62% of AI Citations Don’t Mention Your Brand: The Ghost Citation Problem

Getting your URL cited in an AI engine response is not the same as getting your brand mentioned — and the gap between those two outcomes is larger than most marketing teams realize. A study published in June 2026 by Semrush in partnership with growth advisor Kevin Indig analyzed 3,981 domain appearances across ChatGPT, Gemini, Google AI Overviews, and Google AI Mode, and found that 62% of those citations are what the researchers call “ghost citations” — the source link surfaces in the AI response, but the brand name is never spoken or written anywhere in the body text. If you have been measuring AI visibility by counting citations alone, you have been measuring the wrong thing.

What Happened

The Semrush Ghost Citations Study, published in June 2026 in partnership with SEO and growth strategist Kevin Indig, is one of the most granular analyses of AI engine source attribution conducted to date. Across 115 prompts tested in 14 countries, the researchers tracked exactly what happened each time a domain appeared in an AI response: was the brand mentioned by name, was it cited with a link, both, or neither?

The study analyzed 3,981 total domain appearances — a sample size spread across the four AI platforms that currently dominate how people get information online. The methodology is worth noting: this was not a theoretical analysis of how AI engines are designed to attribute sources. It was empirical observation of what actually happened when real prompts were run and real responses were captured across multiple countries and query types.

The headline finding is stark. 74.9% of domain appearances included a citation — a clickable link to the source — but only 38.3% included an actual brand mention, meaning the brand name appeared somewhere in the body of the AI’s response. Just 13.2% of appearances achieved both: a citation and a brand mention in the same response. An additional 25.1% of appearances included a brand mention but no citation — the AI named the brand without linking to it.

Do the math and the picture becomes clear. Of every 100 times an AI engine links to a domain, roughly 62 of those link appearances happen without the brand name being spoken or written in the response. The AI uses the content as a source, gives the footnote credit, but never names the company that produced it. Users who do not click through have received no brand signal from that interaction.

This distinction matters because AI responses are increasingly how people form their initial awareness of brands in a category. When a user asks ChatGPT to recommend project management tools, the brands that get named in the response get mindshare. The brands whose blog posts were used to support the response but whose names never appeared get nothing from that exchange — no awareness, no recall, no consideration placement. They are invisible participants in an answer that shaped the buyer’s perspective.

The geographic dimension adds complexity. Running prompts across 14 countries, the researchers found mention rates varying by a factor of nearly three: India and Sweden showed 50% mention rates, Canada came in at 44%, the UK at 41%, while Italy, Brazil, and the Netherlands registered mention rates of just 18-22%. A global brand running one unified AI visibility strategy is working with assumptions that hold in some markets and fail badly in others.

Query type creates equally dramatic variation. Short, conversational prompts generated nearly 100% mention rates. Long, structured queries that resemble research briefs generated just 2-3% mention rates. That is a 30x to 50x variation in brand mention probability depending entirely on how the user phrased the question — before any consideration of content quality or domain authority.

The study also found meaningful differences by query intent. Informational queries — explaining how something works, defining concepts — showed 89.3% citation rates but only 18% mention rates. Comparative queries generated 43.3% mention rates, more than double the informational baseline. How-to queries came in at 42.8%. Commercial queries showed 35.6% mention rates alongside 84.4% citation rates.

Strong consumer brands like Google and Apple appeared named more frequently than cited. Aggregators like Medium and Wikipedia showed the opposite pattern: cited heavily but rarely named. The study suggests that domain authority and content volume drive citation inclusion, while brand strength and query context drive mention frequency — and those two signals respond to different optimization strategies.

Why This Matters

The ghost citation problem is fundamentally a measurement crisis masquerading as a visibility problem. Most marketing teams and SEO professionals who are actively tracking AI visibility are using tools that report “appearances” or “citations” — and they are interpreting domain appearances as brand impressions. The Semrush data quantifies exactly how far apart those two things are.

Consider the downstream effect on brand awareness. If an AI engine cites your domain as a source for a claim about email deliverability rates but never says “According to [YourBrand]…” or names your company anywhere in the response body, the user has received a traffic-generating interaction but zero brand lift. At scale — across thousands of queries per day — a brand could be accruing significant AI-search referral traffic while generating essentially no AI-search brand awareness. Those are different business outcomes that require different measurement frameworks, different content strategies, and different definitions of success.

