LLM Ranking Factors: What Actually Determines Whether AI Recommends Your Brand

Introduction: The New Battlefield for Visibility

Something fundamental has shifted in how people find information online. In 2024, roughly 60% of Google searches ended without a click. By early 2026, LLM-driven website sessions have increased by approximately 527% compared to 2024. The question is no longer just “How do I rank on page one?” It’s “How do I get AI to mention my brand when someone asks it a question?”

This is not a theoretical concern. A brand can rank first on Google for a target keyword and still be completely absent from ChatGPT’s answer to the same query. Traditional SEO and LLM visibility operate on overlapping but fundamentally different logic. Understanding that difference is now a competitive necessity.

The playing field is shifting, and for smaller brands it’s becoming more meritocratic. Not “everything you knew is obsolete” — brand search, freshness, topical authority, and multi-platform presence have always mattered. What’s new is that some of those classic signals are being re-weighted, and a handful of new ones (entity clarity, extractability, structured data as a retrieval shortcut) are emerging. This post is a map of that re-weighting.

This post breaks down what we know about LLM visibility factors in 2026: what the research says, how it compares to traditional SEO, and exactly what you can do to increase your brand’s presence in AI-generated answers.

What Are LLM Visibility Factors?

Traditional search engines use a well-documented set of ranking factors: backlinks, page speed, keyword relevance, domain authority, and hundreds of others. These factors determine where your page appears in a list of ten blue links.

LLMs work differently. When a user asks ChatGPT, Perplexity, Gemini, or Claude a question, the model doesn’t return a ranked list of pages. It synthesizes an answer, drawing from its training data, retrieval-augmented sources, or both. Within that answer, the LLM typically mentions 3 to 5 brands or sources. Getting into that short list is the new game.

LLM visibility factors, then, are the things that influence whether a large language model includes, cites, or recommends your brand or content in its response. Unlike traditional SEO factors, these are not published by any platform. They’ve been inferred through research, experimentation, and large-scale analysis of AI outputs.

Most of what follows is correlational. We’re describing what co-occurs with high LLM citation rates in the published research, not a causal mechanism we’ve reverse-engineered from a black box. Some of these correlations are strong and reproducible across studies. Others are suggestive but thin. We flag which is which. Treat this as a field map, not a rulebook.

The shift is from ranking to representation. You’re no longer competing for position on a page. You’re competing for a slot in a synthesized answer.

The 12 Core LLM Visibility Factors

Based on the latest research, including the landmark GEO study from Princeton/IIT Delhi (published at KDD 2024), industry analyses from 2025-2026, and large-scale citation tracking data, here are the factors that most influence LLM visibility. Before the 12, one factor gates all of them.

0. Query-Grounding Match (the precondition)

Before any of the next 12 factors fire, your content has to match the query the model actually ran. And that match happens in two distinct steps, not one. Getting this wrong is the single biggest reason technically well-optimized pages stay invisible in AI answers.

Step 1 — Match the real user prompt (via training data)

When a user types a question into ChatGPT, the model first sees the real prompt and decides whether it can answer from its pretraining alone. If yes, no web search happens at all — the model just generates an answer from what it already “knows.” The way you match this step is by having been in the training corpus: broad, consistent, well-structured web presence that the model absorbed during pretraining. This is why Factors 1 (brand authority), 2 (multi-platform presence), and 12 (domain credibility) matter so much — they’re how you get embedded into the model’s weights in the first place. Step 1 is a pretraining game, and you “rank” for it by being the answer the model remembers.

Step 2 — Rank for the fan-out (when web search is triggered)

If the model decides it needs fresh or grounded information — anything recent, anything commercial, anything where it would rather cite than guess — it rewrites the user’s prompt into multiple sub-queries and searches on those. This is query fan-out. ChatGPT fans out through a combination of Bing and Google (via SerpAPI). Gemini fans out through Google. Perplexity runs its own retrieval layer. Claude uses a web search tool. A single user question can trigger three or four different internal queries the user never sees. (For the evidence that ChatGPT uses both Bing and Google, see our Search Engine Land piece as well as our investigation into how ChatGPT scrapes Google.)

Our intuition, based on observing prompt routing patterns, is that OpenAI is heavily optimized for cost. Cheaper, higher-volume queries on free-tier plans likely route primarily through Bing (where OpenAI has a commercial relationship), while more complex queries on premium models (like o4-mini-high at the time of writing) appear to tap Google via SerpAPI more frequently. This is speculative, but the pattern is consistent enough in our data that it’s worth noting: both Bing and Google SEO matter for ChatGPT visibility, with the mix depending on query complexity and user tier.

