AI is already reshaping how buyers discover and choose brands.
When someone asks ChatGPT or Google AI Mode about your category, two things happen:
Brands are mentioned in the answer
Sources are cited as proof
Most companies get one or the other. Very few win both.
And that’s the problem.
According to the latest Semrush Enterprise AI Visibility Index, only a small fraction of companies appear in AI answers as both seen (mentions) and trusted (citations).
That gap is the opportunity.
We’re proposing the Seen & Trusted (S&T) Framework — a systematic approach to help your brand earn mentions in AI answers and citations as a trusted source.
Do both, and you multiply visibility, trust, and conversions across platforms like ChatGPT, Google AI Mode, and Perplexity.
SEO remains the foundation.
But AI doesn’t just look at your site. It pulls signals from review platforms, Reddit threads, news coverage, support docs, and community discussions.
When those signals are fragmented, your competitors will own the conversation.
This guide shows you exactly how to fix that with two playbooks:
Get Seen: Win favorable mentions in AI answers
Be Trusted: Earn citations as a reliable source
Run them together and you give AI no choice but to recognize, reference, and recommend your brand.
Why AI Search Strategy Isn’t Just SEO’s Job
Your SEO team can optimize every page on your site and still lose AI visibility to a competitor with weaker rankings but stronger brand signals.
Why? Because AI systems pull signals from everywhere, not just your website.
When AI generates responses, it mines:
Review platforms for product comparisons
Reddit threads for pricing complaints
Developer forums for implementation details
News sites for company credibility
Support docs for feature explanations
The challenge is that these signals live across different teams.
For instance, your customer success team drives customer reviews on G2 and Capterra. But if they’re not tracking review quality and detail, AI has nothing substantive to cite when comparing products.
Similarly, your product team controls whether pricing and features are actually findable. Hide everything behind “Contact Sales” forms, and AI will either skip you entirely or make assumptions based on old Reddit threads.
Your PR team lands media coverage and analyst reports. These third-party mentions build the trust signals AI systems use to determine authority.
Your support and community teams shape what gets said in forums and Discord servers. Their responses (or silence) directly influence how AI understands your product.
SEO and content teams own the site structure and content creation. But that’s just one piece now.
Without coordination, you get strong performance in one area, killed by weakness in another.
To grow AI visibility, you need synchronized campaigns — not just an “optimize for AI” line item tacked onto everyone’s OKRs.
That’s where the Seen & Trusted Framework comes in. It gives every team a role in building the signals AI depends on.
Note for enterprises: Cross-departmental coordination is challenging.
Fortunately, any progress each team makes in their area directly improves AI visibility.
Better reviews? You win. More transparent pricing? You win. Active forum engagement? You win. It all compounds.
This guide can be your internal business case. Forward the data on AI visibility gaps to stakeholders who need to see the competitive threat.
Solve this, and you’ll gain a big edge over competitors who are stuck in silos.
Playbook 1 – How to Get Seen (The Sentiment Battle)
Getting “seen” means showing up in AI responses as a mentioned brand, even without a citation link.
When a user asks ChatGPT, “What are the best email marketing tools?” they get names like HubSpot, ActiveCampaign, and MailChimp.
These brands just won visibility without anyone clicking through.
But here’s a challenge:
You’re fighting for favorable mentions against every competitor and alternative solution.
This is the sentiment battle.
Because AI doesn’t just list brands. It characterizes them.
You might get mentioned as “expensive but comprehensive” or “affordable but limited.”
Like here, when I asked ChatGPT if ActiveCampaign is a good option:
In some cases, the response could be more negative than neutral. Like this:
These characterizations stick.
So, how can your brand get more mentions and have a positive sentiment around?
There are four main sources that AI systems mine for context.
Pro tip: Track how AI platforms perceive your brand using Semrush’s Enterprise AIO sentiment analysis.
It shows whether mentions across ChatGPT, Claude, and other LLMs are positive, neutral, or negative.
Step 1. Build Presence on the Right Review Sites
AI systems heavily weigh review platforms when comparing products. But not all reviews are equal.
A detailed review explaining your onboarding process carries more weight than fifty “Great product!” ratings.
AI needs substance, like specific features, use cases, and outcomes it can reference when answering queries.
G2 is one of the top sources for ChatGPT and Google AI Mode in the Digital Technology vertical, according to Semrush’s AI Visibility Index.
The platform gives AI everything it needs: reviews, features, pricing, and category comparisons all in one place.
Slack ranks among the top 20 brands by share of voice in AI responses for the Digital Technology vertical.
Share of voice is a weighted metric from Semrush that reflects how often and how prominently a brand is mentioned across AI responses.
Part of that success comes from their G2 strategy.
When I ask ChatGPT, “Is Slack worth it?” it cites G2 as one of the sources.
Look at Slack’s G2 reviews and you’ll see why.
Its pricing, features, and other information are properly listed and up-to-date
Users write detailed reviews about channel organization, workflow automation, and integration setups.
G2 isn’t the only platform that matters.
For B2B SaaS: G2, Capterra, and GetApp
For ecommerce: Amazon reviews
For local/service businesses: Yelp and Google Reviews
In my experience, the depth of the review matters just as much as the platform — if not more.
You’ll see many very detailed product reviews as a source in AI answers from sites with low domain authority.
So, what does this mean in practice?
You need reviews from customers. And your review strategy needs four components:
Timing: Email customers after they’ve used your product enough to give meaningful feedbac, but while the experience is still fresh
Templates: Provide prompts highlighting specific features to discuss. “How did our API save you development time?” beats “Please review us.”
Incentives: Reward detail over ratings. A $XX credit for reviews over 200 words can generate more AI-friendly content
Engagement: Respond to every review. AI systems recognize vendor engagement as a trust signal.
Step 2. Participate in Community Discussions
Community platforms are where real product conversations happen. And AI systems are listening.
Reddit threads comparing alternatives
Stack Overflow discussions about implementation
Quora answers explaining use cases
These unfiltered conversations shape how AI understands and recommends products.
Reddit and Quora consistently rank among the top sources cited by ChatGPT and Google AI Mode across industries.
Like in the Business & Professional Services vertical here:
Online form builder Tally is a great example of dominating community discussions and winning the AI search.
AI-powered search is now their biggest acquisition channel, with ChatGPT being their top referrer.
This is their weekly signup growth of the past year, driven by AI search:
“Inclusion of web browsing is turned on by default, which made forums, Reddit posts, blog mentions, and authentic UGC part of the AI’s source material… We’ve invested for years in showing up in those places by sharing what we learn, answering questions, and being human.”
Here’s Marie talking about her product on Reddit:
And answering users’ questions:
And partaking in ongoing conversations:
This authentic engagement creates the context AI needs.
So, when I ask ChatGPT what’s the best free online form builder, it mentions (and recommends) Tally.
Big brands like Zoho take part in Reddit discussions as well. To answer questions, address concerns, and control their brand sentiment.
Like here:
Zoho ranks among the top brands by share of voice in ChatGPT and Google AI Mode responses. Just behind Google.
The community platforms like Reddit, Overflow, Quora, and even LinkedIn matter a lot in AI visibility:
Your community and customer success teams should be active on these platforms.
But presence alone isn’t enough.
Your strategy needs authenticity.
How?
Answer questions even when you’re not the solution
Address common misconceptions about your product (don’t let misinformation take over threads)
Share your actual product roadmap, including what you won’t build
Give detailed, honest responses to user complaints, even if it means acknowledging past mistakes
Encourage your product, support, or founder teams to answer technical or niche questions directly
AI systems can detect promotional language. They prioritize helpful responses over sales pitches.
The brands winning community presence treat forums like customer support, not marketing channels.
Step 3. Engineer UGC and Social Proof
User-generated content and social proof create a feedback loop that AI systems amplify.
When customers share their wins on LinkedIn
When users post before-and-after case studies
When teams document their workflows publicly
…all of this becomes training data.
Brands with strong community engagement and visible social proof see higher mention rates across AI platforms.
Patagonia is a fitting example here.
When I ask ChatGPT about sustainable outdoor brands, Patagonia dominates the response.
In fact, Patagonia holds the highest share of voice in AI responses for the Fashion and Apparel vertical.
They consistently appear in discussions around “ethical fashion” and “sustainable brands.”
Not because they advertise, but because customers evangelize. And that advocacy is visible everywhere.
Customers regularly mention their positive experience with Patagonia’s exchange policy.
