When someone asks ChatGPT for a product in your category, it doesn’t always crawl websites in real-time.
Its first move is to pull from what it already knows about you and your competitors from its existing knowledge.
Clear and recognizable entities in AI training data are just as important as having the most authoritative and optimized website.
This shift means your webpage might rank #1 in classic search, but if your brand isn’t well-structured for entities, AI might overlook you entirely in the answer.
The rules we’ve relied on for decades don’t fully apply when machines create answers. They draw on their own knowledge and real-time data from sites, including yours.
You’re about to learn what this means, why it matters, and what you can do about it.
What Are Entities in AI Search?
An entity is a “thing” that search engines and AI models can recognize, understand, and connect to other things.
Think of entities as the building blocks that AI uses to construct answers. In other words, gigantic relational databases.
Let’s use email marketing company Omnisend as an example.
Through the lens of a database, Omnisend isn’t just a website with pages about email marketing. It’s a network of connected entities:
Use cases: “welcome series,” “abandoned cart recovery”
Here’s what the entities look (hypothetically ) like to a large language model (LLM):
These records become the foundation for AI answers.
LLMs do more than just find keywords on your page. They also retrieve entities, place them in vector space, and choose the ones that best answer your question.
Vector space explained: It’s a mathematical method that AI models use to understand relationships between concepts. Imagine a 3D map where similar items group together. For example, “Apple,” the company, is close to “iPhone” and “Tim Cook.” Meanwhile, “apple,” the fruit, is near “banana” and “orchard.”
For example, ask Google: “What’s the best email marketing tool for my Shopify store?”
You’ll see brand entities like Klaviyo, Omnisend, Brevo, Mailchimp, Privy, and MailerLite mentioned. This makes sense because the entities are closely related in the AI’s understanding.
Notice: the brand mentions aren’t linked to the websites. It’s just building the answer and then linking to the brand SERP on Google.
Why Entities Matter More Than Websites
AI models are constantly mapping relationships between entities when serving up answers.
When someone types “best email marketing tool for Shopify,” LLMs spread out the query. They turn that one question into multiple related searches.
Think of AI doing lots of Google searches at the same time.
The system simultaneously explores “What integrates with Shopify?”, “Which tools handle abandoned carts?” and “What do ecommerce stores actually use?”
Your brand can appear through any of these paths, even if you didn’t optimize for the original query.
Classic SEO relied a lot on keyword density and page authority.
But AI uses dense retrieval, where it’s looking for semantic meaning across the web, not just word matches on your page.
Dense retrieval explained: AI systems focus on meaning, not just exact keywords. They find related content, even if different words are used.
A Reddit comment that clearly explains “We switched from Klaviyo to Omnisend because the Shopify integration actually works” carries more signal (assuming the model prioritizes authentic discussions) than a page stuffed with “best email marketing Shopify” keywords.
The AI understands the relationship between the entities (Klaviyo, Omnisend, Shopify) and the context (switching, integration quality).
PR folks have been fighting for this moment: mentions without links still count.
For the longest time, we’ve obsessed over backlinks as the currency of SEO.
But AI systems recognize when brands get mentioned alongside relevant topics, using these as relationship signals.
So when Patagonia appears in climate articles without a hyperlink, when Notion shows up in productivity discussions on Reddit, when your brand gets name-dropped in a podcast transcript — these all strengthen your entity in AI’s understanding.
Here’s a real example that clarified this for me:
Microsoft OneNote often shows up high in AI recommendations for “note-taking tools.”
In ChatGPT:
In Perplexity:
And in Google AI Overviews:
But EverNote dominates Google’s number one ranking spot for “note taking tools”.
Why?
OneNote’s integration with the Microsoft ecosystem means it gets mentioned constantly in productivity discussions, enterprise software comparisons, and Office tutorials. This creates dense entity relationships in AI training data.
Evernote, by contrast, has focused on SEO and earned strong backlinks that dominate traditional search rankings.
How Entities Get Recognized
So how does Google (and other AI systems) actually know that Omnisend is an email marketing platform and not, say, a meditation app?
The answer sits at the intersection of structured data, human conversation, and pattern recognition…at massive scale.
Entity Databases and Product Catalogs
Google maintains what they call Knowledge Graphs and Shopping Graphs.
Other AI systems have similar entity databases, just with different names.
The idea is the same: huge databases that map every product, company, and person along with their attributes and relationships.
When Nike releases the Pegasus 41, it doesn’t just become a new product page on Nike.com. It becomes an entity in Google’s Shopping Graph, connected to “running shoes,” “Nike,” “marathon training,” and hundreds of other nodes.
The system knows it’s a shoe before anyone optimizes a single keyword.
Human Conversation as Training Data
AI systems learn just as much from informal mentions as they do from structured markup.
When an Outdoor Gear Lab review casually mentions testing Patagonia’s Torrentshell 3L against the expensive Arc’teryx Beta SL, that relationship gets encoded.
When a podcast guest says, “I moved from Asana to Notion for task and project management,” this competitive link adds to the training data.
Reddit and Quora have become unexpectedly powerful for entity recognition. (Google explicitly stated they’re prioritizing “authentic discussion forums” in their ranking systems.)
A single comment on why someone picked Obsidian over Notion for knowledge management matters more than you might realize.
These platforms capture what websites struggle to do: real people sharing real decisions with real context.
Multimodal Recognition
AI systems extract entities from audio and video. They do this by turning speech into text through transcription.
Every mention in a transcript, every product on screen, and every comparison in a talking-head segment is processed.
A 10-minute YouTube review of project management tools turns into structured data that compares ClickUp, Notion, and Asana. It includes feature comparisons and maps out use cases.
The New SEO Power Dynamic
You can’t game entity recognition the way you could game PageRank.
You can’t manufacture authentic Reddit discussions. You can’t fake your way into natural podcast mentions. The system rewards genuine presence in genuine conversations, not optimized anchor text.