The actionable takeaway: once the fan-out happens, you’re competing in traditional search results on the right search engine. For ChatGPT that means Bing SEO matters — not just Google SEO — and almost nobody is optimizing for it. We covered this specifically in our Search Engine Land study on Bing rankings and ChatGPT visibility: ranking well on Bing is one of the highest-leverage, lowest-competition moves a brand can make to show up in ChatGPT responses.

This is the point Yevhen Kralych raised in the LinkedIn discussion on the first version of this post, and he was right that it belonged at the top. Without the two-step grounding match, nothing else on the list gets a chance to matter.

Which step matters more? It depends on your brand size.

The relative weight of Step 1 vs Step 2 shifts dramatically with brand size. For large, well-known brands — the ones that saturate the training corpus because they appear across thousands of high-quality pages — Step 1 does a lot of the heavy lifting. The model already “knows” about them and can generate a recommendation from pretraining alone, often without even triggering a web search. Third-party presence on Reddit, LinkedIn, and review platforms reinforces that training-data signal and is where those brands should invest.

But for the vast majority of brands — the challenger brands, the mid-market companies, the startups — Step 1 is weak because they simply haven’t accumulated enough web presence to be deeply embedded in training data. For these brands, Step 2 is where the game is won or lost. That means the priority should be optimizing your own website first: schema markup, semantic HTML, content extractability, clear entity definitions, front-loaded answers. Your own domain is the one surface you fully control and can optimize end-to-end. Third-party sources — Reddit threads, LinkedIn posts, Medium articles, review platforms — are important too, but they should come after the website is airtight, not instead of it.

On-site factors — what you control on your own website

1. Content Freshness

AI systems prioritize recent content more aggressively than traditional search engines ever did. The data is stark: 65% of AI bot traffic targets content published within the past year, 79% accesses material updated within two years, and only 6% cites content older than six years.

For time-sensitive topics, freshness is essentially a gating factor. If your content hasn’t been updated recently, it may not even be in the running.

2. Content Depth and Extractability

LLMs prioritize content that thoroughly answers a question over content that partially addresses it. Comprehensiveness beats brevity, but with an important nuance: sources with clear, self-contained chunks of 50-150 words receive 2.3x more citations than long-form unstructured content.

Extractability for the win. The LLM isn’t reading your article end-to-end — it’s lifting the chunk that most cleanly answers the query. Your job is to make that chunk easy to find, easy to lift, and self-contained once it’s been pulled out of context.

The lesson is not “write more.” It’s “cover the topic completely, but structure it so each section can stand alone as a citable unit.”

3. Direct Answer Presence

Content that directly answers a question in the opening lines performs significantly better for LLM citation. Research shows that 44.2% of all LLM citations come from the first 30% of text (the introduction).

4. Structured Data and Schema Markup

A terminology note. “Structured data” and “schema markup” are often used interchangeably, but they’re not the same thing. Structured data is the broader category — any machine-readable representation of your content’s meaning. That includes schema.org markup (usually via JSON-LD), but also microdata, RDFa, OpenGraph tags, clean semantic HTML tables, and consistently formatted lists. Schema markup is the most common implementation of structured data, but a well-structured HTML comparison table with clear headers is also structured data and carries real weight for LLMs doing fact extraction.

A fair pushback we’ve received on previous drafts: structured data has well-documented CTR benefits in traditional SERPs, but why should it matter for AI, which synthesizes rather than clicks? The answer is about extraction cost.

The mechanism. When an LLM is asked a question, it needs to locate the relevant facts inside a candidate source, assess whether those facts are reliable, and format them into an answer. Free-form HTML prose forces the model to do entity resolution (“is ‘the company’ in paragraph 3 the same as ‘we’ in paragraph 1?”) and fact extraction at inference time, which is expensive and error-prone. JSON-LD gives the model a pre-parsed, disambiguated map of entities and their attributes. It is, in Stephen Honight’s phrase from the comments on this post, the path of least resistance. Models route through it because routing through free-form prose is harder.