There are countless positive articles written on third-party platforms about their products.
And on social platforms like Instagram.
These real-world endorsements are the kind of social proof AI recognizes and amplifies.
No wonder Patagonia has a highly favorable sentiment score (according to the “Perception” report of the AI SEO Toolkit).
So, how do you get people creating content (and proof) that AI pays attention to?
Encourage customers to leave ratings on trusted third-party sites
Partner with micro-influencers to share authentic product stories, tips, and reviews in their own voice
Invite users to post before-and-after results or creative use cases
Design features or experiences users want to show off (like Spotify Wrapped)
Reward customers who share feedback or use cases publicly (early access, shoutouts, or swag)
Reply to every public mention or tag because AI recognizes visible engagement
The mistake most brands make?
Asking for just testimonials instead of conversations.
Don’t ask customers to “share their success story.” Ask them to help others solve the same problem they faced.
The resulting content is authentic, detailed, and exactly what AI systems look for.
Step 4. Secure “Best of” List Inclusions
Comparison articles and ‘best of’ lists are key sources for AI citations.
When TechRadar publishes an article on top “Project Management Tools for Remote Teams,” that article becomes source material for hundreds of AI responses.
When Live Science reviews running watches, those comparisons train AI’s product recommendations.
These third-party validations carry more weight than your own content ever could.
In fact, sites that publish “best of” listicles consistently appear as top sources for AI platforms — including Forbes, Business Insider, NerdWallet, and Tech Radar.
Garmin is a perfect example.
Their products appear in virtually every “best GPS watch” article across running, cycling, and outdoor publications.
Like in this Runner’s World article:
Or this piece in The Great Outdoors:
But what makes their strategy work is consistency across platforms.
Yes, the specs are the same by nature.
But what stands out is how consistently those specs, features, and images appear across independent sites.
That repetition reinforces trust for AI systems, which see the same details confirmed again and again.
So, when I ask ChatGPT, “Which is the best GPS watch?” it mentions Garmin.
And it doesn’t stop there. It highlights features that other third-party articles emphasize, like battery life, accuracy, solar charging, and water resistance.
This consistency across independent sources is why Garmin holds one of the highest shares of voice in ChatGPT and Google AI Mode responses for the Consumer Electronics vertical.
So, how do you land in these “best of” lists?
It starts with a great product. Without that, no list will save you.
That aside, you need to make journalists’ jobs easier. Most writers work under tight deadlines and will choose brands that provide ready-to-use assets over those that make them hunt.
So build a dedicated press kit page with specs, pricing, high-res images, and other assets.
Like Garmin does here:
Next, reach out to journalists and niche publications. Don’t wait for them to find you.
Timing matters a lot as well.
Most “best of” lists update annually. So, pitch your updates a few months before refreshes.
Also, don’t just target obvious lists. Focus on category expansion.
For instance, Garmin doesn’t just appear in “best GPS watch” roundups. They also feature in broader outdoor and fitness lists that cover running, cycling, and multisport gear.
That reach multiplies the mentions AI systems can cite.
The bottom line: AI visibility favors the brands that keep showing up in independent comparisons.
Secure those “best of” inclusions, and you increase your chances of being mentioned in AI answers.
Playbook 2 – How to Be Trusted (The Authority Game)
Getting mentioned is half the battle. Getting cited is the other half.
When AI systems cite your content, they’re not just naming you. They’re using you as evidence to support their answers.
Look at any ChatGPT or Google AI Mode response.
At the bottom or side, you’ll see a list of sources. These citations are what AI considers trustworthy enough to reference.
According to Semrush’s AI Visibility Index, certain sources dominate AI citations across industries. Like Wikipedia, Reddit, Forbes, TechRadar, Bankrate, and Tom’s Guide.
They have achieved, what I call, the “Citation Core” status.
Citation core (n.): A small group of sites and brands that every major AI platform trusts, cites, and uses as default sources.
Why do these platforms get cited so often?
AI systems trust sources with verified information, structured data, and established credibility. They need confidence in what they’re citing.
This is the authority game.
You’ve earned mentions through the sentiment battle. Now you need to build the trust that also earns you citations.
This is how you maximize your AI visibility.
Here are five ways to build that authority.
Step 1. Optimize Your Official Site for AI
AI platforms can only cite what they can crawl, parse, and understand.
If your details aren’t exposed in clean, readable code, you’re invisible. No matter how good your content is.
Use semantic HTML to structure your content.
That means marking up pricing tables, product specs, and feature lists with tags like <table>, <ul>, and <h2>.
Don’t tuck information inside endless <div>s or custom layouts that hide meaning.
Also, avoid relying on JavaScript to render your main content.
AI crawlers can’t read JavaScript.
If your pricing or docs load only after scripts fire or buttons click, those details will be skipped.
Almost every top-cited site in AI answers passes the Core Web Vitals assessment, which signals that the page loads fast, stays stable, and presents content in a clean structure.
Like Bankrate — the most cited source in Google AI Mode for the Finance vertical:
Or InStyle — the 8th most cited source on ChatGPT in the Fashion & Apparel vertical.
These sites consistently surface in AI responses because their pages are easy to crawl, fast to load, and simple to extract structured information from.
A lot of what you’ll do to optimize your site for AI is SEO 101.
Structure all key information in native HTML elements (no custom wrappers)
Keep important content visible on initial load (no tabs, accordions, or lazy-loaded sections)
Use schema where it reinforces facts: pricing, product, FAQ, organization
Run regular audits with JavaScript disabled to see what AI sees
Minimize layout shifts and script dependencies that delay full render
To check your entire site’s health and performance, use Semrush’s Site Audit tool.
Get a detailed report showing technical issues on your website and how you can fix them.
At the end, you want a fast, stable, and easy-to-parse website.
That’s what earns AI citations.
Step 2. Maintain Wikipedia + Knowledge Graph Accuracy
AI systems rely on public data sources to build their understanding of your brand.
If that information is wrong, every answer AI generates about you will be too.
Wikipedia is one of the most cited sources on ChatGPT for all industries covered in Semrush’s AI Visibility Index.
Interestingly, Google AI Mode leans heavily on its Knowledge Graph to validate facts about companies and products.
When your Wikipedia page contains outdated info — or your Knowledge Graph shows old details — those inaccuracies get baked into AI responses.
That hurts trust, sentiment, and your chance of being cited in the long-term.
So your job is twofold:
Make sure your brand exists in these systems
Keep the data clean and current
Start with your Wikipedia page.
If you have one, audit it quarterly.
Fix factual errors, like outdated product names, revenue ranges, or leadership bios.
Support every edit with a credible third-party source: news coverage, analyst reports, or industry publications.
Wikipedia doesn’t allow brands to directly promote themselves. And promotional edits get removed.
But updates to fix factual errors usually stick. As long as you provide solid citations.
You can use the “Talk” page of your Wikipedia entry to propose corrections.
If you don’t have a Wikipedia page, you’ll need to meet notability guidelines.
That typically means coverage in multiple independent, well-known publications.
Once that’s in place, a neutral editor (not on your payroll) can create the page.
Next, fix your Knowledge Graph.
Google pulls its brand facts for its knowledge graph from multiple sources. Like Wikidata, Wikipedia, Crunchbase, social profiles, and your own schema markup.
Start by “claiming” your Knowledge Panel.
This means a knowledge panel already exists for your company when you search its name. You just have to claim it by verifying your identity.
If you don’t see one, you’ll need to feed Google more structured signals.
Start by adding or improving your Organization schema on your homepage.
Then, make sure your company has a proper Wikidata entry. Google may use this to build its Knowledge Graph.
Note: Adding your company to Wikidata is much easier than getting a full Wikipedia entry. But you still need to follow the guidelines. Stick to neutral language, avoid any promotional tone, and cite credible third-party sources.
A strong Wikipedia page and Google knowledge panel shape how AI understands your brand.
Get them right, and you build a foundation of factual authority that AI systems can trust.
Step 3. Publish Transparent Pricing
Hidden pricing creates negative sentiment that AI systems pick up and amplify.
When users can’t find your pricing, they turn to Reddit and LinkedIn. And the speculation isn’t always favorable.
For instance, Workaday doesn’t show its pricing.
And the Reddit comments aren’t helpful to its potential customers.
According to Semrush’s AI Visibility Index, when enterprise software hides pricing behind “Contact Sales,” AI uses speculative data points from Reddit and LinkedIn.