This finding challenges several widely held assumptions that have shaped the GEO (generative engine optimization) conversation since AI search became a mainstream concern in 2023 and 2024.

The assumption that more citations equals more brand visibility. The ghost citations data breaks this directly. A brand that has optimized purely for citation inclusion — producing data-heavy research, building the credibility signals that encourage AI engines to cite the domain — may have built a ghost citation factory. Cited everywhere, named nowhere.

The assumption that ChatGPT dominance means ChatGPT brand exposure. The data inverts this assumption. ChatGPT’s aggressive citation behavior (87% citation rate) actually makes it the platform least likely to mention brand names (20.7% mention rate). If brand awareness is your primary goal, concentrating resources on ChatGPT citation optimization may be channeling effort toward the wrong outcome on the wrong platform.

The assumption that a unified AI visibility strategy works across markets. The country-level data makes this untenable. The same brand, the same content, the same platform, produces 50% mention rates in Sweden and 18-22% in the Netherlands. Market-level AI behavior is not homogeneous, and treating it as such produces targets that are simultaneously too aggressive in some regions and too lenient in others.

The implications vary by team type. For content marketing teams, the question shifts from “are we getting cited?” to “are we being named when we get cited?” Those are different content architecture challenges — being a credible source versus being a recognizable source requires different structural choices in how content is written, attributed, and optimized.

For agency teams producing GEO reporting for clients, the measurement framework needs to split citation tracking from mention tracking as a baseline requirement. Any report that conflates domain appearances with brand mentions is overcounting brand impressions by approximately 2.6x, based on the ratio of citation rate (74.9%) to mention rate (38.3%) in the study. That is a significant misrepresentation of AI-channel brand value, and it will eventually surface as a credibility problem when clients correlate AI citation reports with actual brand awareness survey data.

For B2B marketers, the comparative query finding carries particular commercial weight. Buyers using AI engines for vendor evaluation — asking “best CRM for mid-market manufacturing” or “HubSpot alternatives for professional services” — generate 43.3% mention rates. Buyers asking informational questions generate 18%. The highest-intent stage of the buying journey, where brand comparison is active, also happens to be the stage where AI engines are most likely to name your brand explicitly. GEO investment in comparative content earns the highest brand return per dollar spent.

For brand managers thinking about AI brand safety, ghost citations introduce a risk dimension that the industry has barely begun to address. Your content can be used to support claims you would never endorse, in contexts you would never choose, without your name ever appearing. Your credibility is being borrowed; your brand is not being built. But the ghost citation dynamic also creates a compliance ambiguity: if your research is cited in a problematic context without your brand name attached, the connection to your organization is technically absent from the response — which may or may not provide meaningful protection depending on the regulatory context.

The Data

The core numbers from the Semrush Ghost Citations Study establish the citation/mention gap as the defining measurement problem in AI search marketing. The data breaks down across four key dimensions.

Citation vs. Brand Mention Rate by AI Platform

ChatGPT and Gemini represent the two poles of AI engine behavior. ChatGPT is a citation engine that rarely names brands; Gemini is a mention engine that rarely links out. A brand optimizing for one platform with a strategy designed for the other is engineering toward the wrong outcome.

Brand Mention Rate by Query Intent

The comparative query row is analytically significant. It is the only intent category where citation rate and mention rate converge. When AI engines handle comparative questions, they are almost equally likely to cite and to name — every citation comes with near-equal probability of an associated brand mention. Comparative query presence is the most brand-efficient position in AI search.

Brand Mention Rate by Geography

The geographic spread across documented markets is nearly 3x from top to bottom. Any global brand operating with a single AI mention rate target is either sandbagging for high-mention markets or setting unachievable goals in low-mention markets — and the mismatch compounds over time as GEO budgets are allocated uniformly against non-uniform opportunity.

Brand Mention Rate by Query Length

This table represents the most operationally useful finding in the entire study for content strategists. A brand that dominates short conversational query responses in its category is generating completely different brand value than one that dominates long research-query citations — even if raw citation counts look identical on a dashboard report.