The evidence. Multiple sources converge on a measurable uplift from comprehensive schema markup:

• Aiso’s own schema markup experiment (see our controlled test with live sites) measured a ~30% improvement in AI information retrieval accuracy when identical content was served with vs. without JSON-LD. Sample: paired test sites with and without Product/Organization schema, queried across ChatGPT and Perplexity.
• The Princeton/IIT Delhi GEO paper (KDD 2024) found that structured information presentation — citations, statistics, and explicit entity markup — produced the largest visibility gains of any intervention tested.
• Industry analyses from Search Engine Land and Growth Memo (2025-2026) report coverage lifts in the 28-40% range for pages with comprehensive schema vs. unmarked controls, with effect sizes converging across studies.

One caveat worth naming: the 40% figure you’ll see quoted in this category (including in earlier drafts of this post) is a ceiling, not an average. It shows up in the highest-effect studies and the best-instrumented tests. A realistic expectation is 20-30% uplift from implementing schema well on pages that had none, with diminishing returns as coverage increases.

5. Entity Clarity

LLMs need to understand what your content is about at an entity level. Ambiguous pronouns, unclear references, and generic language reduce the chance of citation. Define entities clearly, use consistent terminology, and connect your content to well-known entities in your domain.

6. Heading Hierarchy and Document Structure

Descriptive H2/H3 headings, short paragraphs, and logical document flow improve what researchers call “LLM extractability.” The model needs to parse your content and pull relevant sections. A clear hierarchy makes that easier.

Think of headings as labels for the model: each one should clearly communicate what the section below it contains.

7. Statistics, Citations, and Quotations

The GEO study found that adding statistics to content can increase AI visibility by 22%, while including quotations from experts can boost it by 37%. When used together with source citations, the combined effect reaches an average of 31.4% improvement.

8. Table and List Usage

Structured content formats like comparison tables, feature lists, and specification grids are highly extractable by LLMs. When a user asks “What are the best X for Y?”, the model is looking for structured comparisons it can synthesize. If your content provides that structure, you’re more likely to be sourced.

9. Topical Authority and Content Clusters

Topical authority — the depth and breadth of your coverage of a specific subject — matters more for LLM visibility than raw domain authority. Sites that publish extensively on a focused topic area, with strong internal linking between related pieces, build the kind of signal that LLMs use to assess expertise.

This aligns with how LLMs process information: they’re looking for the most authoritative source on a specific topic, not necessarily the most authoritative domain in general.

Off-site factors — brand presence beyond your website

10. Brand Authority and Search Volume

Brand search volume is the strongest observed correlate of LLM citations in the public research, with a correlation of 0.334 (GEO, KDD 2024) — outweighing even traditional backlinks. When users actively search for your brand, it is a direct signal of market relevance.

A causation caveat. The correlation runs in both directions. High brand search volume could drive LLM citations (because trained models embed what the web talks about). But the reverse is also plausible: brands that get cited in AI responses gain exposure, which drives users to search for them, which then shows up as “brand search volume.” We don’t have the data to cleanly separate cause from effect, and you should read this factor as “brand demand and AI visibility co-occur” rather than “buy more brand search and AI will cite you.”

That said, the actionable direction is the same: real-world brand demand and AI visibility reinforce each other, so investing in brand presence is a defensible bet regardless of which way the arrow actually points.

11. Multi-Platform Presence

Brands appearing on 4 or more platforms are 2.8x more likely to appear in ChatGPT responses than single-platform brands. But not all platforms are equal. YouTube mentions and branded web mentions are the top factors correlating with AI brand visibility across ChatGPT, Google AI Mode, and AI Overviews.

Domains with profiles on review platforms like Trustpilot, G2, Capterra, Sitejabber, and Yelp have 3x higher chances of being selected as a source by ChatGPT. Reddit and Quora mentions also carry outsized weight: domains with millions of brand mentions on these platforms have roughly 4x higher citation rates.

12. Domain Credibility Signals

While backlinks have a weaker correlation with LLM citations than with traditional search rankings, domain credibility still matters. It just manifests differently. LLMs assess credibility through the diversity and quality of mentions across the web, presence on review and industry platforms, consistency of information across sources, and the absence of contradictory or low-quality signals.

LLM Ranking vs. Traditional SEO: What Changed, What Didn’t

The relationship between traditional SEO and LLM optimization is not replacement. It’s expansion. Here’s how the landscape has shifted.