And it often links that brand with negative price sentiment.
Because AI systems are biased toward answering, even if it means citing speculation.
They’d rather quote a complaint from third-party sites about “probably expensive” than admit they don’t know.
Without clear pricing, you’re also excluded from value-comparison queries like “best budget option” or “most cost-effective for enterprises.”
Publishing transparent pricing creates reliable data that AI trusts over speculation.
Now I understand this isn’t always possible for every brand. Whether to show pricing depends on various other decisions and strategies.
But if you want to build trust for higher AI visibility and positive sentiment, transparent pricing is important.
Which means:
Include tier breakdowns with feature comparisons
Spell out annual vs. monthly options
List any limitations or user caps
Update your pricing on G2, Capterra, and other review sites
When reliable sources like your pricing page and G2 have clear information, AI stops turning to speculation.
That transparency becomes part of your brand identity and authority.
Step 4. Expand Documentation & FAQs
Your support docs and help center often get cited more than your homepage.
Because AI systems look for detailed, problem-solving content. Not marketing copy.
Apple holds one of the highest shares of voice in ChatGPT and Google AI Mode responses for the Consumer Electronics vertical.
Its support documentation appears consistently in AI citations across tech queries.
When I ask ChatGPT how to fix an iPhone issue, it cites support.apple.com.
Product documentation dominates citations in technical verticals.
Why?
Because it answers specific questions with step-by-step clarity.
Your product documentation is a citation goldmine if you structure it right.
Start by creating dedicated pages for common problems. “How to integrate [Product] with [Product]” beats a generic integrations page.
For example, Dialpad has dedicated pages for each app it integrates with.
And each page clearly explains how to connect both apps.
Next, write troubleshooting guides that address real user issues.
(You can learn about these issues from your sales teams, account managers, and social media conversations.)
Also, build a comprehensive FAQ library that actually answers questions. Not marketing-friendly softballs, but the hard questions users really ask.
Make sure every page is crawlable:
Use static HTML for all documentation
Create XML sitemaps specifically for docs
Implement breadcrumb navigation
Add schema markup for HowTo and FAQ content
The goal is to become the default source when AI needs to explain how your product works.
Not through SEO tricks, but by publishing the most helpful, detailed, accessible documentation in your space.
Step 5. Create Original Research That AI Wants to Cite
Original research gives AI systems something they can’t find anywhere else. Your data becomes the evidence they need.
Take SentinelOne as an example. It’s a well-known brand in cybersecurity.
They regularly publish threat reports, original data, and technical insights.
This is one of the reasons they often get cited as a source in AI responses.
In the intro, I said very few brands are both mentioned and cited by AI. Remember?
SentinelOne is one of those brands that has built dual authority.
According to Semrush’s AI Visibility Index, it’s the 15th most cited and 19th most mentioned brand in the Digital Technology vertical.
Because it publishes original insights that aren’t available anywhere.
And AI systems want: verified data, industry insights, and quotable statistics.
But not all research gets cited equally.
Annual surveys with significant sample sizes (think: 500+) carry weight. But “State of [Industry]” reports based on 50 responses might not.
Benchmark studies comparing real performance data become go-to references. But thinly-veiled sales pitches disguised as research might get ignored.
You can use your proprietary data to create original research reports.
Or team up with market research companies like Centiment that can help you collect data through surveys.
When creating these reports:
Lead with key findings in bullet points
Include methodology details for credibility
Provide downloadable data sets when possible
Add structured data markup for datasets
Also, promote findings through press releases and industry publications.
When Forbes, TechCrunch, and other leading publications cover your research, AI systems are more likely to notice.
Like this SentinelOne report covered by Forbes:
The compound effect here is powerful.
Your research gets cited by news outlets → which gets cited by AI → which drives more coverage → which builds more authority.
That’s how you go from being mentioned to being the source everyone (including AI) trusts.
Pulling It All Together – Running Both Playbooks
You’ve seen the framework. Now it’s time to execute.
Step 1. Audit Your Current AI Visibility
Start by understanding your baseline.
Run test queries in ChatGPT and Google AI Mode. Search for your brand, your category, your product, and the problems you solve.
Note where you’re mentioned (in the answer itself) and where you’re cited (in the source list). Screenshot everything.
If you’re using Semrush’s Enterprise AIO, you can use Competitor Rankings to see how often your brand shows up in AI answers compared to your competitors.
Step 2. Build Parallel Campaigns
Both playbooks need to run simultaneously.
You can’t wait to be “seen” before building trust.
Playbook 1 (Seen): Customer success drives review campaigns. Community managers engage in forums. PR pushes for “best of” list inclusion.
Playbook 2 (Trusted): Product publishes transparent pricing. SEO and engineering improve site structure. Support expands help content. Marketing creates original research.
The key is coordination.
Create a shared dashboard to track each team’s contributions to AI visibility.
Step 3. Monitor and Iterate
AI visibility shifts fast. What worked last month might not work today.
Track your mentions and citations monthly.
Use an LLM tracking tool like Semrush or a manual prompt list to see how you’re showing up (and how often).
Google is piloting a new “Sales & Promotions Feature Bundle with Flighted Budgets” in Google Ads, designed to help advertisers push harder during short-term promos without wasting spend.
What’s new
Campaign Total Budgets: Fix a set spend across 3-90 days.
Promotion Mode: Accelerates spend for 3-14 days, prioritizing volume over strict efficiency.
Cross-campaign support: Works with Performance Max, Search, and Shopping – including tROAS and tCPA bidding strategies.
Why we care. This update gives more control over spend pacing and volume during promotions, something current Google Ads tools can’t fully deliver. Instead of just telling Smart Bidding that conversion rates will spike, the feature bundle actively reallocates budget to hit promo goals – whether for flash sales, holiday weekends, or ticket launches. In short, it helps advertisers spend faster, scale smarter, and maximize returns when timing matters most.
How it’s different. Instead of just adjusting for expected conversion rate shifts, the bundle uses sale dates, promo assets, and explicit ROAS tradeoffs to give Google Ads stronger signals for promotion periods.
Best fits
Flash sales
Holiday weekends and seasonal promotions
Ticket launches, travel deals, and other time-sensitive offers
What’s next. Advertisers running Q4 promos could see major upside if they test this tool early. The big shift will be deciding when to prioritize scale over efficiency – a tradeoff this feature makes more explicit than ever.
First seen. This alpha release was noted by Yash Mandlesha, co-founder of Mediagram, on LinkedIn.
The search and marketing world never slows down. Last week’s inaugural edition of Semrush’s Marketing Countdown, featuring Search Engine Land, explored how the landscape is rapidly shifting under our feet.
We unpacked five of the biggest stories making waves:
Bottom line: SEO remains critical in the AI-driven search era. A strategic, brand-focused, and user-first approach is essential. Companies must align messaging, produce authoritative content, and track emerging AI visibility metrics to thrive in a diversified, AI-influenced ecosystem.
Here’s the video of everything you need to know to stay ahead of the curve – plus takeaways and insights you won’t want to ignore.
Marketing Countdown was hosted by Rita Cidre, head of Academy at Semrush, and featured:
Mordy Oberstein, Founder of Unify and communications advisor for Semrush
Danny Goodwin (that’s me), Editorial Director at Search Engine Land
Erich Casagrande, content product specialist at Semrush
It focused on the evolving landscape of SEO, the impact of AI on search, and actionable marketing strategies. Some of the key themes discussed:
Generative AI in search
AI is changing how people research, but Google remains the dominant starting point due to habit and trust.
AI summaries offer convenience but often reduce clicks to websites, posing challenges for publishers.
Google’s AI upgrade
Google’s announcement of its biggest search upgrade lacked transparent data.
Publishers report rising impressions but falling clicks, showing a “great decoupling” between search visibility and user traffic.
Answer engines and content
Platforms like Perplexity highlight the need for authoritative content, topical authority, and trusted citations.
Video content and user engagement are increasingly important for visibility.
Google AI Mode
Rolled out in 180+ countries.
Presents comprehensive AI-generated answers in a separate tab, suggesting a future where AI synthesizes multiple subtopics into a single response.
ChatGPT & Google
Despite OpenAI’s claims of Bing reliance, ChatGPT Plus reportedly pulls from Google results, reinforcing Google’s central role in SEO.
Shift in marketing strategy
Marketers need to blend tactical SEO with brand-building.