A second important data point from Semrush’s AI Overviews study adds structural context: only 20-26% of URLs included in Google AI Overviews overlap with the top 10 organic search results, and the #1 organic ranking appeared in only 46% of AI Overview responses. The traditional SEO model — rank #1, capture disproportionate clicks — does not replicate in AI search. AI engines source from a broader, less predictable content pool, which means broad presence across a topic area matters more than deep optimization of a single ranking position. This makes the ghost citation measurement problem even larger: not only are citations failing to translate into brand mentions at the rate marketers assume, but the content generating those citations may be coming from ranks 2-30 rather than the #1 position where optimization effort has historically concentrated.

Real-World Use Cases

Use Case 1: SaaS Brand Auditing Its AI Visibility Measurement Stack

Scenario: A B2B SaaS company in the project management space has been tracking AI citations for six months using a GEO monitoring tool. Their dashboard shows strong citation growth quarter over quarter, but inbound sales intelligence tells a different story — fewer prospects are mentioning AI search as a discovery channel. The VP of Marketing suspects the metrics are inflated.

Implementation: The team works with their GEO tool vendor to segment all AI visibility data into three buckets: pure citations (link only, no brand name in response body), pure mentions (brand name in response, no link), and combined appearances (link plus brand name). They run this segmentation platform by platform and discover that 72% of their citations are on ChatGPT — the platform with a 20.7% mention rate — and that most of their cited content is long-form research reports that surface in long structured queries, where mention rates run at 2-3%. Based on the ghost citations data, they create 15 shorter, entity-rich content pieces — each 300-500 words, each with the brand name in close proximity to every key claim — targeting the conversational versions of their existing research topics. These are designed to appear in short-query responses where mention rates approach 100%.

Expected Outcome: Citation volume holds steady while brand mention rate increases measurably within 60-90 days as the shorter content starts capturing short-query responses. The measurement framework split alone delivers immediate clarity by giving leadership an accurate picture of how much AI-channel brand awareness the team is generating versus how much traffic they are capturing — two different business metrics that should not be reported as one.

Use Case 2: Global Agency Segmenting AI Strategy by Market

Scenario: A digital marketing agency managing AI visibility for a multinational enterprise cybersecurity client is producing consistent global GEO reports but getting pushback from regional teams. The continental Europe team says monthly mention rate targets feel unachievable. The UK team is consistently beating theirs. No one understands why the same strategy produces such different outcomes.

Implementation: Using the country-level mention rate benchmarks from the Semrush Ghost Citations Study — India and Sweden at 50%, UK at 41%, Netherlands and Italy at 18-22% — the agency rebuilds the client’s KPI framework with country-specific targets calibrated to documented AI platform behavior. For high-mention markets (UK, Canada), they concentrate GEO investment on citation quality and content depth, pushing toward 40%+ mention rates. For low-mention markets (Netherlands, Italy), they redirect resources toward building broader unlinked brand presence — press coverage, analyst report inclusion, relevant Reddit and YouTube brand mentions — which influences the behavioral signals AI engines weight for brand familiarity over time, and which the Semrush GEO framework identifies as meaningful inputs for long-term AI visibility.

Expected Outcome: Regional teams operate with targets matched to documented AI behavior in their specific markets. The client gets an honest, differentiated view of AI brand visibility by geography. Budget allocation shifts from uniformly distributed across markets to strategically concentrated where brand mention ROI is highest, improving overall program efficiency without requiring additional spend.

Use Case 3: DTC Brand Engineering Comparative Query Dominance

Scenario: A direct-to-consumer nutrition brand knows that buyers are using Gemini and ChatGPT for supplement research, but the brand rarely appears in AI responses despite strong organic search rankings in their category. They have been running a GEO strategy focused on informational content — ingredient guides, nutrition science explainers, how-to dosing guides — and it is generating citations but minimal brand mentions.