What Still Matters

Traditional SEO builds the infrastructure that LLM visibility depends on. Domain authority, technical health, crawlability, and content quality remain foundational. Websites ranking on Bing page one are approximately 3x more likely to be cited by ChatGPT (our Search Engine Land study), and since ChatGPT uses both Bing and Google for retrieval, your existing SEO work on either engine isn’t wasted — it’s a launchpad.

What Matters More Now

Semantic relevance over keyword matching. LLMs don’t match keywords. They understand meaning. Optimizing for search intent and semantic depth matters more than keyword density ever did.

Answer completeness over link profiles. The model is looking for the best answer, not the most-linked-to page. A well-structured, comprehensive answer on a newer domain can beat a thin page on a high-authority domain.

Structured formatting over page speed. While page speed is still good for user experience, LLMs care about how easy your content is to parse and extract. Structured data, clear headings, and self-contained sections are the new page speed.

What Matters Less

Backlink volume. Backlinks and organic traffic have a weak correlation with AI citations. The relationship exists but is substantially weaker than in traditional search.

Exact-match keywords. LLMs understand synonyms, context, and intent. Keyword stuffing or even keyword targeting in the traditional sense has minimal impact.

Click-through rate. There’s no SERP to click through. The metric that matters is whether your brand is mentioned in the response.

The GEO Research: What the Data Actually Says

The most rigorous research on LLM ranking factors comes from the GEO (Generative Engine Optimization) study, a collaboration between researchers at Princeton and IIT Delhi, published at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024).

Study Design

The researchers created GEO-bench, a large-scale benchmark of diverse user queries across multiple domains, along with relevant web sources. They then systematically tested different content optimization strategies and measured their impact on visibility in generative engine responses.

Key Findings

Overall impact. GEO optimization methods can boost visibility by up to 40% in generative engine responses.

Statistics addition. Adding relevant statistics improved visibility by 22% on Position-Adjusted Word Count metrics.

Quotation addition. Incorporating expert quotations boosted visibility by 37% on Subjective Impression metrics.

Citation combination effect. When source citations are combined with other methods, the average improvement reaches 31.4%.

Fluency and readability. Simply improving the writing quality of source text resulted in a 10-20% visibility boost.

The Equity Finding

One of the most significant findings: lower-ranked websites benefit substantially more from GEO optimization than high-ranking ones. Traditional search engines rely heavily on backlinks and domain age, which are hard for smaller players to accumulate. GEO optimization methods level the playing field by emphasizing content quality, structure, and information density, which any site can improve.

The path to LLM visibility is more meritocratic than the path to Google page one. This has major implications for startups and challenger brands.

How Different LLMs Behave Differently

Traditional SEO meant Google, full stop, for twenty-five years. LLM visibility is split across ChatGPT, Gemini, Claude, Perplexity, and Grok — each with different retrieval pipelines, citation behavior, and update cadences. Optimizing for one does not automatically optimize for the others. The research finding that only 11% of domains overlap between ChatGPT and Perplexity isn’t a footnote; it’s the whole strategic point. A single-platform strategy is a trap.

Where to focus: a usage-based priority

As of early 2026, ChatGPT is still the usage leader, but not by the margin it once was. Gemini is a close second and closing the gap fast, especially since it’s embedded in Google Search (AI Overviews), Android, and Workspace. Claude, which has historically skewed technical, is broadening its user base but remains strongest among developers and knowledge workers. Perplexity is growing rapidly but from a smaller base. Grok is niche (X power users).

Our recommendation: focus on ChatGPT and Gemini first — that’s where the majority of AI-driven queries happen today. One exception: if your company sells developer tools, dev infrastructure, or technical products, Claude should be your priority. Its user base over-indexes heavily on engineers and technical decision-makers, and being visible there puts you in front of the right buyers.

Multimodal content: text is no longer enough

One thing that makes LLM visibility fundamentally different from traditional Google SEO: these models are multimodal. They don’t just read text — they process video, audio, images, and structured data. This matters most for YouTube, which is now a first-class content format for LLM retrieval, not just a traffic source.

But how each model handles YouTube is very different:

• ChatGPT did one large-scale ingest of YouTube during pretraining (so historically popular videos are baked into its knowledge). For live retrieval via web search, it mainly sees the video title and the text summary on Google/Bing search results — it does not watch the video or read the full transcript at retrieval time.
• Gemini has a massive advantage here: because Google owns YouTube, Gemini can access the full transcript of any video. It reads the entire spoken content, not just the title and metadata. However, based on our own research, Gemini tends to favor the most popular videos — the ones with the highest view counts — rather than pulling uniformly from all available content.
• Claude and Perplexity handle YouTube more like ChatGPT: they see what appears in web search results (title, description, snippet) rather than full transcripts.

