This post was sponsored by Victorious. The opinions expressed in this article are the sponsor’s own.
A year into the shift toward AI search, the marketing industry is full of confident takes about the factors that impact AI visibility. But we’ve seen very little data to support commonly held assumptions.
We wanted to see what correlations we could find between traditional search performance and AI mentions and citations. So we built a study to see if we could uncover evidence-based recommendations from the data.
The Study Methodology: Comparing Traditional Search vs. AI Search Performance
To compare how brands perform in traditional search versus AI search, we needed a dataset that captured both signals for the same companies during the same period of time.
We built it out in four phases.
Step 1: Determine The Brand Set.
We selected a representative cross-section of 177 brands across five verticals: healthcare, SaaS, financial services, ecommerce/retail, and legal services.
Step 2: Capture The AI Visibility Signal.
For each brand, we tested vertical-specific prompts across eight AI platforms: ChatGPT, Perplexity, Gemini, Google AI Overview, Google AI Mode, Microsoft Copilot, Claude, and Meta AI. That gave us 107,011 AI responses to analyze.
For every response, we recorded two things: whether the platform named the brand (mention), and whether it linked to the brand’s domain as a source (citation).
Step 3: Pull The Organic Performance Data.
For the same 177 brands, we tracked domain-level organic performance in Semrush during the first quarter of 2026, including traffic trends and Authority Scores.
Step 4: Cross-Reference The Two Datasets.
We joined the AI visibility data with the organic data so every brand had three comparable measures: mention rate, citation rate, and Authority Score. That structure let us look at the relationship between traditional ranking signals and AI visibility, and whether those factors were more or less related across the different verticals.
Why We Tracked Mention Rate & Citations Separately
One metric doesn’t capture AI visibility, so we tracked both mention rate and citation rate as separate signals. For example, a brand can be mentioned often and cited rarely, or cited often and rarely mentioned. Tracking both separately, rather than collapsing them into a single “AI visibility” score, ended up being central to the nuances we could pull from the different verticals.
Finding 1: Most Brands Have No AI Mentions At All
Of the 177 brands in our dataset, only 18 had any AI mention rate above zero in Q1 2026. That means 89.8 percent of the brands we tested were largely absent from AI search across the eight platforms we measured. They weren’t mentioned. The brands weren’t surfaced in relation to answers, as sources, or examples.
This runs counter to a lot of the current industry chatter, which treats AI visibility as a race that’s already well underway. Our data shows a very different picture. For an overwhelming number of brands, the race hasn’t yet begun.
The fact that only 18 of the 177 brands in our research registered any AI mentions at all indicates that brands willing to take AI visibility seriously now will be competing against a small number of incumbents in their vertical, not against the entire category.
Finding 2: AI Visibility Patterns Vary By Vertical
Once we broke the data down by vertical, three distinct patterns emerged.
“Q1 2026 Quarterly Search Report: Mention rate vs. citation rate, by vertical: Healthcare, SaaS, and Financial Services” created by Victorious. May 2026.
Brands within these three verticals were consistently mentioned and cited, but for different reasons. Healthcare brands benefit from clear entity identifiers such as names, locations, specialties, and network affiliations, which reinforce the signals that AI platforms use to evaluate expertise and authority. SaaS brands are commonly featured on third-party platforms such as G2, Reddit, and LinkedIn, where products are discussed by users and reviewers. Financial Services benefits from strong editorial media presence on platforms like MarketWatch, Bankrate, and NerdWallet, which are common sources AI platforms turn to for financial questions.
Financial Services was also the only vertical where citation slightly exceeded mention, which suggests AI platforms trust the content slightly more than it trusts specific brands yet.
In each case, the brands that show up have something AI platforms can attach the brand identity to: structured data, third-party validation, or editorial coverage. The brands that don’t show up usually lack one or more of those.
Mentioned More Than Cited: Ecommerce & Retail Brands
“Q1 2026 Quarterly Search Report: Mention rate vs. citation rate for Ecommerce/Retail” created by Victorious. May 2026.
Ecommerce posted the widest gap in our dataset. AI platforms recognize these brands but pull their source material from somewhere else, usually marketplaces, aggregators, and review sites rather than the brands’ own domains.
For these brands, recognition comes from marketplace presence and consumer familiarity. The bigger challenge for ecommerce brands is giving AI platforms content worth citing on their own domain, instead of leaving the field to Amazon, Reddit, and review aggregators.
Cited But Rarely Mentioned: Legal Services
“Q1 2026 Quarterly Search Report: Mention rate vs. citation rate for Legal Services” created by Victorious. May 2026.
Legal services posted the inverse pattern as ecommerce brands. AI platforms regularly source content from legal sites, but they rarely credit the firm behind the article.
Closing that gap means building the entity signals that connect a piece of content back to a recognizable firm.
Findings 3 – 4
Each AI platform draws from a different set of sources.
Personalization may be compounding early AI visibility.
Google’s Personal Intelligence update pulls signals from a user’s Gmail and Photos into AI Mode responses, biasing results toward brands the user has already encountered. If that effect holds, brands that win a user’s first AI interaction on a topic could compound their visibility faster than later entrants. The full report walks through what we’re watching in Q2 to test this.
Key Takeaway
If you take away nothing else from this data, remember that you haven’t lost first-mover advantage. With only 18 of the 177 brands we measured earning mentions AI search, there’s still white space in your vertical waiting to be claimed.
Google upgraded AI Mode with Gemini 3.5 Flash as the new default model and redesigned the Search box with AI capabilities, the company announced at I/O. The changes also include search agents and an international expansion of Personal Intelligence.
Liz Reid, VP and Head of Search, said AI Mode has passed one billion monthly users. She said queries have more than doubled every quarter since launch and reached an all-time high last quarter.
Gemini 3.5 Flash As Default In AI Mode
Google made Gemini 3.5 Flash the new default model in AI Mode for everyone globally, starting today.
Google redesigned the Search box with AI. Reid called it the biggest upgrade to the Search box in over 25 years.
The new box expands dynamically to accommodate longer queries. It offers AI-powered suggestions beyond autocomplete and accepts multimodal inputs, including images, files, videos, and Chrome tabs. Standard search results still appear alongside AI features. The redesigned box is rolling out today in all countries and languages where AI Mode is available.
Separately, users can now ask follow-up questions directly from an AI Overview, which then flows into a conversational AI Mode session. Context carries over between the two. This is live today on desktop and mobile worldwide.
Search Agents
The company announced search agents that run in the background to monitor the web and deliver updates. The first type, information agents, will look across the web, including blogs, news sites, and social posts. They’ll also tap Google’s real-time data on finance and shopping.
Information agents will launch this summer for Google AI Pro and Ultra subscribers.
Agentic booking is also expanding to local experiences and services. Users can share criteria and get results with pricing and availability. For select categories, such as home repair and pet care, Google said users can ask it to call businesses on their behalf. These booking features will roll out to everyone in the U.S. this summer.
Google also announced new agentic shopping capabilities in Search, with details on a separate Shopping blog post.
Generative UI And Mini Apps
The Antigravity platform and Gemini 3.5 Flash coding capabilities are coming to Search. In response to a query, Search can generate custom visual tools and simulations tailored to the question.
Google said Search can also build custom dashboards or trackers that users can return to over time. Reid compared these to mini-apps for specific tasks, like tracking a health routine or managing a move.
The generative UI capabilities will be free for everyone in Search this summer. Antigravity mini apps will start rolling out for AI Pro and Ultra subscribers in the U.S. in the coming months.
Personal Intelligence Expands Internationally
Personal Intelligence in AI Mode is expanding to nearly 200 countries and territories across 98 languages. The feature no longer requires a subscription.
Users can connect Gmail and Google Photos to AI Mode, with Calendar support coming. The feature first launched for AI Pro and Ultra subscribers in January and expanded to free U.S. users in March.