Think about what this means:
Your engineering team’s conference talk that mentions your product’s architecture? That’s entity building.
Your customer’s YouTube walkthrough of their workflow? Entity building.
That heated Hacker News thread where someone defends your approach to data privacy? Entity building.
We’ve spent the longest time optimizing for robots. Now the robots are optimized to recognize authentic human discussion. (Ironic.)
5 Ways to Optimize Your Brand for Entities (Not Just a Website)
Using Omnisend as an example, here are five approaches for evaluating and optimizing entity presence in AI-powered search results.
1. Assess Your Entity Foundation
To start, you need a baseline understanding of your current entity relationships.
For Omnisend, this means mapping how AI systems currently categorize them relative to competitors.
Begin by verifying schema markup across key pages.
Testing Omnisend’s homepage with the Schema Markup Validator shows they use Organization and VideoObject schema.
And the Organization schema is relatively basic.
Omnisends competitor, Klaviyo, uses Organization schema as a container for multiple software offerings.
Klaviyo’s approach maintains brand-level authority while declaring specific software categories and capabilities. This potentially gives them stronger entity associations for queries about email marketing, SMS marketing, and marketing automation.
Next, check your entity presence in major knowledge sources like Wikidata and Crunchbase.
On Wikidata, Omnisend’s records are OKAY.
There’s basic info, like what Omnisend does, the industry, inception date, URL, and social media profiles.
But Klaviyo, again, is all over it. They have multiple properties for industry, entity type, URLs, offerings, and even partnerships.
There’s a clear opportunity for Omnisend to update its Wikidata with more details.
2. Test Query Decomposition
AI systems break down queries into entities and relationships. Then, they may try multiple retrievals.
For example, in Google Chrome, I prompted ChatGPT:
“What’s the best email marketing tool for ecommerce in 2025? My priority is deliverability.”
In the chat URL, copy the alphanumeric sequence after the /c/ directory. For me, it was 68d4e99e-4818-8332-adbd-efab286f4007.
Note: You need to be logged into ChatGPT to get this sequence
Right-click on the page and click “Inspect”.
Choose the “Network” tab, paste the alphanumeric sequence in the filter field, and reload the page.
In the “Find” section, search for “search_model_queries“. Then, click on the search results.
Each decomposed query represents a different competitive pathway.
Omnisend might surface through deliverability discussions, but miss general tool comparisons.
Mailchimp could dominate broad searches while competitors own specialized angles.
This explains why you appear in AI answers for searches you never optimized for. The semantic understanding creates visibility through unexpected entity relationships rather than keyword matching.
You can check this yourself. Run the extracted queries in separate chats and note which brands appear where.
But maybe don’t build a strategy around exploiting this technique.
The methodology depends on undocumented functionality that OpenAI could change without notice.
Important finding: Simple queries produce simple results. When I prompted “Best email marketing tool for ecommerce,” it triggered exactly one internal search with basically the same language. No decomposition.
3. Map Competitive Entity Relationships
Traditional SEO competitive analysis asks “Who ranks for our keywords?”
Entity analysis asks “When do AI systems group us together?”
I tested this with Omnisend to understand when they appear alongside different competitors.
I ran 15 variations of email marketing queries through Google AI Mode to see which brands consistently appear together.
Note: I tested logged out, using a VPN set to San Francisco, in private browsing mode to minimize personalization bias.
I began with simple terms like “best email marketing for ecommerce” and “abandoned cart recovery tools.” Then, I tried different angles like “email automation for Shopify stores.”
Here’s what I found:
Query Context
Omnisend Present
Most Co-Mentioned
Klaviyo Present
Ecommerce email
5/5 queries
Klaviyo, Mailchimp
4/5 queries
General email
5/5 queries
Mailchimp, Brevo
2/5 queries
Deliverability focus
2/5 queries
Brevo, Mailchimp
0/5 queries
Omnisend appeared in 12 of 15 total queries — stronger entity presence than I expected.
But mentions shifted dramatically by context.
In ecommerce discussions, Klaviyo dominated as the top tool.
In general email marketing, Mailchimp took over as the main reference point.
The mention order revealed something important. Klaviyo appeared first in 5 of 5 ecommerce queries, with more positive language around their positioning.
Omnisend routinely ranked second or third. This suggests they’re part of the discussion but not at the forefront.
Here’s what’s interesting:
Klaviyo completely disappeared from deliverability-focused queries while Omnisend maintained some presence.
This shows entity relationships are radically contextual.
Being the leader in ecommerce email doesn’t mean presence in deliverability conversations.
4. Optimize For Entities in Your Content
Entity recognition works best when it has context-rich passages. This helps AI systems extract and understand information more easily.
Take generic descriptions like “Our automation features help ecommerce businesses increase revenue through targeted campaigns.”
An AI system may struggle to identify which product you mean, its automation features, or how it compares to others.
Compare that to: “Omnisend’s SMS automation integrates with Shopify’s abandoned cart data to trigger personalized recovery messages within 2 hours of cart abandonment, without requiring manual workflow setup.”
This version establishes multiple entity relationships (Omnisend → SMS automation → Shopify integration → abandoned cart recovery) within a single extractable passage.
LLMs prefer to use their training data for answers. But when they pull info from the web, strong entity connections help a lot.
You’re reducing friction for both bots and human readers.
As a test, run key passages from your most important pages through Google’s Natural Language API to see what entities get recognized. This can also be video scripts.
Content with strong entity density tends to get cited more often than content requiring additional context.
5. Build Strategic Co-Citations
Entity authority builds through consistent mention alongside relevant entities in trusted sources. This moves the focus from link building to building relationships where natural comparisons happen.
For Omnisend, this means being present in authentic discussions. It’s about genuine comparisons, not forced mentions, that strengthen specific relationships.