Fragmented channels and AI-driven search require holistic, integrated strategies.
Unsiloing teams
Consistency across marketing and AI platforms is essential to avoid contradictory brand messaging.
SEO best practices
Focus on high-quality, user-centric, contextual content rather than outdated keyword tactics.
New metrics include brand mentions, sentiment analysis, and AI visibility tracking.
Content sources for AI
YouTube and Reddit are frequently cited in AI answers.
TikTok and Instagram are less influential in this context.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/09/marketing-countdown-5-industry-shakeups-sept-2025-GbUl0D.jpg?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-09-09 14:00:002025-09-09 14:00:00Video: 5 AI search stories you need to know (September 2025)
Google is rolling out new tools in Google Ads designed to unify web and app advertising, making it easier for marketers to deliver consistent customer journeys and measure performance across platforms.
What’s new
Web to App Connect expansion: You can now send YouTube, Hotel, and Demand Gen ad clicks directly to apps – extending the feature beyond Performance Max, Search, and Shopping campaigns. Google says brands using Web to App Connect on YouTube have seen 2x higher conversion rates.
Unified workflows:
In-product nudges now help you optimize toward in-app events.
Unified conversions bundle app and web events for easier setup.
A new combined overview card shows side-by-side web and app performance directly on the Ads homepage.
App install measurement from web campaigns: For the first time, Search and Shopping campaigns can be credited with driving new app installs and in-app conversions.
Why we care. Managing campaigns across websites and apps has long been a pain point. Customers often bounce between platforms before converting, and disconnected reporting makes it difficult to see what’s working. These updates could help you tighten your funnel, reduce wasted spend, and create app-first strategies that unlock higher ROI.
The big picture. By connecting web and app activity inside Google Ads, you can:
Attract high-value customers: Push users into apps, where they’re more likely to engage and convert.
Streamline campaigns: Target and optimize across web + app without juggling separate workflows.
See the full funnel: Attribute installs and conversions to web campaigns for a more accurate performance picture.
What’s next.With unified reporting, it’ll be easier to spot which touchpoints drive the most value – but it may also expose underperforming spend. Expect brands to test more app-first journeys, especially in categories like retail, travel, and subscription services, where in-app conversions typically outperform the web.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-09-09 13:24:592025-09-09 13:24:59Google Ads links web + app campaigns with new features
Today, we’re excited to welcome Yoast AI Summarize to our growing family of AI features. Just like our other AI tools, this new feature is designed to make your publishing process faster and easier by putting powerful, practical AI right where you work, in the WordPress Block Editor.
Yoast AI Summarize is perfect for bloggers, content teams, agencies, and publishers who want to give readers instant value while also making sure their posts clearly communicate the intended message.
What does Yoast AI Summarize do?
You’ve finished drafting your post, great! But before you hit “Publish,” wouldn’t it be helpful to instantly see the core points your content is actually conveying? That’s exactly what Yoast AI Summarize does.
With one click, you can insert a Key Takeaways block into your content. Yoast AI Summarize scans your post’s main body and creates a short, bullet-point summary, giving your readers a quick, scannable snapshot, and giving you a chance to check if your post is truly saying what you want it to.
How you can access the new feature
Yoast AI Summarize is automatically available to all Yoast SEO Premium customers. Just make sure you’ve updated to the latest version and granted consent to use AI.
Once enabled, simply:
Open your post in the WordPress Block Editor.
Add the new block from the “Yoast AI Blocks” section
Click to generate summary, and watch your Key Takeaways section appear in seconds.
Where you can use Yoast AI Summarize
Right now, Yoast AI Summarize works in the WordPress Block Editor on posts and pages. The block is fully editable, you can change the title, rewrite bullet points, or move it anywhere in your content flow.
Pricing and usage
There are no hidden costs for Yoast AI Summarize, it’s included in Yoast SEO Premium. Like our other AI features, it uses our spark counter to track usage.
A spark is a single click on an AI feature.
Generating one summary = one spark.
Your spark counter resets at the start of each month.
There’s currently no hard limit, so you can experiment freely.
Limitations
Yoast AI Summarize is currently in beta. That means you may notice a few restrictions:
Only available in the WordPress Block Editor.
Summaries are excluded from Yoast SEO and Readability Analysis to protect your scores.
Currently works only on published or drafted content within supported blocks.
For very long posts, it may take a few seconds for the summary to generate.
Try out Yoast AI Summarize today
Upgrade to Yoast SEO Premium to unlock this and all our AI features, including the award-nominated Yoast AI Generate and the powerful Yoast AI Optimize. With Yoast AI Summarize, you can work faster, keep your content aligned with your intent, and give your readers instant value with clear, scannable takeaways.
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As part of our broader APAC plan, which includes new Deep Dive events and two local language
editions, we are happy to announce the details for our Chinese language event: Search
Central Live Hong Kong!
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If these tools don’t reference your content, you’re missing out on a growing share of visibility. That’s where LLM seeding comes in.
LLM seeding involves publishing content in places and formats that LLMs are more likely to crawl, understand, and cite. It’s not a traditional SEO strategy or “prompt engineering.” Instead, you’ll use this strategy to get your content to appear in AI-generated answers, even if no one clicks.
We’ll cover what LLM seeding is, how it works, and the steps you can take to start showing up in AI responses before your competitors get there first.
Key Takeaways
LLM seeding involves publishing content where large language models are most likely to access, summarize, and cite.
Unlike SEO, you’re not optimizing for clicks. Instead, you’re working toward citations and visibility in AI responses.
Formats like listicles, FAQs, comparison tables, and authentic reviews increase your chances of being cited.
Placement matters. Publish on third-party platforms, industry sites, forums, and review hubs.
Track results and monitor brand mentions in AI tools, referral traffic from citations, and branded search growth from unlinked citations across the web.
What is LLM Seeding?
LLM seeding is publishing content in formats and locations that LLMs like ChatGPT, Gemini, and Perplexity can access, understand, and cite.
Instead of trying to rank #1 in Google search results, you want to be the source behind AI-generated answers your audience sees. The goal is to show up in summaries, recommendations, or citations without needing a click. The fundamentals overlap with SEO best practices, but the platform you’re optimizing for has changed.
Let’s say you run a productivity software company. Your content marketing team writes a detailed comparison post about the “Best Project Management Tools for Remote Teams.” A month later, someone asks ChatGPT that exact question, and your brand name shows up in the response, even though you don’t rank on page one in Google.
How did the LLM find your information? Here’s what it looks like behind the scenes.
LLMs have been trained on massive datasets pulled from the public web, including blogs, forums, news sites, social platforms, and more. Some also use retrieval systems (like Bing or Google Search) to pull in fresh information. When someone asks a question, the model generates a response based on what it has learned and in some cases, what it retrieves in real time.
Well-structured content, clearly written, and hosted in the right places, is more likely to be referenced in the response: an LLM citation. It’s a huge shift because instead of optimizing almost exclusively for Google’s algorithm, you’re now engineering content for AI-visibility and citations.
Asking ChatGPT for a list of the best laptop backpacks provides several citations and options.
LLM Seeding vs. Traditional SEO
Traditional SEO focuses on ranking high on Google to earn clicks. You optimize for keywords, build backlinks, and improve page speed to attract traffic to your site.
LLM seeding flips that on its head.
You don’t chase rankings. You build content for LLMs to reference, even if your page never breaks into the top 10. The focus shifts from traffic to trust signals: clear formatting, semantic structure, and authoritative insights. You provide unique insights and publish in places AI models scan frequently, like Reddit, Medium, or niche blogs, which increases your chances of being surfaced in AI results.
SEO asks, “How do I get more people to click to my website?”
LLM seeding asks, “How do I become the answer, even if there’s no click?”
The thing is, it’s not an either/or proposition. You still want to do both. But you’re invisible to a constantly growing audience if you’re not thinking about how AI tools interpret and cite your content.
Benefits of LLM Seeding
LLM seeding goes beyond vanity metrics to the visibility that actually sticks, even when clicks don’t happen. It can be a real game-changer because it lets you do the following:
Stay visible in AI search: Astools like ChatGPT, Gemini, and Perplexity replace traditional searches for quick answers, content needs to appear inside those responses, not just in the search results below them.
Earn brand mentions without needing the click: LLMs don’t always link back, but mentions can still be wins. They keep your brand top of mind and build familiarity, and they nudge users to search for you by name later.