Why This Matters
These updates extend the trajectory Pichai outlined in April. He called search an “agent manager” and predicted users would run long-running tasks rather than browse results. Search agents and custom mini apps move in that direction.
More query activity within Google’s AI interfaces may mean that fewer queries result in outbound clicks. Google says queries hit an all-time high, but independent measurements have consistently found that AI Overviews reduce clicks on queries where they appear.
Personal Intelligence expanding without a subscription in nearly 200 countries is the most concrete change to monitor. Personalized results at this scale could affect how Google selects which content to surface. When connected, the system can draw on a user’s email and photos alongside web results.
Looking Ahead
Gemini 3.5 Flash is now available in AI Mode. The redesigned Search box is starting to roll out. Personal Intelligence is expanding to nearly 200 countries and territories without a subscription requirement.
Several features are scheduled for this summer with different availability tiers. Information agents and Antigravity mini apps will require an AI Pro or Ultra subscription. Agentic booking will be available to everyone in the U.S. Generative UI will be free for everyone in Search.
No timeline was given for Google Calendar integration with Personal Intelligence.
Google is expanding its SynthID verification tools to Search today, with Chrome support planned over the coming weeks. Users will be able to check the origin of images through Search features such as Lens, AI Mode, and Circle to Search.
The company is also launching an AI Content Detection API on Google Cloud, initially available to a group of trusted partners. Several companies are bringing SynthID watermarking to their AI-generated content, according to a blog post by Laurie Richardson, VP of Trust & Safety, and Pushmeet Kohli, Chief Scientist at Google Cloud and VP at Google DeepMind.
SynthID Verification In Search & Chrome
Google said it is expanding SynthID verification to Search today and plans to bring it to Chrome over the coming weeks.
Users can check whether an image was made with AI through features like Lens, AI Mode, and Circle to Search. You can ask questions like “Is this made with AI?” or “Is this AI generated?” to get verification results.
SynthID verification was already available in the Gemini app for images, video, and audio. It works by embedding imperceptible digital watermarks into AI-generated content.
C2PA Content Credentials
Google is also adding verification for C2PA Content Credentials, an industry standard for recording how media was created and modified.
The C2PA verification feature is rolling out in the Gemini app starting today and will roll out to Search and Chrome in the coming months.
AI Content Detection API
Google is launching a new AI Content Detection API on Google Cloud’s Gemini Enterprise Agent Platform, available to select partners. The API is a Google Cloud offering that Google says can detect AI-generated content made by Google and other popular models.
The API can help businesses evaluate and manage media across their platforms. Use cases include sorting feeds, preventing insurance fraud, fact-checking, and labeling synthetic media.
Initial partners include Shutterstock, Snap, Avid, Fox Sports, and Canva.
Industry Adoption Of SynthID
Companies including OpenAI, Kakao, and ElevenLabs are bringing SynthID technology to their AI-generated content. Google has open-sourced its SynthID text watermarking technology and partnered with NVIDIA to watermark AI-generated video from NVIDIA’s Cosmos models.
Meta, a fellow C2PA Steering Committee member, will start labeling camera-captured media with Content Credentials on Instagram. This means photos and videos shot on Pixel phones will be recognized and labeled on Instagram as camera-captured media.
Why This Matters
Google has been developing content-provenance tools since it first introduced SynthID in 2023. At that time, the technology was limited to select Google Cloud customers and was limited to images. The expansion to Search and Chrome moves verification from a specialized tool into surfaces where people encounter content every day.
The AI Content Detection API opens a different use case. Publishers and platforms that need to check whether content was made with AI will be able to access that capability through Google Cloud.
Searchers can already check image context through features like “About this image,” which Google expanded to Circle to Search and Lens in 2024. The SynthID verification adds a layer that checks for watermarks embedded at the point of creation, rather than relying on metadata that can be stripped.
The broader industry adoption of SynthID is worth watching. If more AI-generated media carries SynthID watermarks, Google’s verification tools have a wider base of content to check against. But SynthID only detects content watermarked with SynthID. Content from AI tools that don’t use it may not be identified through SynthID verification.
Looking Ahead
C2PA Content Credentials verification will come to Search and Chrome in the coming months. Google didn’t share specific timelines for broader availability of the AI Content Detection API beyond its initial partner group.
Google used its I/O 2026 event to introduce Universal Cart, a new AI-powered shopping experience designed to work across Search, Gemini, YouTube, Gmail, and participating merchants.
The announcement signals another major step in Google’s broader push toward “agentic commerce,” where AI systems do more than recommend products. Instead, they actively help users manage shopping decisions, monitor pricing, surface deals, and eventually complete purchases on their behalf.
Universal Cart also builds on Google’s expanding Universal Commerce Protocol (UCP), which the company described as a shared infrastructure layer meant to make cross-platform shopping and checkout more seamless.
While many marketers have focused heavily on AI-generated search experiences over the past year, this launch suggests Google is equally focused on turning AI into a transactional commerce layer.
Universal Cart Turns Shopping Into A Persistent AI Experience
According to Google, Universal Cart functions as an intelligent shopping cart that follows users across Google properties and participating merchants.
Users can add products while browsing Google Search, chatting with Gemini, watching YouTube, or even reading Gmail. Once products are added, the system continuously works in the background to monitor deals, price drops, inventory availability, and purchase opportunities.
Google says the experience is powered by Gemini models and will continue improving as the models evolve.
One of the more notable elements of the launch is how aggressively Google is positioning Universal Cart as proactive rather than reactive.
The company says the cart can identify product incompatibilities, suggest alternatives, surface loyalty perks, and recommend savings opportunities automatically.
Image credit: Google
Google also confirmed the system integrates with Google Wallet, allowing the cart to reference payment methods, loyalty programs, and merchant offers during the shopping process.
Some of these checkout features will be rolling out with large merchants including Nike, Sephora, Target, Ulta Beauty, Walmart, Wayfair, and other Shopify merchants this summer.
Image credit: Google
For users building more complicated purchases, such as custom PCs with parts from multiple retailers, Google says the cart can help validate compatibility issues before checkout.
Google Expands The Universal Commerce Protocol
The launch of Universal Cart also serves as a major expansion of Google’s Universal Commerce Protocol initiative.
Google first introduced UCP earlier this year as a common language for commerce systems and AI agents. At I/O, the company confirmed the protocol is already gaining broader retailer and technology partner adoption.
Google says UCP helps enable a smoother checkout process across merchants while still allowing brands to remain the merchant of record.
The company also announced several geographic and vertical expansions tied to the protocol:
UCP-powered checkout is expanding into Canada and Australia, with the U.K. planned later
UCP is coming to YouTube in the U.S.
Google plans to expand into additional commerce categories, including hotel bookings and local food delivery
This portion of the announcement may ultimately matter more to advertisers and retailers than the cart itself.
Google appears to be building a commerce infrastructure layer that connects discovery, shopping behavior, checkout, payments, and AI agents into one ecosystem.
For retailers already investing heavily into Merchant Center feeds, product data quality, and omnichannel commerce experiences, this likely increases the importance of structured product information even further.
What This Means For Advertisers And Retailers
Universal Cart is another strong signal that Google wants shoppers spending more of the purchase journey inside Google-owned experiences.
Historically, Google Search primarily sent users outward to retailer websites. Universal Cart starts pulling more of that activity back into Google itself.
Now, Google is positioning its platforms as the place where users discover products, compare options, monitor pricing, manage carts, and potentially complete purchases.
That creates both opportunities and new challenges for advertisers.
Retailers with strong product feeds, accurate inventory data, loyalty integrations, and competitive pricing may gain stronger visibility across these experiences.
It also increases the importance of Merchant Center optimization beyond traditional Shopping campaigns.
Product data is increasingly becoming the foundation for how products appear across AI-driven discovery surfaces.
The YouTube expansion also stands out to me.
Google continues tying video engagement more closely to shopping behavior and checkout infrastructure. That could create more pressure for brands to think about YouTube as a ecommerce channel, not just a video awareness platform.