A Reddit thread comparing “Klaviyo vs Omnisend for Shopify stores” carries a different entity weight than appearing in generic “email marketing tools” content.
The specific context (Shopify integration) strengthens both brands’ association with ecommerce email marketing.
The most valuable co-citations happen in:
Reddit discussions comparing tools for specific use cases
YouTube reviews demonstrating multiple platforms
Industry roundups grouping tools by specialization
Podcast discussions of marketing technology stacks
This Reddit thread shows strategic co-citation in action. The original post creates dense entity relationships (Klaviyo → Omnisend → pricing → Shopify store). While the comment adds even more context (pricing concerns → business scaling → “pretty good” user experience).
The discussion goes way beyond optimized content. It’s genuine decision-making that strengthens both brands’ entity associations with ecommerce email marketing.
This approach emphasizes genuine participation. Your category is discussed and evaluated by actual users who make real decisions. This is better than having artificial mentions in content made mainly for search engines.
Moving Forward with Entity SEO
If you’ve built a strong brand across various channels, you’ve laid the foundation.
Quality SEO is still crucial.
Genuine mentions in industry talks, real customer chats, and multi-channel distribution matter too.
Begin with your key product line. Organize it well, track its appearances in AI responses, and then expand to other entities.
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AI answers are taking over search. More people are turning to Google AI Overviews, ChatGPT, and Perplexity for recommendations.
And if your brand isn’t showing up in those AI answers? You’re missing out on a huge (and growing) slice of your market.
That’s why Semrush built the AI SEO Toolkit. It’s a major unlock for marketers trying to understand how AI is impacting their
business.
Today, I’m going to show you how to use it — step by step — with a real example.
TL;DR: Measure Your AI Search Visibility
Here’s what you need to know about Semrush’s AI SEO Toolkit:
What it does:
Tracks how your brand appears across ChatGPT, Google AI Overviews, Google AI Mode, and Perplexity — showing which prompts include you and where you’re missing
Provides prompt tracking, content audits, and competitor comparisons
What it costs:
$99/month per domain (no trial)
Step 0: Start With a Brand
Before we analyze anything, let’s pick a brand to make this walkthrough concrete.
I went to Exploding Topics, browsed the ecommerce category, and picked Petlibro — a trending startup that sells smart pet feeders and water fountains.
I have zero affiliation with Petlibro. This isn’t sponsored. I just wanted a brand that’s growing fast and has enough search demand to make this example interesting.
Step 1: Get Your Search Baseline
Before we look at AI, we want to know how Petlibro is doing in traditional search. It’s super valuable context that will help us understand how they’re performing in LLMs.
Enter the brand’s domain name and look at the last 18 months. Looking at petlibro.com, they’ve been growing a TON.
They get most of their traffic from the U.S., rank for more than 25,000 keywords, and have a domain Authority Score of 43 with backlinks from 2.8K referring domains.
And they rank well in traditional SERPs for a bunch of highly relevant category and product keywords.
So they’re a real brand that’s already doing a good job with SEO. And good search engine optimization often correlates with good AI optimization.
If your brand has so far neglected SEO, now is the ideal time to tackle that with a solid AI SEO strategy (which this audit will help you form).
Step 2: Check Your AI Visibility
Now for the fun part.
Back in the Semrush dashboard, look for AI SEO in the sidebar.
Enter petlibro.com, and a few minutes later, your Brand Performance dashboard will be ready for review.
On the right side, you can see the Share of Voice versus Sentiment Score.
The most interesting thing I noticed right away is that Petlibro has relatively low Share of Voice (6%) in regular ChatGPT, without Search.
That’s because ChatGPT 5 without search enabled has a training data cutoff of September 30, 2024.
And as we saw in traditional search, Petlibro has been growing a LOT in the last year.
Fortunately, they’re performing much better in SearchGPT, Google AI Mode, and Perplexity. All three of which use live search to generate their answers. For example, Petlibro’s Share of Voice in Google AI Mode is 27.8%:
Pro tip: Keep this in mind when analyzing your own brand too. These tools might not have your newest content in their training data. This can affect your apparent visibility, so be sure to check your visibility when search is enabled (as search-powered experiences are becoming more common).
This tab gives you a broad overview of your brand’s visibility. The next step will help you get more granular.
Step 3. Gauge Visibility at the Prompt Level
You can get prompt-level details by heading to the Visibility Overview tab.
Note: Things are evolving fast in the AI SEO space. This tool is brand new at the time of writing, so there isn’t much in the way of historical data right now. But tracking your visibility here over time will help you understand how well optimized your site is for an increasingly AI-based search landscape.
Scroll down and you’ll be able to quickly understand:
Your top-performing topics
Opportunities to improve your brand’s visibility
Popular sources for prompts relevant to your industry
Where your competitors are being cited that you’re not
Where you are being cited as a source
Click on any of the topics (or select Prompts) to see exact prompts and the AI response that you appear as part of.
To get more data on the prompts your rivals are appearing for that you’re not, head to the Narrative Drivers tab. First, you’ll see your brand’s Share of Voice by platform.
This gives you an overview of where your rivals are winning on each AI platform. But we want to scroll down to Share of Voice and switch to the Average Position view.
You can then toggle each competitor individually to get a better idea of how you perform against key rivals over time.
This view essentially gives you a snapshot of your brand’s visibility for key prompts.
To understand which prompts you are and are not appearing for compared to your rivals, you want to scroll down to the Breakdown by Question section.
You’ll see your position, which is where you show up in the answer snippet compared to your competitors.
You can see which ones your rivals appear for that you don’t by using the filters:
For example, Petlibro isn’t appearing for a few prompts that multiple competitors are mentioned in:
Identify the most relevant queries you want to start appearing for, and do this for each AI tool (using the toggle at the top left).