Build authority at scale: When LLMs start citing your brand alongside major players, it’s like being quoted in the New York Times of AI. You earn topical authority and credibility by association.
Bypass the ranking fight: You don’t need to beat everyone to position one. You just need the best answer. From what we know right now, good focus areas are building around clarity, structure and trust signals.
Get ahead while others sleep on it: LLM seeding is still an “under-the-radar” strategy. Right now, you’ve got a first-mover advantage. Don’t wait until your competitors are already showing up in AI responses.
Best Practices For LLM Seeding
If you want LLMs to surface and cite your content, you need to make it easy to find, read, and worth referencing. Here’s how to do that:
Create “Best of Listicles”
LLMs prioritize ranking-style articles and listicles, especially when they match user intent, such as “best tools for freelancers” or “top CRM platforms for startups.” Adding transparent criteria boosts trust.
Use Semantic Chunking
Semantic chunking breaks your content into clear, focused sections that use subheadings, bullet points, and short paragraphs to make it easier for people to read. This structure also helps LLMs understand and accurately extract details. If you’re having trouble thinking about where to start, think about FAQs, summary boxes, and consistent formatting throughout your content.
Write First-Hand Product Reviews
LLMs tend to favor authentic, detailed reviews that include pros, cons, and personal takeaways. Explain your testing process or experience to build credibility. Websites like Tom’s Guide and Wirecutter do an excellent job of this.
Wirecutter’s table of contents breaks down why they choose the items they choose and why you, the reader, should trust them.
Add Comparison Tables
Side-by-side product or service comparisons (especially Brand A vs. Brand B) are gold to LLMs. You’re more likely to be highlighted if you include verdicts like “Best for Enterprise” or “Best Budget Pick.” An example of a brand that does comparison tables particularly well is Nerdwallet.
Include FAQ Sections
Format your FAQs with the question as a subheading and a direct, short answer underneath. LLMs are trained on large amounts of Q&A-style text, so this structure makes it easier for them to parse and reuse your content. FAQ schema is also fundamental to placement in zero-click search elements like featured snippets. The structured format makes your content easier for AI systems to parse and reference.
Almost every article we publish on our site features FAQs that have been properly formatted.
Offer Original Opinions
Hot takes, predictions, or contrarian views can stand out in LLM answers, especially when they’re presented clearly and backed by credible expertise. Structure them clearly and provide obvious takeaways.
Demonstrate Authority
Use author bios, cite sources, and speak from experience. LLMs use the cues to gauge trust and credibility. If you’ve been focusing on meeting E-E-A-T guidelines, much of your content will already have this baked in.
Layer in Multimedia
While ChatGPT may not show users photos inside the chat window, screenshots, graphs, and visuals with descriptive captions and alt text help LLMs (and users who do click through) better understand context. It also breaks up walls of text.
Build Useful Tools
Free calculators, checklists, and templates are highly shareable and are easy for AI systems to parse and extract. Make sure the title and description explain each item’s value upfront.
It’s telling that many of the best practices for traditional SEO often work well for LLM seeding. At their core, both priorities involve giving people the best possible answers to their questions in a highly readable and simple way to digest. In fact, creating content that works well for all avenues is a cornerstone of search everywhere optimization.
Ideal Platforms for LLM Seeding Placement
Publishing on your site isn’t enough to excel with LLM seeding. AI models pull from a wide mix of sources across the web. The more places your content shows up, the more likely it is to influence or be cited in AI-generated answers.
1. Third-Party Platforms
LLMs tend to surface structured, public content hubs. Medium, Substack, and LinkedIn articles get crawled often and carry extra weight because of their clean formatting and tied-to-real-author profiles. These sites publish large volumes of content and are widely trusted, so your content benefits from their visibility and is more likely to be surfaced in AI-generated answers.
2. Industry Publications & Guest Posts
Contributing to trusted outlets, such as trade blogs, marketing publications, and niche news sites, offers your brand credibility and increases the odds of your content being surfaced or cited in AI-generated answers.
3. Expert Quotations
Offering quotes to journalists or bloggers through services like HARO or Featured can land you in articles LLMs surface and cite repeatedly.
4. Product Roundups and Comparison Sites
Sites like G2, Capterra, or niche review sites are LLM goldmines. Get your customers to leave detailed reviews and provide quotable explanations about why your product or service stands out.
5. Forums and Communities
Reddit and Quora are two of the most frequently surfaced sources in AI answers. Niche forums and communities (such as AVS Forum or Contractor Talk) also carry weight because they’re packed with authentic, experience-driven insights. Consider creating a public-facing profile to answer questions about your product or service. In addition, they’re excellent spaces to source user-generated content (UGC) that can provide additional context and support.
6. Editorial Microsites
Small, research-driven microsites can carry more authority than heavily branded pages. Because they are often well-structured, focused, and treated as independent resources, they are more likely to be picked up by LLMs when generating answers.
7. Social Media
Platforms like LinkedIn, YouTube, and even Reddit threads can double as searchable databases for LLMs. Use structured language, captions, and context in every post.
Here’s the bottom line: LLM seeding works best when your content is everywhere AI looks, not just on your blog.
How To Track LLM Seeding
Tracking LLM seeding is different from tracking SEO performance. You won’t always see clicks or referral traffic, but you can measure impact if you know where to look. These KPIs matter the most:
1. Brand Mentions in AI Tools
Manual testing: Tryrunning audience-style prompts in ChatGPT, Gemini, Claude, and Perplexity in incognito mode so past queries don’t bias results. As a note here, results can vary from instance to instance, so test multiple times to see consistent patterns.
We’re in pretty good company among the top five resources.
Tracking tools: Perplexity Pro lets you see citation sources, while ChatGPT Advanced Data Analysis can sometimes surface cited domains. Even enterprise tools like Semrush AIO have started to track brand mentions across AI models. There are also dedicated tools like Profound that specifically focus on AI visibility.
2. Referral Traffic Growth
Using tools like GA4 can help you determine LLM seeding’s effectiveness, but not via traditional metrics.
With GA4, you’ll want to navigate through Reports > Acquisition > Traffic Acquisition and then filter for your chosen form of traffic. Be sure to review the source/medium dimension for more details about specific LLM platforms. Referral traffic may come from LLMs if they include a clickable link to your website. By contrast, brand mentions without links are more likely to drive users to search for you after using an LLM, which GA4 usually classifies under organic search.
This isn’t super-likely by comparison. Since this is less common, it’s best to look at referral traffic alongside LLM visibility metrics for the full picture of performance.
3. Unlinked Mentions
You have several options for seeking out unlinked mentions. Set up Google Alerts for brand name or product mentions; that can help you surface when your brand is mentioned in the news or other platforms. For example, Semrush’s Brand Monitoring tool lets you look for citations without backlinks.
Semrush touts its brand monitoring tool as one of the best in the business.
4. Overall LLM Visibility
No matter which tools you use, building a log to track your monthly tests across AI platforms can provide insights. Document the tool(s) used, prompt asked, and the exact phrasing of the mention. You’ll also want to track your brand sentiment; is your brand being talked about in a positive, neutral, or negative light?
Companies like Serpstat, Similarweb, and Profound have begun to offer AI visibility reporting, and those options will mature fast.
There’s currently no silver bullet to track LLM seeding comprehensively. It’s partly manual work, partly analytics, and partly new tools still in beta. You can create an AI Visibility Dashboard that combines GA4, brand monitoring, and a spreadsheet of monthly AI prompts to get a head start.
FAQs
What is LLM seeding?
LLM seeding is publishing content in formats and locations that large language models (LLMs) are more likely to surface and cite. Instead of optimizing only for search rankings, you’re optimizing for visibility in AI-generated answers.
What are LLM citations?
An LLM citation happens when an AI platform like ChatGPT, Gemini, or Perplexity references your content with a source link in its response.
What is an LLM mention?
An LLM mention is when an AI platform references your content but doesn’t provide a clickable source link.
How do I know if my brand is being cited?
Run audience-style prompts in AI tools (like “best project management software for startups”) and see if your brand shows up. Also, track referral traffic trends in GA4.
Conclusion
Search looks different today because users no longer rely exclusively on Google. Your audience asks questions in ChatGPT, Gemini, and other AI tools. They’re now the ones who decide which brands get mentioned.
LLM seeding matters. You can stay visible even when clicks don’t come and earn credibility by showing up in AI responses. This futureproofs your marketing against zero-click trends and keeps you agile and top of mind.