From a measurement standpoint: If more shopping activity happens inside Google interfaces, advertisers may need to rethink how they evaluate attribution, assisted conversions, and customer journey reporting across channels.
Looking Ahead
Universal Cart is in its infancy stage, and many of the more advanced agentic commerce features will likely take time to mature.
Even so, this announcement offered a clearer picture of where Google appears to be heading with shopping.
The company is moving beyond AI-enhanced product discovery and deeper into the shopping journey itself.
From product recommendations and cart management to pricing insights and checkout infrastructure, Google is steadily expanding how much of the buying process happens inside its own platforms.
For advertisers and retailers, that could eventually change far more than just where ads appear.
It may also change how brands measure influence, attribute conversions, and compete for visibility during the purchase journey.
YouTube brought several AI-focused updates to Google I/O this year, but two announcements stood out more than the others.
The platform is introducing a new conversational discovery experience called “Ask YouTube” alongside expanded AI video remixing powered by Gemini Omni.
Together, the updates suggest YouTube is putting more focus on helping users discover content through natural-language interactions while making Shorts creation easier and faster for creators.
YouTube also spent considerable time discussing creator protections alongside the rollout, including watermarking, metadata labeling, opt-out controls, and expanded likeness detection tools tied to AI-generated remixes.
One of YouTube’s bigger AI announcements at I/O was a new conversational search feature called “Ask YouTube.”
According to Google, the experience allows users to search using more detailed questions instead of relying on traditional keyword searches.
Google’s examples included searches like:
Tips for teaching a child to ride a bike
Finding cozy game reviews before bedtime
Refining searches through follow-up questions
Rather than returning a standard list of videos, Ask YouTube compiles content from across YouTube, including both long-form videos and Shorts, into what the company describes as an “interactive, structured response.”
The update pushes YouTube closer to the same conversational discovery experience Google is increasingly building across Search through AI Overviews and AI Mode.
Instead of users manually sorting through results themselves, YouTube’s systems may play a larger role in interpreting intent and organizing recommendations around the query itself.
Ask YouTube is currently available to Premium members ages 18 and older in the United States through youtube.com/new, with broader rollout plans expected later.
Gemini Omni Expands AI Remixing Inside YouTube Shorts
Another major announcement focused on Gemini Omni integration inside YouTube Shorts Remix and the YouTube Create app.
YouTube described Gemini Omni as an upgrade designed to help creators generate new video variations from prompts and images while making remixing faster and easier inside Shorts.
According to the announcement, creators can:
Change scenes into different visual styles
Insert themselves alongside creators
Generate new concepts while preserving context from the original video
Perform more advanced video and audio edits automatically
Google says the system handles more of the editing complexity behind the scenes, reducing some of the technical work traditionally required for video remixing.
What stood out most from YouTube’s presentation was how heavily the company framed these tools around creator participation rather than pure automation.
Many recent AI creative announcements across Google products have emphasized efficiency and scale. YouTube’s messaging leaned more toward helping casual creators participate in trends and create content more easily.
The company also spent significant time discussing creator protections.
AI-generated remixes created through Omni will include digital watermarks, identifying metadata, and links back to original videos.
Creators can also opt out of visual remixing inside Shorts entirely.
YouTube additionally announced expanded access to its likeness detection tool for creators ages 18 and older. The system is designed to help creators identify and manage AI-generated uses of their likeness.
Gemini Omni remixing is rolling out now at no cost inside Shorts Remix and the YouTube Create app.
What These Updates Could Mean Next
Ask YouTube suggests YouTube may gradually shift toward a more conversational discovery experience instead of relying as heavily on traditional search behavior alone.
That could eventually create new challenges for creators, marketers, and advertisers trying to understand how content is surfaced and discovered inside the platform.
Historically, YouTube optimization has depended heavily on measurable signals like search queries, clicks, watch time, thumbnails, subscriptions, and recommendations.
Conversational discovery introduces more interpretation between the user query and the final content recommendation.
That creates a situation where users may become less likely to search using highly trackable keywords and more likely to rely on broader conversational prompts and follow-up questions.
Advertisers are already navigating similar visibility and reporting concerns across AI Overviews and AI-powered Search experiences.
If YouTube continues moving in that direction, measurement and attribution may become increasingly difficult there as well.
Google did not announce any ad-specific changes tied to these updates.
The announcements remained heavily focused on creator tools, remixing capabilities, and user experience improvements.
Still, the longer-term implications around reporting transparency, discovery visibility, and AI-organized content experiences will likely be worth watching as these features expand more broadly across YouTube.
Google’s recent definition of commodity vs. non-commodity content is a bit meh. Meh if I’m being kind. Downright useless if I’m being more reasonable.
Complete and utter rubbish if I’ve had a drink.
Image Credit: Harry Clarkson-Bennett
They all read like headlines you’d see in Discover and scroll past very quickly.
Maybe in a few years, that’ll be all that’s left, and that’s what Googlers are prepping us for. Personally, I think it’s far more likely their idea of quality, interesting content is just a bit rubbish.
Marble vs. grape juice – what a stupid title. Although interesting that they specify this is a video. Don’t hate the shoe one. No idea how that will make money for anyone, however… Doesn’t matter to Google.
Anyway, here’s how I think you can create unique, interesting content that still drives actual value to your business. (Hint: It’s not about grape juice).
TL;DR
Commodity content is doomed for two reasons: It is easily summarized (because it has been done to death), and it doesn’t make (as much) money in a zero-click world.
If you are creating content just for SEO and have nothing unique to offer, stop. You are throwing money down the drain.
Be more than an SEO. Help other teams structure their workflows to generate the maximum value from all channels, with things like demand analysis.
Google calculates the uniqueness of a document using a custom “information gain” score at a query and document level.
Why Commodity Content Is Doomed
People are like water. We take the easiest possible route. One that really doesn’t include clicking to find an answer, even if said answer is riddled with BS.
“Focus on making unique, non-commodity content that visitors from Search and your own readers will find helpful and satisfying. Then you’re on the right path for success with our AI search experiences, where users are asking longer and more specific questions — as well as follow-up questions to dig even deeper.”
This means we have to focus our efforts elsewhere.
We have to focus our time and efforts on content more likely to drive legitimate value. Content that cannot easily be summarized by AI adds something of real value to the user and hasn’t already been thrashed to death by savvy SEO teams.
If you’re unsure whether to create content or not, ask yourself two questions:
Are we creating this just for SEO?
Are we adding anything unique to the existing corpus of information?
If you answered 1. Yes and 2. No, throw it straight in the bin.
You do not have the time, money, or resources anymore to spend time on content that doesn’t drive value.
Does This Mean Things Like Search Volume Are Useless?
At an individual keyword level, search volume has been declining in value for a long time. We just can’t generate the value we once could, and it isn’t coming back.
But search volume just indicates demand. If you’re savvy and use monthly data, you can help content, social, paid marketing, and editorial teams understand when users really care about a topic.
In this capacity, your job is to help teams understand when to create or update content, what that content should cover, and crucially, why it’s spiking in search at this particular time.
Five years’ worth of searches in Google for [family holidays] (Image Credit: Harry Clarkson-Bennett)
If we take searches for [family holidays] in Google Trends as an example, there is clear and obvious consistency. Searches spike every January as people plan their family holidays for the year ahead in the bleak midwinter.
So you should still get your core family holiday content ready for January. But as we shouldn’t operate in a silo, you should share this with social and travel teams so they know what time of year this type of content will generate the most value.
Planning and structure take center stage.
It is no longer about “Create x, get y.” That click-based marketing is dead.
Commodity Or Not Commodity
Loosely, this header was a Shakespearean-based to be or not to be joke, which is a. clunky and b. outside of my wheelhouse.
Image Credit: Harry Clarkson-Bennett
Now I’ve had to explain it.
I wrote about this in “How to do evergreen content in 2026 and beyond.” Which is, ironically, quite a commodity topic. But it has evolved. There’s new stuff to share. You can make commodity, non-commodity.
