How To Outrank Competitors In AI Search? (Based on Real Citation Data)

We analyzed 67,111 citations across 7,950 AI-generated search results to understand what actually drives visibility in generative search engines. The data was pulled from outputs by OpenAI’s GPT-4o, Google Gemini, and Perplexity, and it spanned more than 8,500 unique search prompts across a wide range of commercial and informational categories—including software, eLearning, cybersecurity, healthcare, and travel.

This article holds a part of the 50 GEO stats that matter in 2026 and outlines the seven most consequential findings, each grounded in data and translated into a concrete action.

Build structured comparison pages that ai models can parse and cite

Out of 7,950 citations analyzed, the most commonly surfaced pages were not blogs, essays, or thought leadership pieces. They were product rankings, vendor lists, and comparison guides. Collectively, the top 150 most cited URLs in the dataset accounted for over 1,100 citations—and 130 of those 150 were structured list-style resources. CNET’s product comparison page alone received 99 citations. Zapier’s roundup of SEO tools earned 91. Thinkific’s “best online learning platforms” article was cited 92 times.

These pages perform well because they are designed for exactly the kind of retrieval that large language models (LLMs) perform. When an AI engine receives a prompt like “What are the top CRM tools for 2025?,” it looks for source material that already reflects the answer format: a clearly ranked list, a short summary of each item, and signals of authority like brand names, metrics, or feature coverage.

Unlike traditional SEO, which may favor long-form guides for dwell time or backlink potential, generative engine optimization favors clarity and structure. They are trained to extract high-confidence answers from sources that look like tables, grids, or ranked breakdowns. If your content doesn’t lend itself to summarization, it’s less likely to be reused.

What this means for marketers is straightforward: writing a solid opinion piece on industry trends may help with executive branding or newsletter engagement, but it will not win you visibility in AI-generated responses. That visibility comes from content designed to support structured decision-making—and structured output generation.

If you want to be cited in generative search, you need to produce content that mirrors the format of the answers models are generating.

That means writing:

• Pages titled with specific decision queries (“Top X for Y”)

• Headings that segment each tool, vendor, or solution

• Tables or bullet summaries that break down key features

• Frequent mentions of proper brand names (not “this platform” or “one tool”)

Do not bury the ranking in a narrative. Do not assume a user—or an LLM—will read to the end. Treat every section as a standalone unit that could be lifted and quoted.

When done well, this format doesn’t just help LLMs—it helps buyers. It reflects how decisions are actually made: by comparing tradeoffs, identifying categories, and filtering down based on context.

Cover every angle of your category to earn more citations across more prompts

In our analysis of 8,500 citation rows, the domains with the broadest topic coverage also ranked among the highest in total citations.

To put numbers to that:

• Reddit.com was cited under 41 different search terms—the most of any domain in the dataset.

• Wikipedia.org appeared under 38 distinct prompts, followed by LinkedIn.com at 36, and YouTube.com at 33.

• Even domains with fewer total citations, like Statista (21 terms) or Reuters (20 terms), demonstrated strong visibility simply by showing up across a breadth of AI prompts.

Generative engines are identifying which domains reliably provide useful answers across an entire topic category. That domain-level reliability—what we might call semantic surface area—makes a site more likely to be pulled into multiple types of generative queries.

This is especially important for marketers competing in competitive SaaS or B2B categories. It’s not enough to have one strong piece of content on “best project management software.” If that’s the only angle you cover, your domain may be considered relevant for a single use case—but irrelevant for everything else. Meanwhile, a site that publishes ten related articles—covering project management for enterprise, startups, healthcare, compliance, integrations, pricing comparisons, and so on—is far more likely to be treated as a go-to source in generative outputs.

We also found that this breadth effect compounds citation volume. Sites that appeared across more search terms typically earned more total citations. Wikipedia, Forbes, and Reddit—three of the top five by search term breadth—also ranked top five in total citations. Their advantage wasn’t just content quality. It was content coverage.

This has strategic implications for content planning. AI visibility is driven by publishing patterns. If your site shows up repeatedly in prompts tied to the same thematic space, it becomes part of the LLM’s internal representation of that domain.

If you want your brand to be cited in more AI-generated responses, you need to own the full context—not just the core term.

What that looks like in practice:

• Build content hubs that start with a primary query (“best LMS platforms”) and expand outward

• Write supporting content that covers variations by buyer type, use case, geography, company size, or industry vertical

• Add comparative “vs” pages and alternative listicles (“Top LMS Alternatives to Cornerstone”)

• Ensure your internal linking reflects semantic proximity—don’t silo your content by funnel stage or persona if it breaks thematic cohesion

The broader your coverage, the more opportunities you create to show up in those patterns—and the more citations you will earn over time.