To win this new landscape, start small: publish in formats LLMs love like listicles, FAQs, and comparisons), seed content across third-party platforms, and track whether your brand shows up in AI outputs.
The companies that adapt today will own the conversation tomorrow.
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Google is no longer the only place people search. Millions now bypass search engines entirely and turn to large language models (LLMs) like ChatGPT, Gemini, and Perplexity for answers.
This creates a massive opportunity. LLM SEO is how you get your content in front of those systems. The idea is to make your content so clear and credible that a model has no choice but to pull from it.
That means writing in a way machines can process, and people still want to read. Do it right, and you’ll show up where the traffic is already shifting.
This isn’t a future concern. It’s happening now. If you don’t adapt, readers will still get answers—just not from you. You’ll lose the click before you even get the chance to earn it.
Key Takeaways
LLM SEO makes your content visible to large language models like ChatGPT, Gemini, and Perplexity.
Unlike traditional SEO, visibility in LLMs means being cited in AI-generated answers vs. just ranking in search results.
Clarity, structure, and credibility are important factors that increase the likelihood LLMs will surface your content.
LLM SEO builds on traditional SEO. You still need a strong technical and content foundation.
Embracing LLM SEO now gives you a leg up on the competition. Most marketers aren’t yet focused on how LLMs deliver answers.
Citations, mentions, and brand visibility inside AI tools are emerging markers of success with SEO for LLMs. You can’t measure performance just by clicks or keyword rankings.
What Is LLM SEO?
LLM SEO is the process of optimizing your content so that large language models can understand, interpret and surface is in their responses. Think of it as preparing your content for systems like ChatGPT, Gemini, and Perplexity just as you prepare content for Google.
Instead of focusing only on rankings, LLM SEO targets being recognized as a credible source. That means:
Writing in a clear, direct style that reflects how people naturally ask questions.
Structuring content with headings, FAQs, and lists so models can easily pull useful snippets.
Building authority through transparent sourcing, strong E-E-A-T signals, and unique insights.
Publishing content in multiple formats, like text, video, and visuals, which increases the chances that models can understand and incorporate your content.
LLM and traditional SEO share the same goal: to connect your expertise with what people are looking for. What’s changing is where and how those answers show up.
LLM SEO specifically targets making your content easy for large language models to parse and cite, often in search engine-related contexts. This includes optimizing for Google’s AI Overviews (AIOs) and ensuring your content is structured so it’s more likely to be surfaced by AI-driven platforms like ChatGPT or Gemini.
LLMO goes further. It’s about increasing your brand’s overall visibility in AI-generated answers across platforms like ChatGPT, Perplexity, Gemini, and Claude. That reach isn’t limited to search. It also means:
Ensuring your content is easy to find in sources LLMs actively use, like crawlable websites and public databases.
Using structured data, schema, and multi-format content so LLMs can interpret your information cleanly.
Building authority and mentions across the web to build trust in your brand so it’s cited and not just ranked.
In short, LLM SEO helps you show up in AI answers connected to search. LLMO ensures your brand is present across any context where large language models generate responses.
LLM SEO vs. Traditional SEO
LLM SEO builds on the foundation of traditional SEO but shifts the focus to how large language models process and deliver information.
Traditional SEO is about rankings. You optimize for Google or Bing so your content climbs the results page. Success is measured in keyword positions, clicks, and traffic.
LLM SEO is about citations. Instead of fighting for position one, you make your content easy for LLMs to read, trust, and include in their responses. Success is measured in mentions and visibility inside tools like ChatGPT or Gemini, even if the user doesn’t click through.
The overlap is important. Both require:
High-quality, well-structured content.
Strong signals of expertise, authority, and trust (E-E-A-T).
Technical performance, like fast load times and mobile readiness.
The differences matter. Traditional SEO leans on backlinks and click-through optimization. LLM SEO rewards clear language, structured formats like FAQs and lists, and transparent sourcing. Whereas SEO optimizes for crawlers, LLM SEO optimizes for language models.
Marketers who stop at traditional SEO risk losing visibility as more searches end inside AI answers.
Instead of clicking through search results, people ask AI tools like ChatGPT direct questions and get immediate answers. That shift is changing brand discovery.
You can already see this shift playing out, with some industries showing up in AI Overviews far more often than others.
For businesses, the risk is obvious. If your content isn’t structured for LLMs, your expertise may never surface, even if you rank well in Google. That means losing visibility to competitors optimizing for both.
There’s also the matter of trust. LLMs lean heavily on authoritative, clearly written content with well-cited sources. If your brand is not putting out content that signals credibility, you’re less likely to be included in the answers users see.
Finally, this shift is accelerating. More platforms are rolling out AI-driven responses, and users are adopting them quickly because they save time.
Every month you wait is a month of lost visibility. LLM SEO puts your brand where attention is headed, not where it’s fading.
Best Practices for LLM SEO
Visibility in large language models isn’t about hacks. It comes down to making your content easier for these systems to understand, trust, and reuse. The following practices build on what already works in SEO but adapt it for how LLMs process and deliver information.
Write Conversational and Contextual Content
Large language models are built to handle natural conversation. Content that reads conversationally and adapts to context is more likely to be included in generated answers. Drop the keyword stuffing and rigid phrasing. Instead, write the way people actually ask (and follow up on) questions.
Implement FAQs and Key Takeaways
LLMs thrive on clarity. Adding FAQ sections and concise takeaways gives them ready-made snippets they can use to build answers. It helps readers, too, breaking content into scannable, useful chunks while giving AI systems obvious entry points into your page.
Use Semantic and Natural Language Keywords
Traditional SEO often leaned on exact-match keywords. LLM SEO works better with semantic and contextual phrasing, language that reflects how people naturally ask questions. Build around related terms and long-tail queries so models can recognize intent and surface your content more often.
Maintain Brand Presence and Consistency
LLMs look for signals of authority and consistency across multiple platforms. A brand that regularly publishes on its own blog, contributes to third-party sites, and maintains a strong profile across social channels is more likely to be trusted. Consistency reinforces your credibility.
Share Original Data, Insights, and Expertise
Original insights stand out. Publishing unique research, case studies, or proprietary data makes your content more valuable to LLMs. These models are designed to identify and prioritize information not easily found elsewhere. For example, graphics like the piece below showcase data points that my team sourced on its own.
Monitor and Query LLM Outputs
Optimization does not stop at publishing. Regularly test how your brand appears in ChatGPT, Gemini, or Perplexity. Query these platforms with the same questions your audience might ask. Monitoring performance helps you identify where your content is being cited and where you need to adjust. In the example below, you can see how a brand can be portrayed in AI tools based on different sources. We’ll cover later on how you can go about doing this.
Keep Content Fresh and Updated
Stale content gets overlooked. Updating old posts with new statistics, recent examples, or revised insights signals that your brand is current.
Practice Search Everywhere Optimization
LLMs draw from a variety of different sources, and this is where Search Everywhere Optimization comes in. LLMs pull from forums, video transcripts, and social media. The more places your brand shows up, the more likely it is to be discovered and cited by AI.
This is the essence of search everywhere optimization: making sure your expertise is visible wherever people (and AI models) go looking for answers.
Measuring LLM SEO Results
Measuring success in LLM SEO is not as straightforward as checking keyword rankings, but there are now tools and methods that make it possible.
Specialized platforms like Profound are built to track how often brands and websites appear in AI-generated answers across platforms. See below for a look at the Profound interface and how it helps showcase AI visibility.
Established SEO platforms, including Semrush, have also rolled out features that measure AI visibility alongside traditional search metrics. In the screenshot below, you can see how Semrush showcases AIO presence for a given page.
These tools give you a clearer picture of whether your content is surfacing where people are asking questions.
In addition to platforms, hands-on monitoring still matters. Query the models directly using the same questions your audience would ask. Document when your content is cited and watch for changes over time. This kind of manual testing tracks progress beyond what analytics alone can show.
You should also monitor referral traffic. Some AI tools now include links to sources, and those clicks show up in analytics as traffic. Another thing to keep an eye out for is brand mentions. Even if an AI result doesn’t give a link, brand mentions inside AI outputs are valuable, as they reinforce awareness and authority.
Finally, fold LLM SEO tracking into your broader SEO reporting. Look at engagement signals like time on page, repeat visits, and social shares for optimized content. If people find your content more useful, LLMs are more likely to treat it as a trusted source.