Note these down somewhere, as these will help frame your AI optimization strategy. Think of this part like the keyword research stage in a traditional SEO campaign.
Step 4. Review Your Brand’s Trust Factors
Next, you want to understand where your brand is doing a good job of appearing trustworthy to both your users and the LLMs themselves.
To do this, head back to the Brand Performance tab and scroll down to Key Business Drivers.
This essentially shows where your brand is strong compared to your competitors in various areas that help convey trust to users.
It might look overwhelming at first.
But basically:
The numbers illustrate how often key business drivers (i.e., trust factors) appear in answers where your brand is also mentioned. The bigger the number, the better.
(Look for the trophy icon to see where you’re currently ahead of your competitors.)
For example:
Searchers may value smart home integration when selecting a smart pet feeder.
When AI tools mention PetSafe, they also sometimes mention the fact it has these features.
This makes the brand more likely to appear in AI search responses when a user is looking for smart pet feeders with features like smart home integrations.
If Petlibro offers this, the brand needs to do a better job of conveying that in their content, or they’re going to struggle to appear in AI responses for relevant prompts.
Meanwhile, PetSafe is being mentioned for this kind of user prompt:
Go through this tab and identify trust factors you want to appear for.
If you spot areas competitors are strong but you’re not being picked up, make sure you:
Include trust factors and unique selling points on your website homepage
Add mentions of relevant features to product pages
Write helpful FAQ questions on product pages and blog posts that cater to these trust factors
Step 5. Audit Brand Sentiment in AI Tools
The next step involves diving deeper into how AI tools (and by proxy your users) perceive your brand.
To do this, we’ll head to the Perception report and scroll to the Key Sentiment Drivers section.
This will show you Brand Strength Factors and Areas for Improvement.
This is a great snapshot to see where you’re already doing well. And where you might need to focus new efforts on improving your brand’s perception in AI responses.
Brand strength factors are essentially areas where the AI tools talk positively about your brand.
In Petlibro’s case, these are factors like app connectivity, mechanical jams, and customer support.
Pro tip: Look for anything that’s not accurate here. You don’t want AI tools to be recommending your brand for things you don’t offer — this will just lead to disappointed customers.
The areas for improvement are areas where you might want to:
Create optimized content to make it clear to customers what you offer
Optimize your existing product pages to better reflect their strengths
Improve your products or services to better meet your customers’ needs
That final point is worth emphasizing. Semrush’s AI SEO tools don’t just give you content ideas.
You can use the insights you gain here and the prompts real users are inputting into AI tools to understand where you can improve and expand your products/services.
The future of marketing is truly collaborative across departments. And these kinds of insights can help align both your SEO/content teams and your product and marketing divisions.
This can lead to a better user experience on your site, a better product for your customers, and increased business growth.
Pro tip: At the bottom of most of these tabs, you’ll also find “AI Strategic Insights.” These are AI-powered suggestions you can use immediately to boost your AI visibility.
Step 6. Identify More Content Ideas
Step 6 is to find more ideas for creating new content and optimizing your existing pages.
First, head to the Questions tab and scroll down to the Query Topics section.
Answer these questions with new content or in your existing content.
For example, Petlibro could create a blog post titled “How to Stop Your Cat Shaking Food Out of Its Feeder.”
They could also update their product pages to highlight that their feeders support different portion sizes for morning and evening meals, and add an FAQ section answering common branded questions.
To understand what content you might want to create (and which prompts are actually worth optimizing for), enter the relevant ones into tools like ChatGPT. (Make sure you enable web search.)
The example below returns a lot of scientific papers, so it would likely be a tough one for Petlibro to appear for.
But there is a Reddit thread in there too. Which means a Reddit marketing strategy could be worth exploring to boost visibility for these kinds of prompts.
This next one is a more likely candidate, and we can see PetSafe (a competitor) gets cited as a source. (And Reddit appears again too.)
There is also a product carousel with links further down — none of which are from Petlibro.
So this would definitely be worth digging into to see why PetSafe (and the other products) are being recommended:
Do the product pages do a better job of conveying trust signals?
Are they more descriptive?
Do they have FAQ sections that answer the prompt’s question?
Bottom line:
You need to look closer than simply the prompts themselves to understand why other brands are being recommended ahead of yours.
But once again, if you scroll to the bottom, you’ll find AI-powered insights that can give you a head start.
Turn Your AI SEO Audit Insights Into Action
An AI SEO audit is a vital first step to make your brand AI ready. And Semrush’s AI SEO Toolkit gives you everything you need to get started.
But the audit is just the first step. Use these resources to turn what you learn from the tool into action for your brand:
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Google’s Performance Max (PMax) campaigns now support vertical 9:16 image ads, bringing the popular mobile-friendly format to the platform’s most automated campaign type.
What’s new. Google Ads specialist Thomas Eccel spotted the update, noting that vertical “Story Image Ads” – first seen in Demand Gen campaigns earlier this year – are now available in PMax.
Specs at a glance:
Minimum size: 600×1067 (recommended: 1080×1920)
Maximum file size: 5MB
Google hasn’t officially confirmed where these will serve, though in Demand Gen, they appear in YouTube Shorts Image placements.
Why we care. Vertical 9:16 images let PMax campaigns fit naturally into mobile-first environments like YouTube Shorts, where user attention is highest. Experts say this update goes beyond creative specs. As Phil Byrne, founder of Positive Sparks Marketing LTD, noted, it’s about “meeting users where they naturally consume content.”
With Shorts, Reels, and TikTok dominating mobile engagement, vertical formats are key to maintaining attention and relevance.
The bigger picture. Mike Ryan, head of ecommerce insights at Smarter Ecommerce, added that PMax is already monetizing YouTube Shorts through “GMC Image Shorts,” which display multiple product images for remarketing and personalization – a sign that Google is leaning deeper into short-form, shoppable media.