Target a top 3 ranking or accept that you won’t be seen

In AI search, the visibility advantage is heavily concentrated at the very top. Across the dataset, we observed a sharp drop in average visibility scores between the top three ranked results and everything that followed. Specifically, content ranked first had an average visibility score of 88.0, second-place content dropped to 79.1, and by the time you reach fifth position, the average visibility score falls to just 53.6.

Large language models like ChatGPT and Claude typically generate answers from just a handful of sources. When they cite URLs or base summaries on external content, the vast majority of those citations come from the first two or three results they retrieve. Anything below that threshold is far less likely to be included in the model’s response, regardless of how relevant or well-written it might be.

In practice, this means content that lands in fourth, fifth, or lower positions rarely surfaces—especially in zero-click generative experiences where users don’t scroll through links. The distribution curve is steep, and there is no long tail.

For marketers, that means if your content is not optimized to claim a top-three slot in generative search, it’s essentially invisible. 

To compete for visibility, you need to identify the opportunities where incumbents are weak and decisively overtake them. That means moving beyond passive keyword targeting and investing in richer, more comprehensive content experiences that AI engines prefer to cite.

What that looks like in practice:

• Audit high-intent keywords where existing content is thin, outdated, or unstructured

• Prioritize formats with a clear answer shape—tables, summaries, head-to-head comparisons

• Include citations, real examples, and visual structure that LLMs can parse cleanly

• Benchmark your pages against the current top three for structural quality and topical coverage—not just word count or SERP position

Mention your brand name clearly and often to maximize retrieval

One of the more subtle—but consistent—patterns in the citation data was the correlation between visibility scores and brand specificity. Across the dataset, citations that included explicit brand mentions—rather than generic phrases like “this platform” or “a leading tool”—tended to rank higher and be cited more often.

This is backed by data, which is precisely what AI visibility optimization tools are designed to track. Brand-specific references in Rank 1 position averaged visibility scores above 60, while more generalized content often fell below that threshold. The takeaway is that models are more likely to retrieve and reuse content that clearly signals which entities are involved. This improves both citation accuracy and summarization confidence.

From a technical perspective, this makes sense. LLMs are trained on text patterns and statistical associations. The clearer and more repeatable your reference to a brand, the easier it is for the model to remember, retrieve, and reuse that information. Ambiguous phrasing introduces friction; specificity unlocks visibility.

If your brand name doesn’t appear in the sections models are likely to quote, your chances of being surfaced go down. Generic references dilute your retrieval signal.

What this means for content creators:

• Use your brand name consistently in list items, headers, tables, and product callouts

• Avoid substituting pronouns or vague descriptors after the first mention—repeat the name where clarity matters

• Treat brand mentions as a core part of your on-page structure, not just a branding detail

• When quoting clients or products, use specific names and roles, not abstracted summaries

Demonstrate expertise to outrank wikipedia in commercial queries

Wikipedia was the second most frequently appearing domain in the dataset, cited in over 1,300 rows and present under 38 unique search terms. Its dominance in informational queries is well-established. But when we isolated citations tied to commercial or B2B queries, a different pattern emerged. Wikipedia was still present—but it was often outranked by more focused, expert-driven sources like CNET, Zapier, or Investopedia.

This reflects a key difference in how AI engines evaluate general knowledge versus decision-stage content. For top-of-funnel queries (“What is LMS?” or “What does CRM stand for?”), Wikipedia often appears by default. But when a prompt requires a recommendation, comparison, or opinion—especially one involving product or vendor evaluation—Wikipedia is quickly replaced by sources that demonstrate category expertise.

That’s where marketers have a real advantage. You don’t need to out-neutral Wikipedia. You need to out-expert it.

This requires more than just writing about a product or category. You need to demonstrate situational knowledge—through the use of clear frameworks, real-world use cases, customer outcomes, and market insights that reflect lived experience, not just aggregated facts.

What to do:

• Lead with credentials, both personal and organizational. Signal expertise upfront.

• Use customer data, benchmarks, or third-party validation to support claims

• Avoid flattening content into generic definitions—start with specificity and branch into depth

• Compare alternatives with commentary, not just features

Design every page so that machines can parse, quote, and reuse it

Across the top-cited URLs in the dataset, the most visible pages were those that were cleanly organized, semantically structured, and machine-readable. They weren’t just informative—they were easy to process, segment, and repackage.

Among the highest-performing content, we observed consistent use of:

• Clear H1 and H2 tags to define topics and subtopics

• Bullet lists and tables to present comparisons or features

• Consistent brand references that reinforced retrieval clarity

• Minimal visual clutter that could confuse layout parsing

This matters because large language models work by recognizing structure, spotting relationships between elements, and confidently inferring what belongs where. When a page presents its information cleanly, with reliable markup and consistent formatting, it becomes far more likely to be selected, quoted, or summarized by the model.

The inverse is also true. Pages that bury key points in dense paragraphs, rely on stylistic dividers instead of semantic headers, or introduce excessive design elements (like tabs, accordions, or JavaScript-rendered content) often fail to surface—not because the content isn’t valuable, but because the structure is too opaque for the model to navigate effectively.