The bottom line is that measurement is evolving. You now have tools, data, and direct testing methods that show whether your LLM SEO efforts are paying off.
FAQs
What is LLM SEO?
LLM SEO is the process of optimizing content so large language models such as ChatGPT, Gemini, and Perplexity can understand, interpret, and surface it in their responses.
How is LLM SEO different from traditional SEO?
Traditional SEO focuses on ranking in search engine results. LLM SEO focuses on being cited inside AI-generated answers. Both rely on quality content, authority, and structure, but the measurement of success is different.
Is LLM SEO the same as LLMO?
No. LLM SEO is a subset of LLM optimization (LLMO). LLM SEO focuses on search-related visibility in LLM outputs, while LLMO covers the broader goal of increasing brand presence across all AI-generated answers.
How do you measure LLM SEO results?
Tracking visibility in LLMs involves querying the models directly, monitoring referral traffic from AI tools in places like GA4, and using platforms such as Profound or Semrush that offer AI visibility tracking.
Why does LLM SEO matter now?
Adoption of LLMs is growing rapidly. Users are increasingly asking questions on these platforms instead of traditional search engines. Brands that optimize early gain visibility where attention is shifting, while others risk losing ground.
Conclusion
Large language models are already changing how people search and discover brands. More users are asking questions in ChatGPT, Gemini, and Perplexity instead of clicking through a list of Google results. That shift is real, and it’s growing.
LLM SEO is how to meet that change head-on. The same fundamentals still matter: quality content, structure, and authority. But they need to be applied in ways LLMs can understand and reuse. That means writing conversationally, answering questions directly, and keeping your content current and credible.
This shift also fits into the bigger picture of search. The rise of zero-click searches shows how often users get the information they need without visiting a website at all. At the same time, semantic search highlights how engines and now LLMs look at meaning and context instead of just exact keywords.
If you want a practical first step, update one or two of your top-performing pages. Add FAQs, refresh the data, and shape answers around the questions your audience is actually asking. Then watch how often those pages begin showing up in both search engines and AI outputs.
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Product variations are more than just an ecommerce feature. They give your customers choices, whether it’s size, color, style, or material, while helping your store stand out in competitive search results. When optimized correctly, product variations do more than display available options. They improve the customer experience by making shopping easier. At the same time, they boost conversions by catering to diverse needs and support your SEO strategy by targeting more keywords.
This guide will explain the best practices for product variations and show you how to optimize them for search engines and customers so your ecommerce site can grow in traffic, rankings, and sales.
What are product variations in ecommerce?
Product variations or product variants are different versions of the same product designed to give customers options. These variations can be based on attributes like size, color, material, style, or capacity. Instead of creating multiple product listings, variations group all options under a single product, making it easier for customers to browse and purchase.
For example, when you search for an iPhone on Amazon, you’ll see options for different colors and storage capacities, all available on a single page. This setup lets customers explore multiple choices without leaving the main product page.
Example of product variants
Managing product variations depends on the platform you use:
In WooCommerce, product variations are created using attributes such as size or color, and then assigning values to those attributes. Store owners can upload unique images, set prices, and adjust stock for each variation
In Shopify, variations are managed under the ‘Variants’ section of a product. You can add options like size, color, or material, and then assign values. Each variant can have its own price, SKU, and image, making it simple to customize how the variations appear in your store
Okay, now let’s see why you need product variants and not upload each option as a completely separate product. Think of it this way: customers don’t want to scroll through endless listings just to compare a black t-shirt with a white one or a 64GB phone with a 128GB version. Variations keep everything in one place, making shopping smoother and more intuitive.
Here’s why product variations are so important for your customers:
Improved shopping experience: Variants reduce unnecessary clicks and allow customers to compare options side by side within a single product page. This saves time and makes decision-making easier
Higher conversions and lower bounce rates: When customers find their preferred size, color, or feature right away, they are more likely to complete a purchase instead of leaving your store
Reduced purchase anxiety: Variants ensure customers do not feel limited by stock. Seeing multiple choices available decreases the chance of cart abandonment
Personalization and satisfaction: Offering customers options empowers them to choose a product that feels tailor-made for them, improving overall satisfaction
Indirect SEO benefits: A better shopping experience often leads to longer session durations, fewer bounces, and more engagement. These signals may indirectly support stronger SEO performance, as they align with positive user experience metrics
How do product variations support your ecommerce SEO strategies?
Product variations are not just about creating a better shopping experience; they also bring direct ecommerce SEO benefits that can help your store attract more qualified traffic. When optimized correctly, variants can make your product pages richer, more discoverable, and more engaging.
Increase in keyword targeting
Variants allow you to target a wider range of long-tail keywords that reflect real customer search behavior. For example, instead of only competing for ‘men’s wallet,’ you can rank for ‘men’s black leather wallet’ or ‘slim men’s brown wallet.’ These specific keywords usually carry higher purchase intent and face less competition
Levi’s product page for jeans uses long-tail keywords in the product description for keyword targeting
Richer content for search engines and AI engines
Each variation allows you to add unique attributes, descriptions, and specifications. This creates a more detailed and content-rich product page that search engines and AI-driven engines (like ChatGPT or Google’s AI Overviews) value when surfacing answers and shaping brand perception.
ChatGPT showing product options for a t-shirt
Improved user engagement and longer sessions
A well-structured page that clearly displays variations keeps users from bouncing to competitor sites when they don’t immediately find their preferred option. Instead, they spend more time exploring, comparing, and interacting with your store, which indirectly supports SEO through stronger engagement signals.
Better structured data for enhanced search results
When product variants are properly marked up with structured data, search engines can display rich snippets that include price ranges, availability, color options, and reviews. This not only makes your listings stand out but also boosts click-through rates (CTRs) from search results.
Yoast SEO’s Structure data feature describes your product content as a single interconnected schema graph that search engines can easily understand. This helps them interpret your product variations more accurately and increases your chances of getting rich results, from product details to FAQs.
In short, optimized product variants make your product pages more keyword-diverse, content-rich, and engaging while also improving how your store is presented in search results and generative AI chat replies.
Blueprint for optimizing your product variations
Here’s the part you’ve been waiting for: how to optimize your product variations for SEO, conversions, and user experience. In this section, we’ll cover the right technical implementation, smart SEO tactics, and the common mistakes you’ll want to avoid.
Technical implementation of product variations
Getting the technical setup right is the foundation for optimizing your product variations for both ecommerce SEO and user experience. Poor implementation can lead to crawl inefficiencies, duplicate content, and a confusing buyer journey.
Here’s how to approach it effectively:
Handling variations in URLs
One of the biggest decisions you’ll make is how to structure URLs for your product variations:
Parameters (e.g., ?color=red&size=12): Good for filtering and faceted navigation, but they can create crawl bloat if not managed properly. Always define URL parameters in Google Search Console and use canonical tags to consolidate signals
Separate pages for each variation (e.g., /red-dress-size-12): This can be useful when specific variations have significant search demand (like ‘iPhone 15 Pro Max 512GB Blue’). However, it requires careful duplication management and unique, optimized content for each page
Single product page with dropdowns or swatches: The most common approach for ecommerce stores, as it consolidates SEO signals into one canonical page while providing users with all available variations in one place
Takeaway: Use a hybrid approach. Keep a single master product page, but only create dedicated variation URLs for high-demand search queries (with unique descriptions, images, and structured data).
Note: only create dedicated variation URLs if you can add unique value (content/images), otherwise, it risks duplication
Internal linking best practices
Internal linking is crucial in helping search engines understand the relationships between your main product page and its variations.
Always link back to the parent product page from any variation-specific pages
Ensure your category pages link to the main product page, not every single variation (to prevent diluting crawl equity)
Use descriptive anchor text when linking internally, e.g., ‘men’s black leather wallet’ rather than just ‘wallet’
The Internal linking suggestions feature in Yoast SEO Premium is a real time-saver. As you write, it recommends relevant pages and posts so you can easily connect variations, parent products, and related content. This not only strengthens your site structure and boosts SEO but also ensures visitors enjoy a seamless browsing experience.
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Takeaway: Build a clean hierarchy where category pages → main product pages → variations, ensuring both users and crawlers can navigate easily.
Managing faceted navigation and filters
Filters (like size, color, brand, or price) enhance user experience but can create SEO challenges if every filter combination generates a new crawlable URL.