At a glance

ChatGPT (OpenAI)

Still the usage leader. ChatGPT heavily weights brand authority, training data presence, and web search results from both Bing and Google. It tends to recommend well-known, established brands and cites fewer niche sources. If you’re a challenger brand, ChatGPT is the hardest platform to crack.

Google Gemini / AI Overviews

A close second in usage and closing fast. Gemini leans heavily on Google’s own search index, meaning traditional SEO has the strongest carryover here. Its YouTube advantage is significant: Gemini can read the full transcript of any YouTube video, giving it far richer context about brands that publish video content. However, our research shows it favors popular videos (high view counts) rather than pulling uniformly from all content. If you’ve invested in SEO and YouTube, Gemini is where those investments compound.

Claude (Anthropic)

Claude has historically skewed technical — its user base over-indexes on developers, engineers, and technical decision-makers. It draws primarily from training data and emphasizes accuracy and nuance. When web search is enabled, it’s relatively willing to cite lesser-known domains if the content is credible and well-structured. If your company sells dev tools or technical products, Claude should be your #1 LLM priority.

Perplexity

The most citation-heavy of the major LLMs. Perplexity explicitly links to sources and pulls from a wider range of domains, including niche and recently published content. Only 11% of domains are cited by both ChatGPT and Perplexity, indicating very different source selection. For smaller brands and challenger domains, Perplexity is the friendliest surface.

Grok (xAI)

Grok blends pretraining with the X (Twitter) firehose and live web search. It’s the most real-time of the major models and the most responsive to trending content. Brand presence on X matters more for Grok visibility than for any other LLM.

Practical Playbook: How to Optimize for LLM Visibility

Here’s the actionable breakdown, organized by priority and effort level.

Technical Foundations

Start with these high-impact technical implementations, ordered from “evidence-backed today” to “hedge against what’s coming.”

1. Configure AI crawler access. Ensure your robots.txt allows GPTBot, OAI-SearchBot, Google-Extended, Anthropic-AI, PerplexityBot, and CCBot. This is the single most tractable change you can make: if these crawlers are blocked, no other optimization matters because your content is invisible to the models. Check your current state with curl https://yourdomain.com/robots.txt and audit any Disallow lines that match these user agents.

2. Implement comprehensive schema markup. Add JSON-LD structured data to your key pages. Prioritize FAQ schema on question-and-answer pages, Product or Organization markup on commercial pages, HowTo schema for instructional content, and comparison tables with specific attributes. This is the single best-evidenced technical lever in the current research (see the Structured Data section above for sources and mechanism).

3. Use semantic HTML. Proper heading hierarchy (H1 through H4), semantic elements (article, section, aside), and clean document structure help LLMs parse your content accurately. This is free and compounds with everything else.

Future-proofing & hedge bets

4. Create an llms.txt file. Full disclosure: as of April 2026, no major LLM crawler actually fetches or respects llms.txt, and there is no evidence it influences citations today. We still recommend adding one, and we’re treating it as cheap insurance. The file takes 20 minutes to produce, costs nothing to host, and puts you in position the moment a major agentic product ships with support for it. Given how fast this space moves, a new launch with llms.txt support in the next 12 months is more likely than not. The downside of adding it is zero. The downside of not having it the day it starts mattering is a scramble. Think of it as a term insurance premium: you pay very little, and you only notice the value if the low-probability event actually happens.

Content Strategy

5. Front-load answers. Open every piece of content with a direct, concise answer to the question it addresses. Remember: 44.2% of citations come from the first 30% of text.

6. Write in self-contained chunks. Structure your content so each section (50-150 words) can stand alone as a citable unit. Use descriptive headings that clearly label what each section contains.

7. Add statistics and data points. Weave quantitative data into your content wherever possible. Original research and proprietary data are especially valuable since they can’t be found elsewhere.

8. Include expert quotations. Quote named experts, industry leaders, and credible sources. This signals authority to LLMs and provides the kind of unique content that models prefer to cite.

9. Build comparison content. For commercial queries, create detailed comparison tables and feature breakdowns. These are highly extractable and directly useful for LLM synthesis.