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Google introduced two AI-powered features: AI summaries in Discover and a Sports feed in Search.
Google Discover. Users will now see AI-generated previews of trending topics they follow. The summaries cite multiple publishers and can be expanded to view more details and linked articles.
The feature is available in the U.S., South Korea, and India, after earlier testing in the U.S. this summer.
A Google spokesperson seemed to confirm the Discover AI summaries “officially” launched in the U.S. in July. At that time, the Discover AI summaries appeared on iOS and Android for trending lifestyle topics (e.g., sports, entertainment). TechCrunch reported this, but there was no official announcement from Google.
What’s new. In Search, a new What’s new button will soon appear when users look up teams or players on mobile.
This feature opens a feed of trending updates and articles about the topic.
This is rolling out in the U.S. over the coming weeks.
Why we care. Discover has been a reliable traffic source for many publishers. Google says the new tools help people explore more of the web, not less, but publishers should watch whether this shift to AI-generated summaries reduces the need for users to click through to read stories. This could result in a similar negative impact on traffic as AI Overviews have had for many websites in Google Search.
Google Ads is testing a new “View-Through Conversion Optimization” feature in its Demand Gen campaigns.
What’s new. This test was spotted last week. It adds a setting allowing advertisers to include view-through conversions (VTCs) in their bidding models.
How it works. This applies to YouTube (Image + Video) traffic.
More channels are “coming soon,” per the early beta.
The feature could improve early-stage efficiency where clicks are scarce but influence is high.
Why we care. View-through conversions reveal what happens when people see your ad, skip the click, but come back to buy. You can turn it on early to train algorithms faster, boost brand lift, and stretch your creative dollars. This is especially important on YouTube because conversions often trail views by days or weeks.
Zoom out. The move underscores Google’s push to make Demand Gen more competitive with Meta’s Advantage+ and TikTok’s Smart Performance offerings, which both leverage impression-driven optimization signals.
What’s next. Expect broader rollout and performance data as Google fine-tunes how view-through data interacts with its automated bidding systems.
First seen. This update was first spotted by Thomas Eccel, Google Ads specialist at JvM IMPACT.
Citations in AI search assistants reveal how authority is evolving online.
Analyzing results across 11 major sectors shows which domains are most often referenced and what that says about credibility in an AI-driven landscape.
As assistants condense answers and surface fewer links, being cited has become a powerful signal of trust and influence.
Based on Semrush data from more than 800 websites, the findings highlight how AI reshapes visibility across industries.
AI citation trends across industries
The analysis surfaced several clear patterns in how authority is distributed across industries.
Universal authorities
Some domains appeared in the top 50 cited URLs across nearly all 11 sectors, with four domains appearing in every one:
reddit.com (~66,000 AI mentions across 11 sectors)
en.wikipedia.org (~25,000, 11 sectors)
youtube.com (~19,000, 11 sectors)
forbes.com (~10,000, 11 sectors)
linkedin.com (~9,000, 10 sectors)
quora.com (~8,000, 10 sectors)
Other domains are sector-strong but globally influential:
amazon.com (ecommerce and five other sectors).
nerdwallet.com (finance-focused).
pmc.ncbi.nlm.nih.gov (health and academic citations).
Concentration and diversity by sector
Citation concentration varies by sector.
Most concentrated: Computers and electronics, entertainment, education.
Most diverse: Telecom, food and beverage, healthcare, finance, travel and tourism.
This means some sectors rely on a handful of go-to sources, while others distribute authority across a broader field.
Relationships between visibility and SEO metrics
AI visibility and AI mentions are strongly correlated (0.87).
Organic keywords correlate more strongly with AI visibility (0.41) than backlinks (0.37).
Keywords and backlinks themselves correlate at 0.79.
By sector, the coupling between AI visibility and backlinks is strongest in computers and electronics, automotive, entertainment, finance, and education.
In these sectors, the scale of authority clearly helps drive AI references.
Sector breakdowns
Finance
Media brands such as Forbes and Business Insider dominate citations, reflecting the importance of timely commentary and market analysis.
However, NerdWallet shows that specialized finance experts can achieve high AI visibility by building deep evergreen guides and comparison content.
This sector also shows one of the strongest correlations between AI visibility and backlink scale, suggesting that authority signals remain highly influential.
Healthcare
Academic and government domains are heavily cited.
The dominance of PubMed Central (PMC), CDC, and national health portals underlines the central role of trusted peer-reviewed or official information.
Wikipedia also appears consistently, often serving as a layperson-friendly entry point.
Diversity is lower here compared with consumer-facing sectors, reflecting the need for evidence-based references.
Travel and tourism
Citations are spread across government advisories (for example, gov.uk travel advice), booking platforms, forums, and user-generated communities.
This diversity reflects the mix of practical (visa, safety), inspirational (guides, blogs), and transactional (booking) content users need.
The sector’s Herfindahl-Hirschman Index (HHI) score is low, suggesting no single authority dominates, and visibility is earned by serving very specific user needs.
Entertainment
User-generated platforms dominate.
Reddit, YouTube, and Quora all appear near the top of cited domains, alongside reference sources such as Wikipedia and IMDb.
This highlights how conversational, community-driven content is central to how AI assistants explain and contextualize entertainment.
In this space, backlink counts are less predictive than breadth of coverage.
Education
Citations concentrate around reference authorities including Wikipedia, university portals, and open-courseware providers.
Specialist learning platforms and forums also feature, but the dominance of well-known academic sources creates a more concentrated citation environment.
Here, AI assistants lean heavily on authoritative, structured content.
Computers and electronics
Technology news and review sites dominate, with CNET, The Verge, and Tom’s Guide appearing prominently.
Wikipedia is again present, but the sector is notable for its concentration, with citations clustering around a few highly recognizable review hubs.
This sector also shows one of the highest correlations between AI visibility and backlink scale, underlining the competitive role of authority signals.