But you need to have a level of understanding and expertise that can really elevate a topic. That requires experience, a level of uniqueness, and a platform. Your content needs to be found, and what we have always done in search is unlikely to be anywhere near as valuable.
The Pillars Of Non-Commodity Content
Uniqueness.
E-E-A-T.
Engagement.
Structure.
Uniqueness
Uniqueness is the bedrock of everything when it comes to content that will continue to drive value. Without uniqueness, there’s no E-E-A-T. You won’t generate any shares, likes, comments, or links. Certainly not any good ones.
You can make this as fancy as you like.
If you’re lucky enough to have access to high-quality data sources like Similarweb, you can create some truly brilliant proprietary metrics that elevate your content above and beyond.
Let me give you an example.
Similarweb gives excellent engagement data at a site level. App-level too. If I was to combine these three metrics (pages per session, session duration and bounce rate) I have a composite engagement score.
Something no one else has.
If I took that engagement score and correlated it with third-party traffic data or something like branded search/backlinks, I could correlate engagement data with traffic from search over time.
This is part of our audience engagement index (coming soon!) Image Credit: Harry Clarkson-Bennett
This is what stands out. This is what audiences will read, share, and crucially, remember. It requires more effort.
And as we know from the Google Leak (this brilliant warehouse from Daniel Foley Carter is superb), effort is quite literally estimated and scored by Google. Things that are difficult to replicate are rewarded.
Unless they’re absolutely insane. Then probably the opposite.
You don’t get good at this overnight. But Google has been prepping us for this for some time. If you look at the declining youth engagement in the above graph, maybe people have, too.
Not everyone is fortunate enough to have access to Similarweb data. But that doesn’t matter. Creativity and quality research is more important (and more readily available) than ever.
There are so many quality free data sources – Google Trends (combined with Glimpse), Keyword Planner, some free plans on tools like Ahrefs or Similarweb etc. You just need to identify metrics and combine them to make something bigger and better.
Documents are identified against a topic, scored, compared and presented based on the user’s likely need (Image Credit: Harry Clarkson-Bennett)
“In some implementations, information gain scores may be determined for one or more documents by applying data indicative of the documents, such as their entire contents, salient extracted information, a semantic representation across a machine learning model to generate an information gain score.”
Patents aren’t absolute. Just because a patent is present, it doesn’t mean it is always in use. If they’re frequently cited, recently updated, and have worldwide applications, that’s usually a very good sign they have a level of importance.
This patent is all of those things (Image Credit: Harry Clarkson-Bennett)
But “ranking factors” aren’t absolute either. SERPs and topics are vastly different. It’s why we have subtopics like local SEO, YMYL, et al.
What matters for one term or topic may not matter as much, if at all, for another. It’s the nuance of the job and why trial and error is so important.
You don’t know until you know.
Consider The Four E’s
Your content needs a purpose.
Yes, it needs to convert. That is a business purpose. But it needs a purpose for people. Is it designed to entertain? Educate? As audiences turn away from news (and probably more widely, commodity content), this matters more than ever.
What we now term as commodity content was never designed to do any of the above. It was just designed to make money. Over the years, anything substandard propped up by Google just to make money has died.
This is the next cab of the rank.
E-E-A-T
E-E-A-T has taken a bit of a kicking recently. Not without reason.
The premise is sound. Not unreasonable for readers to expect the author to be, you know, a real person, who knows something and has some kind of online presence. And Google absolutely does track authorship and entities. Plenty of evidence of that.
Google has built and maintained its Knowledge Graph for decades, and entities have been the bedrock of news SEO for years. But E-E-A-T requires you to join the dots. To remove ambiguity – something we call disambiguation.
The Knowledge Graph and disambiguation in action (Image Credit: Harry Clarkson-Bennett)
Doesn’t mean doing this is incredibly valuable, but it’s foundational. Particularly in this modern-day iteration of the internet.
Remember, E-E-A-T Projects Have To Add Value
The problem with the whole – use experts, showcase expertise, prove you test everything, create video, make an effort in the industry, etc. – is now twofold:
It’s expensive.
And less valuable than ever.
Having that person build some kind of profile in the industry. A platform that their content can be shared from and that reduces reliance on search can only be a good thing.
If they’re a legitimate expert on the topic, know how to structure great content and effectively showcase expertise, then you’re onto a bloody winner.
Which is why commodity content is doomed. Because people don’t care about it, and now it doesn’t drive value.
We need to find ways to make non-commodity content truly valuable to the business. If it isn’t driving some kind of trackable value, ignore it. Move on.
Be ruthless, brave and interesting.
Content just for SEO has diminishing returns. It’s almost certainly a bad idea IF you do it the same way you have been for the last 10 years.
Engagement
I have always felt that links should be a happy byproduct of creating and sharing brilliant stuff.
Make me an offer, link sellers. I’m all ears. (Image Credit: Harry Clarkson-Bennett)
I’ve never made an effort to build links. I have just made an effort to write stuff I think is interesting, made some semi-libelous jokes, and got out there in the industry.
That is, more or less the Google definition of link building. In their world of sunshine, links are just earned by doing beautiful things. I am, in this scenario, the poster boy for white hat SEO.
The problem is, people need to make money, and links still drive rankings. So there’s a market there. And if you’re a student of the scriptures like I am, you’ll know the buying and selling of links is the oldest recorded job.
Either way, my inbox is full.
Anyway, your content has to fulfill a need. We’re moving away from straight-laced content, being able to do that for you as a publisher. Traditional ad revenue and the volume model sucks, and you sure as hell aren’t going to drive any subscriptions with what time is x or how to tie your shoes.
I really hope this is a good thing for SEOs and publishers. I want us to focus on content that really makes a difference to people’s lives. Content that makes them smile or think.
Content that makes people angry has been a big hit when it comes to numbers for a long time. But I don’t think anger is the emotion you should shoot for.
Measurement
You need to measure quality engagement, on and off-site. That means:
On-Site
No need to overcomplicate it for now.
Session duration.
Bounce rate.
Link clicks.
Pages per session.
Comments.
Read time.
Off-Site
Very much depends on the platform and the purpose, but I would focus on:
Links.
Shares.
Comments.
Saves.
Watch time.
You need to track metrics that tell you clearly whether people truly care about what you are creating. Clicks are dying, so I’d rather be measured against something a. more valuable and b. less miserable.
Create a composite metric(s) that gives you and your creators something to clearly focus on. Make their job easy by guiding their content with simple, straightforward metrics. Metrics that don’t just chase page views.
Structure
Structure’s not sexy. Let’s be honest.
But it matters. If, for some reason, you think LLMs are the zenith of society and content consumption, then you should know that models are more likely to cite or reference content from the top or bottom of the page, thanks to their inability to properly follow an argument.
Semantic markup is still the foundation of a well-ordered page (Image Credit: Harry Clarkson-Bennett)
Unless, of course, the entity and topic are repeatedly referenced throughout.
I shouldn’t have to tell you that this is a bad idea and your content will become unreadable to living, breathing people.
But maybe you don’t care about that anymore.
Proper structure really matters. People have expectations (and accessibility needs). In more traditional commodity content, they want their question answered immediately. If you satisfy that – and the intro to your article isn’t abysmal – you might generate a longer session, a click, or hell, maybe even a conversion.
Theoretically, non-commodity content accessed via search should still be intent-driven. Possibly more so if we’re to believe the more qualified users with longer tail queries theory Google espouses.
So you still need to follow a similar, highly coherent page structure:
Answer the question.
Some form of TL;DR article summary.
Argument.
Concluding thoughts.
Coherent FAQs (if applicable).
One that logically answers queries in the appropriate format – text, video, image, list, etc. – and is highly consumable.
The argument section is where LLMs tend to lose their ability to accurately and appropriately cite and reference content. Which is not at all dissimilar to people.
