SEO is already good GEO — and our research proves it.