Automotive
A mix of consumer guides (for example, Autotrader, AutoZone) and publisher content.
Insurance and financing providers also receive citations, reflecting user queries that span from buying cars to managing ownership.
Citations are somewhat more evenly distributed, but AI assistants lean on a balance of transactional and informational sources.
Beauty and cosmetics
Influencer-led platforms and community discussion spaces are frequently cited alongside brand websites and review hubs.
The combination of user-generated content and brand authority makes this sector more diverse than average.
Here, social-driven citations compete with established publishing brands.
Food and beverage
Recipe hubs, nutrition authorities, and community cooking sites dominate.
Wikipedia also features, especially for ingredient-level explanations.
The sector has one of the lowest HHI values, meaning a wide diversity of domains are being cited.
Backlink totals are less correlated with visibility here. Instead, topical coverage breadth seems to matter more.
Telecoms
Citations are relatively diverse, ranging from provider help portals to tech media and consumer advocacy sites.
Forums like Reddit often feature in troubleshooting contexts.
The sector’s low HHI suggests no single authority dominates, but users’ practical questions drive AI systems to reference customer-support-style material.
Real estate
Cited domains include large listing platforms (for example, Zillow-type sites), financial services tied to mortgages, and government portals for regulation and housing data.
While concentrated, the sector also pulls from news sources when market conditions are being explained.
The patterns in AI citations carry direct lessons for brands and SEOs, highlighting:
How authority is built.
What types of assets AI prefers to reference.
Why traditional SEO levers now interact differently with visibility.
Reference assets matter
Evergreen guides, standards, and explainers attract citations from both search engines and AI models.
To compete with Wikipedia or government sites, brands need to publish authoritative, fact-checked material that others can comfortably reference.
Breadth of coverage drives visibility
Domains with a wide organic keyword footprint consistently show stronger AI visibility.
This means that covering an entire topic area comprehensively – not just optimizing for a handful of high-volume keywords – positions a brand as a reliable reference source.
Sector rules differ
Each sector rewards different authority signals. In healthcare, peer-reviewed or government-backed resources dominate.
In entertainment, community-driven and UGC platforms rise to the top. In finance, explainers and calculators from expert brands are frequently cited.
Brands need to adapt their content strategy to the trust model of their sector.
Fewer links, higher stakes
AI assistants often cite only a handful of sources per response.
Being included delivers disproportionate visibility.
Conversely, being absent means competitors capture nearly all of the exposure.
This concentration raises the bar for what counts as a reference-worthy asset.
Backlinks still matter, but less directly
While backlink scale correlates with AI visibility, the correlation is weaker than for organic keyword breadth.
This suggests backlinks remain an authority signal, but the breadth and relevance of content may be more critical in an AI-driven environment.
User intent alignment
AI assistants pull from sources that best align with the specific intent behind a query.
Brands that anticipate user needs – whether transactional, informational, or troubleshooting – stand a better chance of being cited.
Creating layered content (guides, FAQs, tools) that matches different intents strengthens visibility.
Becoming a referenced brand
Citations in AI search results reveal the trust networks that underpin the next wave of search.
Wikipedia, Reddit, and YouTube are universal reference points, but sector-specific authorities also matter.
For brands, the lesson is clear: to win visibility in AI-driven search, you need to be the page that others cite.
That means authoritative content, breadth of coverage, and assets designed to be referenced.
Analysis methodology
The analysis drew from AI citation data spanning 11 sectors and more than 800 domains, using responses from Google AI Mode, Perplexity, and ChatGPT search.
Two primary metrics were calculated:
AI visibility score: The average share of responses in which a domain was cited across Google AI Mode, Perplexity, and ChatGPT search.
AI mentions: The total number of times a domain was cited across those engines in a given sector.
These metrics were then enriched with:
Organic keywords (Semrush): The number of keywords for which a domain ranks in organic search.
Backlinks (Semrush): The total backlinks pointing to a domain.
Spearman correlation
To measure the degree of correlation between metrics, I used the Spearman correlation coefficient.
Unlike Pearson correlation, which assumes linear relationships, Spearman looks at whether the ranking of one metric moves in step with another.
In simple terms, if domains with higher keyword counts also tend to rank higher for AI visibility, the Spearman value will be high even if the relationship is not a perfectly straight line.
A value near +1 means the two rise together consistently, near -1 means one rises as the other falls, and near 0 means no clear pattern.
Concentration of the HHI
I then measured citation concentration using the Herfindahl-Hirschman Index, a metric borrowed from economics.
It is calculated by summing the squares of market shares, in this case, each domain’s share of AI mentions in a sector.
An HHI closer to 1 means a sector is dominated by just a few domains, while values closer to 0 indicate citations are spread more evenly.
For example, an HHI of 0.05 suggests a concentrated landscape, whereas 0.02 points to greater diversity.
By combining AI visibility, citation counts, SEO scale (keywords and backlinks from Semrush), Spearman correlations, and HHI concentration, I built a cross-sector picture of who holds authority in AI-driven search.
Let’s get one thing straight before the industry turns “GEO” into yet another three-letter source of confusion.
Generative engine optimization isn’t SEO with a new hat and a LinkedIn carousel. It’s a fundamentally different game.
If you’re still debating whether to swap the “S” for a “G,” you’ve already missed the point.
At its core, GEO is brand marketing expressed through generative interfaces.
Treat it like a technical tweak, and you’ll get technical-tweak results: plenty of noise, very little growth.
CMOs, this is where you step in.
SEOs, this is where you either evolve or get automated into irrelevance.
The question isn’t what GEO is – that’s been done to death.
It’s how to tell if your GEO is actually working.
The North Star: Share of search (not ‘share of voice,’ not ‘topical authority’)
The primary metric for GEO is the same one that should already anchor any brand-led growth program: share of search.