I am not saying you need to continually refresh and restate the entity in question. That may be construed as keyword stuffing. It needs to read well for people. But you need to be clear, concise and accurate to make consuming your content simple.
Don’t People Consume Content In Different Ways?
You’re absolutely right, my pedantic friend, they do. Broadly, I think there are four types of consumption:
Scanners: The vast majority. Too lazy or illiterate to read the whole thing, but will be satisfied from a headline, bold text, bullet points, and headers. They treat a page like a map, not a story.
Answer seekers: They find what they want and leave. But still leave satisfied.
Visual/audio consumers: A cohort that either refuses to or cannot read, but will stare at a pretty picture for 60 seconds.
Deep readers: A small cohort, but a deeply engaged one, desperate for you to get something wrong.
I suspect these groups cover more than 90% of people. There are also fact-checkers – who skip the narrative and head straight for the citations, data points, or the “About Us” section before deciding if the content is worth their time.
And community-readers, who scroll to the bottom of the article to see the community reaction before deciding whether the content is worth their time. This is (obviously) more of a social trait. Particularly from younger audiences.
Your content can and should satisfy all of these people. It must:
Answer the question.
Be highly scannable.
Broken up with clear, distinct headers.
Form a concise, easy-to-follow narrative.
Be highly scannable.
Easy to share.
Visually appealing (audio and video options available).
Cite sources and clearly explain your methodology if appropriate.
You might think it’s beneath you, but if you don’t optimize for scanners and answer-seekers, you risk losing up to c. 80% or more of your potential audience within the first few seconds.
This is why front-loading (putting the most important info at the top) and using clear hierarchies is so vital in modern writing.
Incrementality testing has become the default answer to a problem most direct-to-consumer brands genuinely have. Platform attribution disagrees with itself; Meta and Google routinely both claim credit for the same conversion. Not to mention studies we have done reviewing one transaction at a time to find out organic search or Google Shopping transactions were being attributed to direct.
The standard pitch is that incrementality cuts through it. Run a lift study, find out which channels are creating demand versus harvesting it, and reallocate spend accordingly. Most of the content you’ll find on incrementality over the last couple of years lands somewhere in that neighborhood. That framing is incomplete, and acting on it has probably led some growth-stage brands into bad decisions. The most common one is cutting upper-funnel channels that fail standalone lift tests, only to watch total revenue drop because those channels were doing work no single-channel test could see.
The conversation needs a different anchor.
Why Incrementality Alone Doesn’t Answer The Allocation Question
Incrementality measures the causal impact of a specific channel or campaign. That is genuinely useful information, but it is not the same as understanding how marketing contributes to the business as a whole.
Consider a customer who sees a Meta ad on Monday, doesn’t click, then searches for the brand on Wednesday and converts through a paid brand search ad. Meta records a view-through. Google records a last-click conversion. A lift study on either channel in isolation might show a modest incremental contribution. The honest answer is that both ads did real work, just different work. The Meta impression created the brand consideration, while the branded search closed the demand. Cutting either one breaks the journey.
This is exactly the conclusion most brands reach when they read incrementality results without the right context. They see Meta’s lift study come in low, conclude the channel is taking credit for conversions that would have happened anyway, and reallocate the budget. Six weeks later, brand search volume drops, blended efficiency drops with it, and the team is trying to figure out what happened.
One lift study on one channel cannot tell you whether that channel deserves the budget; it can only tell you what happened inside the test, which is why allocation decisions need a metric that captures the whole business.
Marketing Efficiency Ratio (MER) Is The Metric The Conversation Is Missing
Marketing Efficiency Ratio, total revenue divided by total ad spend, is the only commonly available metric that doesn’t care which channel gets credit. It treats marketing as one investment producing one revenue stream. That is what marketing actually is at the business level, and that is the question chief financial officers and founders are actually asking when they look at performance.
MER on its own is not enough. It can’t tell you how to allocate within a budget, and it can be inflated by seasonality or organic demand growth. But it answers the question that should anchor every other measurement decision: Is the blended marketing investment producing acceptable returns at the business level? Once that anchor exists, the role of every other layer becomes clearer.
The Three-Layer Stack That Actually Works
A solid measurement stack has three layers, each answering a different question.
MER answers: Is total marketing spend producing the returns this business needs? Is the investment working?
Incrementality answers: If I add or cut spend on this channel, what happens to MER?
Attribution answers: What touchpoints did customers actually engage with, and what does that tell me about channel role? How does this affect the customer journey?
The mistake brands make is using any one layer to answer questions that require the others. Cutting Meta because brand search closed the sale reads attribution as causation. Trusting Meta’s reported return on ad spend does the same thing in reverse. Treating an isolated lift study as a verdict on whether a channel deserves spend ignores what that channel might be contributing to MER through its effect on other channels.
How To Actually Run Incrementality Inside This Stack
Incrementality testing was not as simple as it is now, and in some cases, the price tag was much higher than they would want to invest. The good news is that the cost of running incrementality tests has dropped meaningfully in 2025. Four testing methods, ranked by accessibility:
Platform-Native Lift Studies
Meta Conversion Lift and Google Conversion Lift run inside the existing ad platforms at no additional cost. Per Google’s official Conversion Lift documentation, the platform now reports directional lift results for studies with budgets above $5,000 USD and 1,000 conversions, supported by a transition to Bayesian statistical methodology that allows studies to run with lower budgets and fewer conversions than the older frequentist approach required. Google Ads Highlights of 2025 confirms Conversion Lift now works at lower spend levels and conversion volume than in prior years.
Meta’s Brand Lift studies sit at the other end of the spend spectrum. Per Meta’s minimum requirements documentation, Brand Lift in the United States requires a $120,000 minimum budget over the study duration. This is up from $30,000, which is a significant increase and puts Brand Lift out of reach for many brands. That said, Meta’s Conversion Lift studies have lower thresholds and remain a viable starting point. The two products measure different things and carry very different costs, which is worth understanding before designing a testing program.
Platform-native tests have a clear limit. They only measure incrementality inside the platform running the test, so they cannot account for cross-channel effects. Read the results as one input, not the verdict.
Geo Holdout Testing
If your sales are spread across enough markets to run a real holdout, geo testing produces cleaner results than user-level lift studies. Pause spend in matched markets while continuing it in others, then measure the revenue gap. Test and control markets need to be matched on baseline performance, seasonality patterns, and customer demographics, with several weeks of pre-test baseline data to confirm the markets behave similarly under normal conditions.
Spend-Down Testing
This is the most direct way to measure MER sensitivity. Cut a channel’s budget by 50 to 75% for a defined window and measure total business impact, not channel-level metrics. If you cut Meta by half and total revenue drops 40%, that channel is contributing more than its lift study suggested. If you cut it by half and revenue holds, the channel was likely harvesting demand that other channels were creating. Spend-down testing produces less statistically rigorous results than a properly structured geo holdout, but it is the only test that explicitly measures the channel’s contribution to MER rather than to its own attributed revenue.
Full Causal Inference Models
Synthetic controls, difference-in-differences analysis, and test-calibrated media mix modeling sit at the top of the methodology stack. Google’s open-source Meridian MMM, released in 2025, brought Bayesian causal inference modeling to advertisers without requiring proprietary vendor relationships, but the methodology still requires meaningful data science capability to implement well. Most brands do not need to operate at this layer to make defensible allocation decisions. The first three methods will answer the budget questions that matter day-to-day.
A Testing Cadence That Builds Real Signal
A practical cadence for a brand spending $100,000 to $1 million monthly across paid channels:
Quarterly incrementality test on the largest channel by spend, structured as a geo holdout where possible.
Annual full-channel holdout on each major channel to refresh baseline contribution assumptions.
Continuous platform-native lift studies on new campaigns and significant creative refreshes.
Spend-down tests when MER moves materially without an obvious explanation.
Brands that build a quarterly testing rhythm develop a defensible view of channel sensitivity that no platform dashboard can give them, and pairing that with a steady MER read sharpens every allocation conversation.