Use <nofollow or noindex for low-value filter pages (like ‘price under $20’ if it doesn’t add SEO value)
Block irrelevant filter parameters in robots.txt to prevent crawl bloat
For valuable filters (e.g., ‘red running shoes’), allow them to be indexed and optimize the content
Takeaway: Conduct a filter audit in Google Search Console. Identify which filtered URLs actually drive impressions and clicks, and only allow those to be indexable.
Media content optimization for ecommerce product variations
When it comes to product variations, visuals and supporting media play a critical role in both SEO and conversions. Shoppers often make purchase decisions based on how well they can visualize a specific variation. In fact, 75% of online shoppers rely on product images when making purchasing decisions.
Here’s how you can optimize media content for ecommerce product variations:
Use unique images for each variation
Avoid using the same generic image across all variations. Display each color, size, material, or feature with its own high-quality image set. For example, if you sell a t-shirt in six colors, show each color separately to help customers make confident choices.
Unique product images for each variant
Leverage 360° views and videos
Showcase variations with interactive media like 360° spins or short product videos. For example, a ‘black leather recliner’ video demonstrates texture and function more effectively than a static image, leading to higher engagement and conversions.
Use videos and 360-degree media to portray your products
Optimize alt text, file names, and metadata
Every image should have descriptive, keyword-rich alt text that specifies the variation. Instead of writing ‘red shoe,’ use ‘women’s red running shoe size 8.’ File names (e.g., womens-red-running-shoe-size8.webp) and captions should also reinforce the variation for better indexing.
Implement structured data for media
Use the Product schema to explicitly define images and videos for each variation. Including structured data ensures that Google and AI-driven engines like ChatGPT can clearly interpret your variation visuals and display them in rich results or AI summaries.
For instance, assigning images to specific SKUs (via image markup) makes it easier for search engines to show the correct variation in shopping results.
SEO tips for product variations
Optimizing product variations for SEO requires more than attractive visuals and solid descriptions. You need to apply some proven SEO techniques to ensure search engines correctly interpret your product pages and users get the best possible experience.
Here are a few key practices every ecommerce store owner should follow:
Use canonical tags to avoid duplicate content issues
Product variations often generate multiple URLs, which can cause duplicate content problems. Canonical tags help solve this by pointing to the primary version of a page, consolidating ranking signals, and avoiding internal competition.
Yoast simplifies this process by automatically inserting canonical URL tags on your product pages. This ensures search engines know which version to prioritize, prevents diluted link equity, and even consolidates social shares under the original page. For store owners, this means less technical overhead and stronger, cleaner rankings.
Apply global product identifiers (GTIN, MPN, ISBN) where relevant
Global product identifiers like GTINs, MPNs, and ISBNs act as unique fingerprints for your products. They help Google and other search engines correctly match your items in their vast index, which improves the accuracy of search listings and reduces confusion with similar products. They also add credibility, since customers can cross-check these identifiers before purchase.
With Yoast WooCommerce SEO, adding these identifiers becomes much easier. The plugin reminds you to fill in missing SKUs, GTINs, or EANs for each product variation and automatically outputs them in structured data. This not only helps your products qualify for rich results but also ensures that no variant is left incomplete from an SEO standpoint.
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Regularly audit Google Search Console data to track performance
Google Search Console is a goldmine for understanding how product variations are performing. By monitoring which variant pages are driving impressions, clicks, and conversions, you can refine your SEO strategy.
For example, if certain variants attract little traffic but consume crawl budget, it might be better to consolidate them under canonical tags.
Regular audits also help you detect indexing issues, thin content problems, or underperforming structured data. This keeps your product catalogue lean, crawl-efficient, and focused on driving meaningful organic traffic.
Common product variation ecommerce errors to avoid
Even if you’ve implemented the right technical setup, added structured data, and optimized your media content, a few small mistakes can undo all that effort. To make sure your product variations support SEO and conversions instead of hurting them, here are some common pitfalls to avoid:
Duplicate content: Creating separate standalone pages for each variation (like size or color) without consolidation leads to content duplication. This confuses search engines and dilutes rankings across multiple weak pages
Poor user experience: If your variation options are hidden, unclear, or slow to load, users struggle to make choices. This friction reduces conversions and increases bounce rates
Incorrect structured data: Applying schema inaccurately can cause search engines to display the wrong product details in search results, damaging credibility and visibility
Thin content: Not providing unique descriptions, images, or metadata for each variation leaves the page with little value. Search engines tend to down-rank such content, reducing discoverability
Crawl bloat: Generating too many low-value variation URLs (like separate pages for every minor option) wastes crawl budget and prevents high-priority pages from being indexed efficiently. Additionally, it could dilute internal link equity
By keeping these errors in check, you’ll ensure your product variation strategy strengthens your SEO and user experience instead of working against them.
Ready to unfold all variations?
Product variations are not just small details hidden in your catalogue. They play a major role in how both search engines and shoppers experience your store. When done right, they prevent duplicate content issues, improve crawl efficiency, deliver richer search results, and create a seamless journey for your customers.
The key is to treat product variations as part of your overall SEO strategy, not as an afterthought. Every unique image, structured snippet, and clear variation option makes your store more visible, more reliable, and more profitable.
This is where Yoast SEO becomes a game-changer. With automatic structured data, smart handling of canonical URLs, and advanced content optimization tools, Yoast helps you get product variations right the first time.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-09-04 17:05:512025-09-04 17:05:51A detailed guide to optimizing ecommerce product variations for SEO and conversions
“Good SEO is good GEO.” That’s according to Google’s Danny Sullivan, a director within Google Search, and former search liaison
Generative engine optimization (or whatever the new acronym is for optimizing for AI search experiences) is the same core work SEOs have always done: creating unique, valuable content for people and providing a great page experience, he said.
Why we care. You can believe Google if you want. But we’ve tried to consistently say that we believe GEO is an emerging practice. That doesn’t mean it replaces SEO today or tomorrow – because SEO fundamentals matter and SEO is still not dead. But I also agree with Michael King’s assessment that SEO is deprecated. The future of Google and conversational AI search will be answers, not ranking, regardless of what Googlers say publicly today.
What he’s saying. Here’s some of what Sullivan said about SEO/GEO during his keynote at WordCamp US on Aug. 28:
“…If you don’t know what GEO is, it’s like the latest acronym, but like I can’t keep track each day. There’s a different one. But SEO, search engine optimization; GEO, generative engine optimization.
By the way, if you could dig it out when I was like in 2010, back when people were panicking then, I was like, you know, SEO doesn’t mean you get into the blue links on Google. SEO means you understand how people search for content and then you understand how to have your content there. And it could be everything from people asking a question to a voice device to people just opening up something on their phone or whatever.
So, the basic things have not changed. Good SEO is good GEO, or AEO, AIO, LLM SEO, or LMNOPO. So, they’re all fine. What I’m trying to say is don’t panic. What you’ve been doing for search engines generally, and you may have thought of as SEO, is still perfectly fine and is still the things that you should be doing. … Good SEO is really having good content for people.
… Are you saying write things in a clear way that people can understand? Cool. Like that’s just for people. All right.
Are you saying write about things that are unique or interesting? Cool. That’s good for people. And all we [Google] try to do is understand how our signals can align with things that are good for people.”
CTR question. During the audience Q&A, blogger Angie Drake said her organic search click-through rate has plummeted since AI Overviews launched, even though impressions are up (known as the great decouoling of search). She asked Sullivan what Google will do to compensate publishers who are losing clicks. Sullivan’s response:
Google has been unapologetic about zero-click factual answers (e.g., “What time is the Super Bowl?”) because users expect direct facts.
Google is committed to rewarding unique, valuable content and supporting the open web.
He said there will be “bumps along the way,” that feedback is heard within Google, and “it’s still part of what we’re going to be figuring out.”
Other takeaways. Some other data Sullivan shared:
Google AI Overviews have led to a 10% increase in searches in the U.S. and India.
Google does “up to 5,000 launches” (a.k.a., updates) per year. The last figure we had was 4,725, so not much has changed since 2022.
The keynote. Here is the full video. I’ve linked to the takeaways portion of Sullivan’s presentation, where he discusses GEO. Drake asks her CTR question starting at 45:06.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/09/zf_sxldftby-KkLY6x.jpg?fit=1280%2C720&ssl=17201280http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-09-02 14:59:012025-09-02 14:59:01Google’s Danny Sullivan: ‘Good SEO is good GEO’