10. Publish consistently and update regularly. With 65% of AI bot traffic targeting content from the past year, a regular publishing cadence and systematic content refreshes are non-negotiable.

Authority Building

11. Get listed on review and directory platforms. G2, Capterra, Trustpilot, Yelp, and relevant industry directories. Presence on these platforms correlates with 3x higher citation rates.

12. Build Reddit and Quora presence. Earn genuine mentions in relevant threads. Don’t spam. Contribute value. These platforms have outsized influence on LLM training data.

13. Invest in YouTube. YouTube mentions are among the top factors correlating with AI brand visibility. Product reviews, tutorials, and expert content on YouTube feed directly into LLM knowledge. Remember: Gemini reads full transcripts, so script with the same care you’d give a written page.

14. Pursue earned media. Press mentions, podcast appearances, and guest contributions on high-authority sites expand your footprint in LLM training data.

Third-party platform risk vs. reward

Not all third-party platforms carry the same risk. Promoting your brand on a platform where you can’t control the conversation can backfire — and with LLMs, the blowback is worse than on traditional search, because negative comments can end up in the training data and the model’s synthesized answer.

Measurement, Tracking, and Closing the Loop

15. Discover what people are actually asking about your brand. Before you optimize, you need to know what your customers are asking LLMs about you. Use a tool like Aiso to surface the real prompts mentioning your brand across ChatGPT and other models. Then check: does your website content actually cover those topics? If there are gaps between what people ask and what your site answers, that’s your content roadmap.

16. Generate and rank for your fan-out queries. Once you know the real prompts, generate the fan-out queries the model would run for each one (Aiso shows these automatically). Then check: do you rank on Bing and Google for those fan-out terms? If not, you have two levers:

• Own content: create or update pages on your site that directly target the fan-out queries with front-loaded answers, schema markup, and clear entity definitions.
• Third-party presence: get mentioned in the third-party pages that are ranking for the fan-out — Reddit threads, review sites, comparison articles, YouTube videos. If you can’t outrank the page that’s already in position, get cited within it.

17. Run systematic prompt testing. Identify the key questions and prompts in your category, then test them across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Track which brands appear and how often.

18. Monitor citation frequency over time. Several tools now track AI visibility — Aiso, Otterly.ai, Profound, Peec AI, Scrunch, Beamtrace, among others — or you can do it manually with systematic prompt testing. The point is to measure your brand’s share of voice in AI responses for target queries over time, not just check once.

19. Track by platform. Since only 11% of domains appear in both ChatGPT and Perplexity results, platform-specific tracking is essential. Don’t assume visibility on one platform means visibility on another.

What This Means for Your 2026 Strategy

The brands that will dominate LLM visibility in 2026 and beyond share a few characteristics.

They treat LLM optimization as a layer on top of SEO, not a replacement. Traditional SEO builds the authority and technical health that LLM visibility depends on. The winning approach is additive.

They invest in genuine brand building. Brand search volume is the #1 predictor of LLM citations. There’s no shortcut for this. Real brand awareness in the real world translates to AI visibility.

They structure content for machines as much as for humans. Clear structure, schema markup, self-contained sections, and front-loaded answers serve both audiences.

They diversify their platform presence. Multi-platform brands win. YouTube, review sites, Reddit, Quora, earned media — each platform contributes to the mosaic of signals that LLMs use to assess authority.

They measure what matters. Citation frequency, brand mention share of voice, and cross-platform visibility are the new KPIs. If you’re not tracking these, you’re flying blind.

Conclusion

LLM visibility isn’t a complete reset of search. It’s a re-weighting. Some classic signals (brand search volume, freshness, topical authority) matter as much as they ever did. Some (exact-match keywords, raw backlink counts, click-through rate) matter less. And a handful of new ones (entity clarity, structured-data extractability, multi-platform presence scored by AI crawlers rather than Google) are emerging as distinct factors.

What the GEO research does show — and this is the claim worth holding onto — is that the re-weighting is more meritocratic than classic SEO. Smaller, newer sites gain disproportionate benefit from optimization on content quality, structure, and information density. Those are things you can actually improve without a decade of link-building behind you.

The factors outlined in this post are mostly correlational, grounded in 2023-2024 peer-reviewed research supplemented by 2025-2026 citation tracking. They’re directional, not deterministic. Treat them as hypotheses to test on your own site, not rules to implement blindly.

The question is no longer whether AI will change search. It already has. The question is whether your content strategy is tracking the re-weighting.