Les Binet didn’t coin a vanity metric for dashboards.
Share of search is a leading indicator of future market share because it reflects relative demand – your brand versus competitors.
If your share is rising, someone else’s is falling, and the future tilts your way.
If it’s declining, you’re mortgaging tomorrow’s revenue. That’s the unglamorous magic of it.
It isn’t perfect. But across category after category, share of search predicts brand outcomes with a level of accuracy that should make “awards case studies” blush.
And yes, GEO affects it, often through PR.
When an LLM recommends your brand (linked or not), some users still open a new tab and Google you.
Recommendation sparks curiosity. Curiosity drives search. Search is the signal.
Expect branded search volume to rise as generative usage grows, because people back-check what they see in AI results.
It’s messy human behavior, but it’s consistent.
Your first diagnostic: plot your brand’s share of search against your closest competitors.
Use Google Trends or My Telescope for branded demand, and triangulate with Semrush.
Watch the trend, not the weekly wobbles.
And do not confuse share of search with share of voice.
Different metric. Different lineage. Different purpose.
The two halves of the signal: Brand demand and buyer intent
Share of search has two practical layers for GEO diagnostics:
Brand search: The purest signal of salience. Are more people looking for you than last quarter, relative to the category? That’s how you know your brand availability is increasing inside generative engines and the culture around them.
Buyer-intent traffic: The money end. Of your non-branded search clicks, how much is clearly commercial or buyer-intent versus informational fluff? And how does your share of that buyer-intent traffic compare to competitors?
You won’t know a rival’s exact click-through rates – and you don’t need to.
Use Semrush to estimate non-branded commercial demand at the topic level for you and them, then compare proportions.
Export everything and segment aggressively by intent.
Where tool estimates diverge from your actuals, you’ll learn something about the noise in third-party data and the real shape of your market.
If your brand search is flat but buyer-intent share is rising, congratulations – you’re harvesting demand but not creating enough of it.
If brand search is rising but buyer-intent share isn’t, you have a conversion or content problem – your GEO is sparking curiosity, but your site and assets aren’t turning that into qualified traffic.
If both are up, pour fuel.
If both are down, stop fiddling with prompts and fix your positioning, advertising, and PR.
Competitors are winning in AI answers. Take back share of voice.
Benchmark your presence across LLMs, spot gaps, and get prioritized actions.
Compare share of voice and sentiment in seconds.
Category entry points: The prompts behind the prompts
GEO lives or dies on category entry points (CEPs) – Ehrenberg-Bass’ useful term for the situations, needs, and triggers that put buyers into the category.
CEPs are how real people think.
“I just left the gym and I’m thirsty.” That’s why there’s a Coke fridge by the exit.
“I’ve just come out of a show near Covent Garden and need food now.” That’s why certain restaurants cluster and advertise there.
These are not keywords. They’re human contexts that later materialize as words.
Translating that to GEO: your customers’ prompts in ChatGPT, Gemini, Perplexity, and AI Mode reflect their CEPs.
Newly appointed marketing manager under pressure to fix organic? That’s a CEP.
Fed up with a current tool because the price doubled and support disappeared? Another CEP.
Map the CEPs first, then outline the prompt families that those CEPs produce.
The wording will vary, but the thematic spine stays consistent: a role, a pain, a job to be done, a timeframe.
Once you’ve mapped CEPs to prompt families, you can evaluate your prompt visibility – how often and in what context generative engines surface you as a credible option.
This is a brand job as much as a content job.
LLMs don’t “decide” like humans. They triangulate across signals and citations to reduce uncertainty.
Distinctive brand assets, third-party coverage (PR), credible reviews, and consistent evidence of capability all raise your odds of being recommended.
Notice I didn’t say “more blog posts.” We’ll come back to that.
Once you’ve outlined your prompt families, test visibility systematically.
Run qualitative checks in the major models. Log the sources they cite and the types of evidence they appear to weight.
Are you visible when the CEP is “newly promoted CMO, six-month plan to grow organic pipeline”?
Are you visible when it’s “VP of ecommerce losing non-brand traffic to marketplace competitors, needs an alternative”?
If you’re absent, don’t complain about model bias – earn your spot with PR, credible case studies, and assets that reinforce what the engines are trying to prove about you.
Next, switch to the quantitative side.
In GSC, build regex filters for conversational queries – the long, natural-language strings (4 to 10 words, often more) that resemble prompts with the serial numbers filed off.
We don’t yet know how much of this traffic comes from bots, LLM scaffolding, or humans typing into AI-powered SERPs, but we do know it’s there.
Track impressions, clicks, and the proportion that are clearly buyer-intent versus informational.
If your conversational query clicks are growing and skewing commercial, that’s a strong signal your GEO is turning curiosity into consideration.
The two-second rule: Why informational content won’t save you
Here’s a hard truth for the SEO content mills: informational traffic is about to become even less valuable.
Most AI citations offer only fleeting exposure.
Brand recall takes more than a glance – in both lab and field data, you get roughly two seconds of attention to make anything stick.
Most sidebar mentions and AI Overview snippets don’t deliver that, and the memory fades fast anyway.
If your GSC export shows that 70% or more of your clicks come from “how-to” mush with no buyer intent, your GEO isn’t working.
It’s subsidizing the LLMs that will summarize you out of existence.
Fix the mix – shift your asset portfolio toward category entry points that actually precede purchase.
Here’s your weekly CMO/SEO standup. Four lines, no fluff.
1. Share of search (brand)
Your brand’s share versus your top three competitors, trended over 13 weeks.
Up is good. Flat is a warning. Down means it’s time to get comms and PR moving.
2. Share of buyer-intent traffic
Your estimated share of non-brand commercial clicks versus competitors (from tool triangulation), plus your actual buyer-intent clicks from GSC.
The gap between the two is your reality check.