This is especially important for marketers creating decision-making content—vendor comparisons, buyer’s guides, product overviews, and similar assets. These formats succeed when the layout reflects the logic of the decision. AI engines prefer structured content because it makes summarization easier, and more importantly, safer. Structured content reduces ambiguity and lets models pull directly from labeled elements without guessing at context.

To earn visibility in AI-generated results, your page must be designed with machines in mind—not just readers. It needs to be not only helpful, but legible to a non-human parser. That means giving the model exactly what it’s looking for: clear labels, nested hierarchies, and consistent content architecture.

What to implement:

• Use semantic HTML to mark up headers (H1, H2, H3), lists (<ul>, <ol>), and tables (<table>)

• Place product names and key takeaways in predictable locations, such as top-of-section summaries or consistent list structures

• Add schema.org markup for articles, product reviews, FAQs, or comparisons where applicable—this supports both LLM interpretation and search engine context

• Avoid dynamic content rendering that requires JavaScript or interactivity to reveal key information

When you make it easy for a model to identify what’s on your page, you also make it easy for that model to cite you. Visibility, in this context, is not just about what you say—it’s about how clearly, consistently, and machine-readably you say it.

Prioritize engine-specific optimization based on how different models cite content

Not all AI engines retrieve and cite content the same way. When we segmented the 67,111 citations by engine, it became clear that each model exhibits distinct behaviors in how it selects sources and distributes visibility. These differences directly affect which types of content get cited—and how often.

Among the four engines analyzed:

• OpenAI’s GPT-4o produced the most citation-dense outputs, averaging 25.7 citations per result row—the highest of any engine. It favors a small number of high-authority domains and appears more selective in what it chooses to reuse.

• Google Gemini 2.0 cited sources at a moderate rate, averaging 13.3 citations per result, with a preference for structured, semi-authoritative pages and brand-driven resources.

• Google AI Overview generated the largest total volume of citations overall (34,494 in total), but spread them thin across prompts—averaging just 6.1 citations per row, suggesting a broader but less citation-intense retrieval model.

• Perplexity Sonar, by contrast, averaged just 3.9 citations per row, often citing community content or exploratory sources but with much lower density.

These differences have strategic implications. Content that performs well in one engine may underperform in another—not because of quality, but because of format, authority signals, or structural alignment with that engine’s retrieval model.

If you want to increase citation volume across AI outputs, you need to tailor content strategy to the distribution patterns of specific models.

What to prioritize by engine:

• For OpenAI GPT-4o, focus on structured, definitive, and high-authority pages. LLMs with higher citation density demand clear rankings, expert context, and strong brand signals. Half-structured content won’t be enough.

• For Google Gemini, emphasize keyword coverage and brand mentions in semantically rich formats—like product comparison lists or vendor roundups tied to buyer-specific prompts.

• For Google AI Overview, cast a wide net with breadth-focused content that shows up across more queries, even if individual pages are cited less intensively.

• For Perplexity, include community validation, Reddit-style Q&A patterns, or topical summaries that support informational coverage across long-tail prompts.

Understanding which engine you're optimizing for—and how each engine evaluates source structure—will allow you to design your content with citation behavior in mind, not just intent matching or keyword density.

Avoid content categories that rarely earn citations in generative outputs

Publishing more content is not the same as earning more visibility. When we broke down citation performance by content category, a clear pattern emerged: some formats attract frequent citations, while others appear often but deliver almost no downstream value.

Specifically, we found that:

• Comparison portals averaged 17.0 citations per row

• Product pages earned 15.4 citations per row

• Knowledge bases and community-driven content both exceeded 10 citations per row

These formats consistently outperformed traditional blogs, PR pages, or general-purpose informational content. On the other hand:

• Wiki pages, while frequently surfaced, earned just 2.1 citations per row

• PR content scored even lower at 1.0 citation per row

• Standard blogs and product blogs hovered at 4.3 and 4.0 citations per row respectively, despite making up the largest share of the dataset

This disparity highlights a crucial optimization point: just because a content type is commonly published doesn't mean it’s worth producing—especially if your goal is visibility in generative search. AI engines do not reward volume; they reward structure, relevance, and information utility.

What to deprioritize:

• Blogs that lack structure, specificity, or comparative utility

• Press releases that are time-sensitive and brand-centric

• Informational content that mimics Wikipedia without adding expertise or perspective

• Static content that fails to answer a prompt directly or aid decision-making

What to scale instead:

• Decision-making content built around product selection and comparison

• Use-case specific product pages that match prompt structure

• Well-maintained knowledge base entries that cleanly define and contextualize technical concepts

• Community-validated or semi-structured Q&A content that answers real buyer prompts

If your team is investing in formats that consistently underperform across engines, the cost isn’t just inefficient effort—it’s lost visibility. Every content dollar should be directed toward formats that models trust, reuse, and cite.