Implementation: Based on the study finding that comparative queries generate 43.3% brand mention rates versus 18% for informational queries, the brand builds a dedicated content cluster targeting comparison searches: “clean protein powder alternatives,” “best collagen supplements for joint health under $50,” and “creatine vs. beta-alanine: what the research shows.” They publish this content on their own site, then seed the key comparisons into relevant Reddit communities (r/supplements, r/nutrition) and YouTube video descriptions. Semrush’s GEO research identifies UGC platforms as meaningful signals for AI brand familiarity, and Reddit content in particular is heavily cited by both ChatGPT and Gemini. They simultaneously pitch data-backed comparison pieces to health and wellness editors at publications that AI engines source heavily in this vertical.

Expected Outcome: Within 90-120 days, brand mention rates in comparative queries begin increasing, particularly on Gemini — the platform with an 83.7% brand mention rate. The comparative content also serves a dual purpose: capturing high-intent organic comparison traffic on traditional search while building the category comparison presence that drives AI brand mentions. Two channels improved with one content investment.

Use Case 4: Content Team Building a Two-Tier Citation Architecture

Scenario: A marketing technology vendor’s content team produces primarily long-form research guides (2,500-5,000 words). Their AI visibility monitoring shows strong citation counts, but the ghost citations data makes clear that long structured content — the format comprising most of their cited pieces — generates brand mention rates of 2-3%. The team is effectively writing AI training material while getting negligible brand name return on content investment.

Implementation: The team audits its top 25 citation-driving pieces and for each one creates a companion article — a 300-500 word, conversationally structured piece targeting the short-query version of the same topic. Where the long-form piece is “The Complete Guide to Marketing Attribution Models,” the companion piece is “Which Marketing Attribution Model Should You Use?” Each companion article is entity-rich: the brand name appears in the first paragraph, every key data point is attributed explicitly (“According to [Brand’s] 2025 benchmark study, 68% of B2B marketers report…”), and the piece is formatted to be read directly in an AI response rather than linked to as supporting research. The long-form content remains as the citation anchor; the shorter content is engineered specifically for brand mention visibility in short conversational queries.

Expected Outcome: Over six months, brand mention rates increase as the companion content begins appearing in the short-query responses where mention rates are highest. Citation volume from the long-form research content holds stable. The two-tier architecture captures both outcomes simultaneously — citation-driven traffic from research content and brand mention visibility from conversational-query-optimized content — rather than sacrificing one for the other.

Use Case 5: Enterprise Brand Safety Auditing Ghost Citation Context

Scenario: An enterprise financial services company has produced extensive benchmark research and data reports over four years. A compliance officer raises a concern after discovering that company research is being cited in AI responses that contextualize the data in ways the company never intended. Because most of these are ghost citations — link only, no brand name — the connection to the company is invisible in the response, but the underlying source document has the company’s name on it.

Implementation: The company establishes a quarterly ghost citation audit workflow. Using their AI visibility tool, they identify the top 50 content pieces generating citations in AI responses, then manually sample the actual AI responses to read the full context in which each citation appears. Content cited in contexts that create regulatory ambiguity or reputational risk is flagged for the legal and communications teams. Separately, the team updates high-citation content pieces to embed explicit brand attribution framing — “According to [Brand’s] 2025 research…” in the body of every key claim — because Semrush’s GEO research supports the premise that AI engines carry attribution language from source content into their responses when that attribution is clearly stated in the document. This converts ghost citations into attributed citations over time, simultaneously improving brand safety documentation and building brand mention rates.

Expected Outcome: Reduced instances of company research appearing in AI responses in unanticipated contexts without attribution. Improved legal and compliance documentation of AI-mediated content usage. A secondary measurable benefit: brand mention rates increase in subsequent monitoring cycles as the attribution framing in updated content carries through into AI-generated responses.

The Bigger Picture

The ghost citations finding is a symptom of a structural reality: AI engines do not experience the web the way search engines do. Traditional search was a navigation layer. Users asked a question, received a labeled list of sources, and chose where to go. Brand visibility correlated directly with rankings because the entire interface was a menu of labeled options and every link had a name attached to it.