3. Prompt visibility index
For each priority CEP, how often are you recommended by major models, and with what supporting evidence?
Track monthly.
Celebrate gains.
Fix absences with PR and proof.
4. Conversational query conversion
Impressions and clicks on 4–10+ word natural-language queries, segmented by intent.
Are the commercial ones rising as a share of total? If not, your GEO is a content cost center, not a growth driver.
How to read the scoreboard
If those four lines are improving together, your GEO is working.
If only one is improving, you’re playing tactics without strategy.
If none are improving, stop thinking you can “Wikipedia” your way to growth with topical-authority fluff.
The levers that actually move GEO
What moves the dial? Not more “SEO content.” GEO responds to the levers of brand availability:
PR that builds credible third-party evidence: Reviews, analyst notes, earned features, and founder or expert commentary with substance. LLMs love corroboration.
Customer-centered case studies: Framed around CEPs, not your product roadmap. “Marketing manager replaces X to cut acquisition costs in 90 days” beats “New feature launch.”
Tighter copy: Precise, functional language matched to CEPs and prompt families. Kill the poetry.
Experience signals: Your site must resolve buyer intent fast. The conversation from AI should land on pages that continue – not restart – the dialogue.
Content still matters, but only as support for these levers.
Most of your old blog inventory was never going to build memory or distinctiveness, and in an AI-summarized world, it certainly won’t.
Scrap the vanity spreadsheets. Build assets that make both engines and humans more certain you’re the right choice in buying situations.
Yes, content marketing is back in a big way – but that’s another article.
GEO isn’t just SEO
When AI modes become the default interaction layer, and they will – whether through chat, answers, or blended SERPs – the game rewards brands that are easy for machines to recommend in buying moments.
That is GEO’s beating heart: increasing AI availability.
Think of it like free paid search.
If you’re still obsessing over informational traffic and topical hamster wheels, you’ll be caught with the lights on and no clothes. Some of you already are.
SEOs who make the leap become organic-search strategists.
You’ll speak CEPs, buyer intent, and brand effects.
You’ll partner with PR, product marketing, and sales enablement.
You’ll still use the tools – Semrush and GSC – but you’ll use them to evidence strategy, not to justify content churn.
The rest of you? You’ll be replaced by an agentic workflow that writes better filler faster than you ever could.
The humbling truth about GEO
Marketing rewards humility.
You are not the consumer, and you are certainly not the model.
Stop guessing. Measure the four lines.
Map the category entry points.
Build the assets that make you easy to recommend.
Cross-reference tool estimates with your own data and let the differences teach you.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/How-to-know-if-your-GEO-is-working-AFtR26.jpg?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-14 12:00:002025-10-14 12:00:00How to know if your GEO is working
Google Lens now supports the Nano Banana, the image generation feature from the Gemini app, within Google Search. Google said, “we’re bringing Nano Banana to Google Search.”
Open the Google Lens feature in the Google app for Android or iOS. Then you can tap on Create mode to make an image. You can then transform an image into your ideas directly from Google Lens.
What it looks like. Here is a video of it in action:
Here are some screenshots:
Why we care. AI search features are moving fast and these fun and creative features might help win over consumer loyalty. OpenAI, Microsoft, Perplexity and other are all trying to compete with AI and Search. Who will win in the future is yet to be determined.
Google launched this in English in the U.S. and India, with more countries and languages coming soon, the company said.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/google-banana-1920-cK2kOm.jpg?fit=1920%2C1097&ssl=110971920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-13 16:54:052025-10-13 16:54:05Google Search gets Nano Banana in Google Lens
Google will roll out ads within AI Overviews beyond the U.S. to select English-speaking markets by the end of 2025, the company confirmed during its Google Access event last week.
Why we care. As AI-generated answers become a central part of Search, this expansion could reshape how advertisers reach users – with ads appearing directly alongside AI summaries rather than traditional text results.
Catch up. Ads in AI Overviews were first unveiled at Google Marketing Live 2025, allowing brands to appear within generative responses when users ask complex, multi-part queries.
What’s next. Google’s gradual rollout will give advertisers and users time to adapt to new ad placements and formats – and could provide early insights into how generative AI changes ad visibility, performance, and measurement across Search.
Bottom line. For advertisers, AI Overviews represent both an opportunity and a challenge – blending paid placements into AI-generated answers could drive richer engagement but may also require rethinking how to optimize for discovery and intent in a more conversational search environment.
First seen. This update was shared on LinkedIn by CEO of Profitmetrics.io Frederik Boysen, after hearing it announced Google Access meeting he attended last week.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/Screenshot-2025-10-13-at-15.42.54-YTBQ8r.webp?fit=524%2C276&ssl=1276524http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-13 16:27:572025-10-13 16:27:57Google to expand ads in AI Overviews to more markets
Google is globally launching a new “Sponsored results” label across desktop and mobile, grouping text and Shopping ads under a clearer header.
The update marks one of Google’s most visible ad labeling changes in years. It allows users to hide groups of ads directly on the search results page.
How it works. Text ads will now appear under a larger Sponsored results header.
The same label will apply to other formats, like Shopping ads.
Users can choose to hide entire groups of sponsored results for a more personalized browsing experience.
Why we care. Clearer ad labeling and the option for users to hide sponsored results could influence ad visibility and click-through rates – meaning brands will need to focus even more on ad relevance and creative quality to attract engaged users who actively choose to view their content.
The big picture. The change aims to make ad placements easier to identify while streamlining navigation, part of Google’s ongoing effort to balance user trust and advertiser visibility in Search.
Bottom line. For advertisers, clearer labeling could mean higher-quality clicks from users who better understand when they’re engaging with paid results.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/How-to-expand-from-paid-social-into-Google-Ads-MtHHWv.jpg?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-13 16:00:002025-10-13 16:00:00Google rolls out new global ‘Sponsored results’ ad label