Reading Incrementality Results Without Overcorrecting
The hardest part of incrementality testing is interpreting results in context. A low lift study on Meta does not mean Meta should be cut. It means the channel is not creating standalone incremental volume during the test window, which is different from whether the channel is moving MER through its effect on brand search, direct traffic, or returning customers.
Read the lift study as one signal alongside MER. If lift comes in low and MER holds steady when you reduce spend, the channel may be replaceable. If lift comes in low but MER drops, the channel is doing work the test could not measure.
The Stack Most Brands Are Almost Building
Most brands at growth stage have all three layers available to them and are not using them as a stack. They are looking at platform ROAS, occasionally checking a lift study, and treating MER as a number that lives in a finance report rather than a measurement decision.
The incrementality conversation has spent two years arguing about whether attribution is broken. It is not the right argument. Attribution describes the journey. Incrementality measures sensitivity. MER is the metric the business runs on. The brands building all three into a single decision-making system will allocate paid media budget more confidently than the ones still arguing about which platform’s number to trust. If you are not anchoring on MER and using incrementality as the diagnostic that explains its movement, that is the gap to close first.
Google’s Universal Commerce Protocol is the first production blueprint for what every website (ecommerce or not) will eventually need to expose to AI agents: discoverable actions, predictable outcomes, persistent sessions, and explicit agent policies.
UCP was released as infrastructure for Google Merchant Center retailers. But the more important story is the architecture underneath it. UCP is the first real implementation of what I’ve been talking about in the Interaction pillar of machine-first architecture. If you want to understand what agent-ready websites look like in practice, you need to look at UCP’s developer documentation. The architecture is the lesson, and it goes far beyond Google Shopping.
What Google Actually Built
UCP is an open standard Google unveiled in January 2026 at the National Retail Federation conference alongside Shopify, Etsy, Wayfair, Target, and Walmart, as a common language between AI surfaces (Gemini, Google AI Mode) and merchant backends. According to Google’s “Under the Hood” post on UCP, the protocol has four moving parts worth paying attention to.
A discovery endpoint at /.well-known/ucp. Agents query the /.well-known/ucp URL to learn what a merchant’s website can do, which products it sells, which actions it exposes, and which transports it supports. This manifest is the handshake between an AI agent and a merchant’s backend. Without that manifest, an agent has no knowledge of what it needs to parse or call. At best, it will try to guess.
Three REST endpoints for checkout. UCP reduces the entire transaction to three calls: Create a session, update a session, complete a session. That is it. No cart page, no address form, no confirmation screen. The checkout state lives in session responses, not in rendered HTML. Human layer of your website gets completely ignored. An interface will exist, but it will not be the one you designed.
Transport flexibility. UCP supports REST, Model Context Protocol (MCP) bindings, and A2A (Agent-to-Agent), so agents built on different stacks can talk to the same merchant backend without custom adapters. An agent running inside Gemini and an agent running on a custom MCP client can both hit the same UCP endpoints. This was a very smart move.
An open specification at ucp.dev. UCP is published as an open spec any website, AI platform, or merchant platform can implement. Google does not own the protocol or its governance. The openness matters because the architecture becomes portable to any website outside Google Merchant Center, even if Google’s onboarding path does not.
Google is building UCP for its own Shopping ecosystem first. UCP’s design is the real lesson for everyone else, and that design is a textbook implementation of the Interaction pillar of machine-first architecture. Shopping carts are abandoned by roughly 70% of humans (per Baymard Institute’s long-running checkout research). You can expect the agent abandonment rate on websites with no Interaction layer to be closer to 100%.
The Interaction pillar of machine-first architecture describes what a website must expose so an AI agent can accomplish a goal on it. Five properties: discoverable actions, predictable outcomes, workflow continuity, error recovery, and agent policies. UCP maps to each one almost perfectly.
Discoverable actions. The Interaction pillar says agents need a machine-readable index of what they are allowed to do on a page before they try to do it. UCP’s /.well-known/ucp capability manifest is exactly the machine-readable action index the Interaction pillar calls for, shipped as a production endpoint. An agent fetches the manifest, reads the list of available operations, and plans its next step. No trial and error, no DOM scraping.
Predictable outcomes. The Interaction pillar says every action should return machine-readable state (computed totals, allocated inventory, success flags), not a 200 OK with an HTML receipt. UCP session responses carry structured data at every step: pricing breakdowns, discount allocations, and explicit session state. An agent reading a UCP response knows exactly what just happened and what it owes next.
Workflow continuity. The Interaction pillar says agents need stable session references that survive across multi-step workflows, so they do not lose context mid-task. UCP sessions have persistent IDs, and PUT updates carry that ID forward. An agent can add a line item, apply a discount, adjust shipping, and complete the order across multiple calls without re-creating state.
Error recovery. The Interaction pillar says failures should return structured alternatives, not dead ends. When a UCP discount code fails, the session response explains why and surfaces alternatives the agent can try. A human might click “try again.” An agent needs a payload that tells it what to do next.
Agent policies. The Interaction pillar says websites should declare what agents are allowed to do, what requires human confirmation, and what is off-limits entirely. UCP’s capability declarations are that policy layer: A merchant signals which actions agents can invoke, under what conditions, and where human approval is required. Request signatures and tokenized payments enforce the policy at the protocol level.
Google’s /.well-known/ucp endpoint is the Interaction pillar’s “discoverability of actions” being shipped as production infrastructure. Agents query it to learn what a website can do before they attempt to do it. UCP requires three REST endpoints for checkout: session creation, updates, and completion. That is the entire Interaction pillar reduced to three API calls.
The Gap UCP Exposes For Everyone Else
UCP is Google’s answer to the agent-traffic gap inside its Shopping ecosystem. Every non-UCP website still has the gap, though not every retailer agrees on where the gap actually lives.
Breanna Fowler, Dell’s Head of Global Consumer Revenue Programs, told Digital Commerce 360 in an April 2026 interview that she has not yet noticed “anything behaviorally consistent” in the agent traffic reaching Dell.com. Her focus is search and discoverability, not agent-specific infrastructure: “If I can’t find your products easily and effortlessly, no amount of content and configurator capabilities, nobody really gives a crap about that stuff.”
Fowler is right that nothing matters if agents cannot find the product. But for an AI agent, “finding” a product does not mean typing into a search box. Finding means querying a capability manifest, reading a structured product catalog, and invoking a discoverable action. In a human-first website, findability is a UI problem. In an agent-ready website, findability is a protocol problem. UCP exists because Google decided that treating findability and checkout as protocol problems, not UI problems, is the only way agent conversions ever scale.
A Gemini agent shopping through a UCP-enabled merchant does not parse a product grid, does not guess at form fields, and does not hope nothing re-renders under it. The agent queries /.well-known/ucp, reads the capability manifest, and advances the session through UCP’s three checkout endpoints. The rest of the web (every SaaS dashboard, every B2B quote flow, every booking system, every subscription portal) has no equivalent protocol coming to rescue it.
Baymard Institute’s aggregated checkout research puts the human cart abandonment rate at 70.22% across 50 studies. The agent abandonment rate on websites without an Interaction layer is closer to 100% because humans hesitate at checkout, while agents cannot even find checkout.
What Every Website Can Learn From UCP’s Architecture
You do not need to implement UCP. You are probably not even a commerce business. UCP’s architecture still generalizes into five principles any agent-ready website should implement: a capability manifest, structured actions, machine-readable state, persistent sessions, and an explicit agent policy.
1. Publish a capability manifest. Agents need to know what your website can do before they start. That manifest might be a /.well-known/ endpoint, an llms.txt file, a WebSite schema node with potentialAction entries, or an MCP server listing available tools. The format matters less than the existence. If there is nothing for an agent to query, the agent has to guess, and guessing is how conversions die.