AI search is a synthesis layer. The engine reads sources, constructs an answer, and delivers a response. Brand names appear in that response only when the synthesis logic requires naming specific entities — which happens naturally and frequently in comparative and how-to contexts, and rarely in informational ones. The citation footnote in an AI response is an accountability mechanism and an auditability feature; it is not a brand promotion mechanism. The engines are not citing sources to give them exposure — they are citing sources to justify their outputs.

This reframes what content marketing means in an AI-first distribution environment. Publishing content that AI engines cite means contributing to the knowledge base that shapes AI answers. But the ghost citations data makes the gap explicit: shaping AI answers does not automatically translate to your brand being named in those answers. The two goals — being a credible source and being a recognizable brand in AI responses — require different content types, different entity-signal optimization, and different distribution strategies.

The broader GEO landscape is beginning to consolidate around this insight. Semrush’s generative engine optimization framework identifies quotes, statistics, and data-heavy content as achieving 30-40% higher AI visibility because AI engines prefer citable specificity. The ghost citations study adds the necessary complement to this: specificity increases citation likelihood, but entity-richness — placing your brand name in close structural proximity to your specific claim, with explicit attribution language — is what drives mention likelihood. Both are required for complete AI brand visibility, and neither alone is sufficient.

What the four-platform study also reveals is that platform behavior is diverging, not converging. ChatGPT and Gemini are moving in opposite directions on the citation/mention spectrum with no indication of convergence. Google AI Mode is developing a distinct behavioral profile from both. Google AI Overviews sits in a different place again. Marketers who treat all AI engines as interchangeable — running one content strategy, one optimization approach, one measurement framework across all platforms — are averaging across systems that behave in fundamentally different ways and are rewarding fundamentally different optimization signals.

The GEO conversation is approximately three years old as an industry-level discussion. The ghost citations study marks the point where that conversation matures past “how do we get cited?” and into “how do we convert citations into brand mentions?” That is a more complex problem. It requires platform-specific strategy, more precise measurement instrumentation, and content architecture designed for both citation acquisition and entity-rich mention triggering. Most teams are still on version one of this problem. The teams that move to version two in 2026 will hold a compounding measurement and strategy advantage.

What Smart Marketers Should Do Now

1. Split Your AI Visibility Metrics Into Citation Rate and Mention Rate — Starting with the Next Report

Any AI visibility report that does not distinguish between domain citations and brand name mentions is overcounting brand impressions by up to 2.6x, based on the Semrush study’s ratio of 74.9% citation rate to 38.3% mention rate. This is not a technical limitation — it is a reporting choice. Push your GEO tool vendor or internal analytics team to segment these before the next reporting cycle. Citation rate measures traffic and referral potential; mention rate measures brand awareness and consideration placement. Combining them into a single “appearances” metric obscures which business outcome you are actually generating and makes it impossible to optimize toward the right one.

2. Build Platform-Specific Optimization Strategies Instead of One Unified GEO Plan

ChatGPT (87% citation rate, 20.7% mention rate) and Gemini (21.4% citation rate, 83.7% mention rate) require entirely different optimization approaches because they reward entirely different content and entity signals. For ChatGPT, accept that it is primarily a traffic and source credibility channel — invest in content depth, data quality, and the domain authority signals that drive citation inclusion. For Gemini, optimize for brand name integration in shorter, entity-rich formats where your brand name surfaces naturally in response bodies. For Google AI Mode, which the study suggests produces roughly double ChatGPT’s mention rate, concentrate on product-specific and evaluation-stage queries where AI Mode is most active. One undifferentiated strategy cannot optimize effectively for all three simultaneously.

3. Create Content Specifically Targeting Comparative and How-To Queries

The query intent data is the single most actionable output of the ghost citations study: comparative queries generate 43.3% mention rates, how-to queries generate 42.8%, while informational queries generate just 18%. If you audit your current AI-targeted content and find it is primarily informational — guides, explainers, reference documentation — you are concentrating publication effort in the lowest-mention query category. Build a dedicated comparative content cluster: “[Your Brand] vs. [Competitor Name],” “best [product category] for [specific use case],” and “how to [accomplish a specific workflow] using [your product type].” These content types are not just more likely to generate brand mentions — they map to higher commercial intent, making each mention more valuable in terms of downstream conversion probability.

4. Establish Country-Specific Brand Mention Benchmarks for Every Market You Track

The geographic variation documented in the study — 50% mention rates in India and Sweden versus 18-22% in Brazil and the Netherlands — makes global average targets operationally misleading. A “30% AI mention rate” global target is too aggressive for continental European markets based on observed AI behavior there, and too conservative for high-mention markets like India and Sweden. Audit your AI visibility reporting by geography, use the country-level benchmarks from the Semrush study as baseline calibration points, and set market-specific KPIs. Then allocate GEO investment toward high-mention markets where effort is most likely to produce measurable brand awareness improvement.

5. Retrofit Entity Attribution Into Your Top Citation-Generating Content

If your GEO monitoring tool surfaces which content pieces are being cited in AI responses most frequently, you have an immediately usable improvement list. Go back into those top-cited pieces and strengthen entity signals throughout: ensure your brand name appears in close structural proximity to every key claim (“In [Brand’s] 2025 study of 5,000 marketing teams…” rather than “Research shows…”), add explicit attribution language for every statistic and finding rather than allowing AI engines to cite the data point in isolation, and remove ambiguity about who produced the content. Semrush’s GEO research supports the premise that content with explicit entity attribution improves the probability that AI engines carry that attribution into their synthesized responses — converting ghost citations into brand mentions without requiring new content production.

What to Watch Next

Platform Attribution Policy Changes: The citation/mention gap is partly a design choice by each AI platform’s product team. OpenAI has not standardized how ChatGPT attributes sources in response bodies — its 20.7% brand mention rate is a product of current behavior and current training, not a fixed technical ceiling. Any public announcements from OpenAI, Google DeepMind, or Perplexity about changes to source attribution in AI responses would immediately and materially affect brand mention rates across the industry. Monitor platform developer documentation, product changelogs, and system card updates through Q3 and Q4 2026.

Google AI Mode Global Expansion: Google AI Mode — which the ghost citations study identifies as producing roughly double ChatGPT’s brand mention rate — was in limited rollout as of June 2026. Its expansion to additional countries will significantly shift AI visibility dynamics for brands in the Google ecosystem. Markets currently relying on traditional Google Search are about to encounter a platform with meaningfully different brand attribution behavior than either AI Overviews or classic blue-link results. Build platform-specific measurement frameworks for AI Mode before your markets switch rather than after.

GEO Measurement Standardization: No shared industry standard currently exists for reporting AI brand visibility. Multiple tool vendors — including Semrush, BrightEdge, and Conductor — are actively developing measurement frameworks. The vendor that establishes citation/mention split as the industry baseline will likely shape how the field reports AI channel ROI for the next three to five years. Watch for methodology white papers and framework publications from major SEO and analytics platforms in H2 2026, and position your organization to adopt the emerging standard early rather than retrofitting an incompatible reporting history.

Training Data Recency and Brand Mention Decay: As AI engines shorten their training data refresh cycles, brands that were prominent in older training windows may lose AI mention share to brands producing and distributing content more actively today. The effect compounds: a brand that was heavily mentioned in training data from 2023 and 2024 but has reduced content output since then may see declining mention rates that do not correlate with any change in their current content quality. Track AI mention rate on a quarterly basis rather than point-in-time, and watch specifically for decline trends that diverge from citation volume trends — that divergence may be an early signal of training data recency effects working against you.

Bottom Line

The Semrush Ghost Citations Study delivers a specific, empirically grounded correction to how the marketing industry has been measuring AI visibility: a citation is not a brand mention, and the gap between those two outcomes is 62 percentage points across the AI engines that handle a growing share of how buyers discover and evaluate brands. Teams that split these metrics, build platform-specific content and measurement strategies that reflect how ChatGPT and Gemini actually behave differently from each other, and concentrate GEO content investment on comparative and how-to query types will generate meaningfully more brand awareness value from the same effort. The AI search landscape has already bifurcated into high-citation engines and high-mention engines — and that divergence is continuing, not converging. The brands that recognize this measurement distinction in 2026 and restructure their GEO strategies accordingly will carry a compounding advantage as AI search volumes grow and the stakes of brand visibility in AI responses increase.