2. Expose actions as structured data. Schema.org has supported Actions for over a decade, including BuyAction, OrderAction, ReserveAction, SubscribeAction, and SearchAction. Almost no websites use them. UCP’s POST /sessions endpoint is effectively a BuyAction target given a stable API contract, which is what schema.org Actions have needed for a decade to actually work. Any website can do the same on its own actions: declare the action type, name the endpoint, document the payload. The how AI agents see your website post covered the Structure pillar side of this question. Schema.org Actions are the Interaction pillar side.
3. Return machine-readable state at every step. Every response to an agent should carry structured state the agent can parse: what happened, what changed, what is next. HTML confirmation pages are not machine state. A redirect to /thank-you is not machine state. JSON with named fields and explicit flags is machine state. Returning JSON state instead of HTML confirmation pages is the single biggest architectural shift from human-first design to agent-ready design.
4. Design for sessions, not pageviews. Agents do not restart when they get distracted. They come back to a workflow in progress and expect the state to still be there. Sessions with stable IDs, safe-to-retry updates, and graceful resume paths are not optional for agents; they are the base layer. Pageview analytics trained a generation of product teams to think in discrete hits. Agents think in transactions.
5. Declare your agent policy explicitly. An agent policy defines three things: what agents can do without asking a human, what requires human confirmation, and what is off-limits entirely. UCP answers these questions through capability declarations. Your website can answer them through an AGENTS.md file, a /.well-known/ policy endpoint, or structured annotations. Pick one. Publish it. Guessing a policy is how agents end up taking actions their users did not intend.
None of these principles require Google’s participation. None require UCP’s adoption. They require a decision to treat a website as an API surface for agents in addition to a screen for humans.
Citation Gets You Into The Answer. Actions Get You Into The Revenue
Most of the AXO conversation today is still about the Content pillar: how to get cited in ChatGPT answers, how to rank in Google AI Overviews, how to become the source AI surfaces quote. That work matters. Citation drives awareness, and awareness is the top of the funnel. The SEO to AAIO and Answer Engine Optimization articles covered how to win it.
UCP demonstrates the Interaction pillar, which is the other half of the agent-ready website stack that AEO and GEO do not cover. The Interaction pillar is about being transacted through by an AI agent, not quoted in its answer. The difference between a cited website and a transactable website is the Interaction pillar. Citation gets you into the AI’s answer. Discoverable actions get you into the AI’s revenue.
On the Cheeky Pint podcast, Sundar Pichai described a future where an AI user has “many threads running” at the same time, research, comparison, booking, purchase, all executing in parallel on behalf of a single human. In that model, the website that lets the agent resolve its thread fastest wins the thread. Resolution means completing an action, not loading a page. Dell has the traffic and loses the thread. A UCP-enabled merchant resolves the same thread in three API calls.
UCP is the first production artifact that gets the Interaction pillar right. UCP will not be the last. Every website that wants to participate in agent-mediated revenue will eventually need to ship its own version of the same architecture, through an open protocol, a schema.org capability layer, a WebMCP endpoint, or a custom MCP server. The spec can vary even if the principles cannot.
UCP is the working reference implementation of the Interaction pillar, built by Google and running in production inside Google Shopping today. Every other website still owes its own answer. Dell’s Breanna Fowler said discoverability is what matters. For an agent, discoverability is a protocol.
In this SEO webinar, Wayne Cichanski, VP of Search & Site Experience at iQuanti, unpacked how AI systems generate answers and what determines whether your brand’s content earns a place in them.
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You prompt ChatGPT with something, and suddenly your brand name shows up in the response. Sounds like a win, right? But before you share the screenshot with your team, there’s one important question to ask: Is your brand being cited or mentioned?
As AI search and LLM-driven discovery continue to grow, understanding the difference between AI brand mentions and AI citations is becoming increasingly important for SEO and brand visibility. In this article, we’ll break down what AI brand mentions are, how they work, and how they differ from citations.
Since we know you’re excited to celebrate your AI visibility win, let’s get straight into it.
Table of contents
Key takeaways
AI brand mentions occur when an AI tool references your brand in responses, while citations support the information with sources
Understanding the difference between mentions and citations is crucial for SEO and brand visibility
To improve AI mentions, create clear, structured, and extractable content that addresses user queries directly
Brands need to build authority through trusted mentions across various platforms to enhance visibility and acceptance by AI systems
Both mentions and citations are crucial; mentions help AI identify your relevance, while citations reinforce your credibility
What is an AI brand mention?
An AI brand mention happens when an AI tool references your brand name inside a generated response, recommendation, comparison, or summary. The brand mentions can be either linked (also known as explicit mention) or unlinked (also known as implicit mention).
Here’s an example of ChatGPT’s response to, “What are some of the best WordPress SEO plugins?”
ChatGPT mentions Yoast SEO explicitly and implicitly
AI can mention brands in different conversational contexts depending on the user’s query and intent. Here are some of the most common ways AI-generated responses include brand mentions:
Direct recommendations
This happens when AI directly suggests a brand, product, or service as a possible solution to the user’s query. For instance, these mentions typically appear in recommendation-style prompts where users are actively seeking options or tools.
Comparisons
AI may mention brands while comparing products, services, features, pricing, or use cases. In such cases, the brand becomes part of a broader evaluation or decision-making discussion.
Examples within answers
Sometimes, AI uses brands as examples to explain concepts, trends, workflows, or industry practices. These mentions help provide context and make the explanation easier for users to understand.
Contextual references
Brands can also naturally appear in broader discussions about a topic or industry. These mentions are less promotional and more about establishing topical relevance within the conversation.
How do LLMs decide what to mention?
Large language models don’t “choose” brands the way a human would. They generate responses based on patterns, probabilities, and signals they’ve learned over time. When a brand shows up in an AI answer, it’s usually because multiple underlying factors align.
LLMs learn from vast datasets that show how often certain brands appear alongside specific topics.
When people repeatedly discuss a brand in connection with a particular use case, the model develops a strong association. Over time, this increases the likelihood that the brand will appear in responses to similar queries.
But it’s not just frequency. Context matters just as much.
What topics is the brand linked to?
What problems does it appear to solve?
What other terms show up around it?
Brands that appear across multiple contexts build deeper, more flexible associations. Those with limited or inconsistent mentions struggle to surface.
2. Retrieval-Augmented Generation (RAG)
Many modern AI systems extend beyond their training data using Retrieval-Augmented Generation (RAG). This is where things get more dynamic, and where many brands either gain visibility or disappear entirely.
At a basic level, here’s what changes:
Without RAG, the model answers using only what it learned during training
With RAG, the system first retrieves relevant information from external or live sources, then passes both the user query and the retrieved content into the model
The model then combines this new information with its existing knowledge to generate a more accurate, up-to-date response.
Descriptive diagram of RAG and training data by Amazon AWS
When a user submits a query, the retrieval system acts as a gatekeeper. It scans indexed sources, such as web pages, documentation, articles, and forums, to find content that best matches the query.
3. Context and semantic understanding
LLMs don’t rely on exact keyword matches. They interpret intent. When someone asks a question, the model maps it to broader concepts and then surfaces brands that fit those meanings.
For example, a query about “tools for remote teams” might connect to:
Collaboration
Async work
Team communication
Workflow management
LLMs are more likely to surface brands that consistently associate themselves with these ideas, even if users don’t use the exact phrase. This is where entity clarity becomes critical. If your brand is described differently across sources, the model struggles to understand what you actually do.
Overall, it’s not just about what you say, but how your content connects to related topics. Therefore, linking your brand to relevant concepts, use cases, and terminology helps AI systems understand when your brand is relevant. This is where it helps to semantically link entities to your content, so those relationships are clearer and easier for models to pick up.
4. Authority and cross-source validation
LLMs don’t rely on a single source. They validate information by comparing patterns across multiple sources and weighing the trustworthiness of those sources. When a claim appears consistently across many independent platforms, the model is more confident in including it. If it shows up in only a few places, that confidence drops.
AI systems combine semantic understanding with retrieval signals to assess which sources to trust. This typically includes:
Source credibility: Well-known publications, academic content, government sites, and recognized organizations are prioritized
Citation patterns: Sources that are frequently referenced by others are treated as more authoritative
Recency: More recent information is often weighted higher, especially for fast-changing topics
Transparency: Content with clear authorship, dates, and references is considered more reliable
Authority in AI is about being consistently referenced across credible, independent sources. This is why PR, earned media, and third-party mentions play a bigger role in AI visibility than they traditionally did in SEO.
5. Relevance to the query
Before anything else, the model evaluates fit. Even highly authoritative or frequently mentioned brands won’t appear unless they clearly match the user’s intent, such as the use case, audience, or problem being solved.
In simple terms, if your brand isn’t a strong answer to the query, it won’t be included.
When surfacing a brand in answers, AI models may include nuances like:
Beginner vs advanced users
Budget vs premium solutions
Niche vs general use cases
Modern AI systems have shifted from traditional keyword matching to query understanding. They use Natural Language Processing (NLP) to understand the “why” behind the text strings. If explained technically, gen AI converts text queries (prompts) into vectors that allow it to find semantic similarity and return relevant answers.
6. Sentiment and human feedback (RLHF)
LLMs don’t rely solely on training data or web sources. They are continuously improved through human feedback, a process known as Reinforcement Learning from Human Feedback (RLHF).
In this process, human evaluators review model responses and guide them based on whether the answers are:
Helpful
Accurate
Safe
Trustworthy
How does this affect brand mentions? If a brand is consistently associated with negative sentiment, the model may learn to avoid or deprioritize it. On the other hand, brands that appear in neutral or positive contexts across sources are more likely to be included.
In this way, RLHF acts as a layer that refines raw data signals, aligning brand mentions more closely with quality, trust, and user expectations.
Tips to get more mentions
Getting your brand mentioned in AI answers isn’t a completely new discipline. It closely overlaps with what many now call LLM SEO. If you’ve already been working on visibility, authority, and content quality, you’re on the right track.
Here are a few practical ways to improve your chances of being mentioned:
Create content that is easy for AI systems to understand and reuse. This means clear definitions, structured explanations, and direct answers rather than long, vague introductions.
For example, a well-structured guide that clearly defines “what is customer data management” with concise sections is far more likely to be picked up than a generic blog post that buries the answer halfway through.
Address evaluative queries
AI assistants often respond to questions like “best tools for X” or “which platform should I choose?” If your content directly addresses these comparisons, you increase your chances of being included.
Like a comparison page, for example, Yoast vs. Rank Math, that explains when your product is better suited than alternatives, it gives the model a clear context to recommend you.
Strengthen authority signals
Mentions across trusted, independent sources significantly improve your visibility. This includes being featured in industry publications, contributing expert insights, or earning mentions in reviews and comparisons.
For example, a brand cited in multiple reputable blogs and reports is more likely to be surfaced than one that only publishes content on its own website.
Keep cornerstone pages current
Freshness plays a key role, especially for topics that evolve quickly. Regularly updating the content of your key pages signals that your information is reliable and up to date. For example, a “best tools” page updated every few months with current data is more likely to be retrieved than one that hasn’t been touched in years.
Broaden entity clarity
Your brand should be consistently described across your website and external platforms. This helps AI systems clearly understand what you do and when to mention you. For example, if your product is always positioned as “project management software for remote teams,” that repeated clarity strengthens your association with that use case.
AI brand mentions vs AI citations
Before sharing the comparison, let me give you a brief overview of citations. AI citations are references that AI systems and search engines include to support the answers they generate.
Citations usually point to a specific source, such as a webpage, report, or article, and credit the source of the information. In many cases, a response can include both a brand mention and a citation at the same time.
ChatGPT’s response mentions brands and cites resources to back its answer
Next, let’s see how they are different.
Aspect
AI brand mention
AI citation
Definition
Your brand name appears within the AI-generated response
AI attributes information to your content, often with a link or reference
Format
Mentioned naturally in text, no link required
URL, footnote, or inline source reference
What it signals
Brand awareness and category relevance
Authority, credibility, and trustworthiness
Impact
Builds mindshare and keeps you in the consideration set
Acts as proof of expertise and can drive traffic
Traffic potential
Indirect, through increased brand recall
Direct, via clickable or attributed sources
Frequency
More common across most AI responses
Less common and more competitive
Where it appears
Across most LLMs, even without live web access
More common in systems with retrieval or web access
How to optimize
PR, earned media, third-party mentions, community presence
Create citation-worthy content, structured data, original research
Mentions get you in the conversation. Citations make you the source.
Mentions make the AI familiar with your brand. Citations make the AI willing to vouch for it.
In short, the most effective strategy is to optimize for both.
Do citations still matter?
Yes, citations still matter, but they are no longer a standalone strategy.
AI systems still use citations as supporting signals to validate information, confirm credibility, and discover trustworthy sources. When multiple reputable websites reference the same brand or source, it reinforces trust and helps AI systems verify the information’s reliability.
While both mentions and citations matter, mentions currently carry more weight for relevance and AI visibility. Citations still help reinforce authority and trust, but mentions give AI systems richer contextual signals about where a brand fits, how often it appears in conversations, and why it matters within a topic.
How to achieve citations and mentions both?
Brands that consistently appear in relevant conversations while publishing credible content are more likely to earn both mentions and citations. Here are some easy strategies that you can follow:
Create mention-worthy content
The easiest way to earn both mentions and citations is to publish content people naturally want to reference. This includes thought leadership, original research, unique insights, industry commentary, and practical resources that add real value. When your content contributes something new to the conversation, it becomes easier for journalists, creators, communities, and AI systems to pick it up.
Focus on contextual brand mentions
AI systems pay attention to how and where your brand is discussed. Mentions across community discussions, industry blogs, PR coverage, podcasts, forums, and trend-based conversations help reinforce your relevance within a topic. The goal is not just visibility, but also appearing consistently in meaningful, context-rich discussions.
Build credibility for citations
If you want more citations, credibility becomes essential. AI systems are more likely to reference content that demonstrates strong expertise and trustworthiness. This is where principles like E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) become important.
AI brand mentions vs. citations: FAQs
While mentions help AI systems recognize and associate your brand with specific topics, citations strengthen trust and authority by validating your content as a reliable source.
The reality is that both work together. Brands that consistently appear in relevant conversations while publishing credible, high-quality content are far more likely to strengthen their AI visibility over time.
Here are some common questions around AI brand mentions and citations:
Are citations and backlinks the same?
Not exactly. Backlinks are traditional SEO links that point from one website to another, mainly to help search engines understand authority and ranking signals. AI citations, on the other hand, are references AI systems use to support or validate the answers they generate. While citations can include links, their primary role is attribution and trust rather than passing ranking value. For a deeper understanding, read AI citations vs backlinks.
If a brand is mentioned, will it be cited too?
Not always. A brand can be mentioned in an AI response without being directly cited as a source. This usually happens because AI systems often recognize brands through repeated contextual mentions across the web, even when they are not using that brand’s content as the primary supporting source for the answer.
Why should businesses focus on both mentions and citations from AI?
Mentions and citations support different aspects of AI visibility. Mentions help AI systems understand where your brand fits within a topic, while citations reinforce authority and trust.
How to track both mentions and citations for my brand?
Tracking AI visibility manually across platforms can quickly become difficult. Tools like Yoast SEO AI+ help brands monitor how they appear across AI-driven search experiences. With AI Brand Insights, you can track mentions, citations, and overall brand presence across AI platforms to better understand where your visibility is growing and where opportunities exist to improve your AI brand visibility using Yoast AI Brand Insights.
I’m a Computer Science grad who accidentally stumbled into writing—and stayed because I fell in love with it. Over the past six years, I’ve been deep in the world of SEO and tech content, turning jargon into stories that actually make sense. When I’m not writing, you’ll probably find me lifting weights to balance my love for food (because yes, gym and biryani can coexist) or catching up with friends over a good cup of chai.