“What’s the best water bottle for hiking in hot weather?”
Once upon a time, this was a question you’d ask a friend or perhaps even a search engine. These days, more users are asking ChatGPT or Bing for product recommendations instead. But instead of pointing them to blog posts or product roundups, these services routinely respond with a few top-rated insulated bottles. They list brands and explain why each one works well in high heat. Sometimes, they’ll even pull a pros and cons list based on verified user reviews.
No link-hopping. No searching. Just answers.
That’s what AI shopping assistants like Rufus and ChatGPT do. They summarize, rank, and serve up product recommendations in the conversation. Amazon’s Rufus does this natively, using real-time catalog data to recommend listings directly from your product description pages (PDPs).
AI shopping assistants like ChatGPT and Rufus now recommend products directly in search results.
Product discovery is changing. If your Amazon PDP doesn’t highlight real-world benefits, you won’t get surfaced.
These tools favor clarity, structure, and reviews over keyword stuffing. Useful beats optimized.
You’re not helping an algorithm, but a customer. AI just makes sure the best answers rise to the top.
Smart sellers adjust PDPs to stay visible and competitive in AI-driven shopping environments.
How AI Shopping Assistants are Changing Search Results
Instead of a list of links, more users seek (and find) direct answers to questions via LLMs like ChatGPT, including product picks.
Ask ChatGPT, Rufus, or other tools something like “best standing desks for small spaces,” and you’ll get a curated list of products, often pulled from Amazon, with detailed descriptions and benefits or drawbacks. Amazon’s Rufus tool does this within its app, serving product recs within the search flow.
Rufus has transitioned from a sidebar feature to a front door to product discovery and another element of the “Search Everywhere” mindset.
That shift matters, both to consumers and brands. When AI shopping assistants serve results, they no longer pull the most optimized pages by default. They interpret context and match buyer intent. The goal? To highlight products that seem most useful, not necessarily the ones with the best keyword density.
In an AI shopping assistant search experience, the assistant is the curator. It summarizes reviews, analyzes product detail descriptions, and ranks options for each individual user based on usefulness, not metadata.
Why Amazon Sellers Should Care
If your products don’t show up in these overviews, guess whose will?
AI shopping assistants are already showcasing products from Amazon in their answers. If your PDP isn’t optimized for this new experience, you’ll be left out in the cold.
When someone asks Rufus or ChatGPT for “the best travel backpack under $100,” the assistant pulls in a few options and adds summaries, ratings, and product highlights. Only a handful of options make the cut.
The prompt for this question was incredibly bare bones. With more detail, ChatGPT could likely source even more relevant products.
Amazon sellers need to rethink their AI shopping strategy. Visibility no longer comes from ranking in traditional search results. Instead, you must be the product that AI shopping assistants name, summarize, and recommend in real time.
Sellers who adapt fast will capture market share without increasing ad spend. Those who stick to outdated PDP structures will watch their competitors gain visibility while their own products get overlooked, even if traditional rankings appear stable.
Here’s what matters most: once AI shopping assistants start to prefer well-structured, benefit-forward listings, there’s no going back. You’re either in the product rec loop, or you’re not.
How AI Shopping Assistants Choose Products
AI shopping assistants represent a major shift from keyword-matching to intent-matching. Unlike traditional search algorithms that reward optimization tactics, AI models prioritize real utility and customer satisfaction, aligning perfectly with long-term business success.
Clarity in Product Benefits
AI models scan for product pages that clearly explain what the item does for the shopper. If your listing highlights “lightweight design for all-day wear” or a “fast-charging battery that lasts 12 hours,” that’s gold. Generic feature dumps or spec lists? Not so much.
Structured Data
Structured product information helps AI understand your listing faster. Bullet points that summarize key specs, consistent formatting, and well-labeled fields give the model more to work with and improve your chances of getting recommended.
Positive Reviews and Social Proof
AI shopping assistants pull in review content when it’s available. They reference common customer praise, star ratings, and repeat feedback trends. If 50 people said your jacket runs true to size and holds up in the rain, it could show up in a response. Even the staple product recommendation or enthusiast websites like Tom’s Guide or Wirecutter occasionally pop up as character witnesses for products.
ChatGPT sources details from enthusiast websites and third-party reviewers to help inform its recommendations.
High Relevance to the Query
AI assistants are great at matching intent. If someone asks for a quiet blender for small apartments, the model will prioritize listings that mention noise level, size, and kitchen fit.
So what’s the takeaway? Keyword-stuffing is a thing of the past. You need real clarity, quality, and signals to tell the AI: This is the one!
Practical Steps to Optimize Your Amazon PDPs
You’re not optimizing for AI. You’re optimizing for the shopper. AI shopping assistants are just the bridge. They pull in products to speak clearly about what customers are asking for.
If Rufus and ChatGPT surface your listings, your PDP answered the question better than anyone else. The goal is not to “trick” the model but to make it impossible to ignore your product.
Here’s how to do that:
Step 1: Clearly Highlight Real-Life Benefits
Most PDPs talk about what a product is. AI shopping assistants (and your customers) want to know what it does. Compare these two potential listings:
“Made from high-density foam, measures 24×18 inches”
“High-density foam cushions sore joints, which is perfect for long yoga sessions.”
It’s a tiny shift that puts the benefits front and center, exactly the kind of language tools like Rufus pick up on.
Take a minute to browse through actual Rufus prompts. People don’t search for “12 oz stainless steel tumblers.” They look for “cups that keep drinks cold all day,” or “easy-to-clean travel mugs for kids.” Build your PDPs around those use cases.
Speak your customers’ language. The AI will reward it.
Step 2: Prioritize Structured Data and Clear Formatting
AI shopping assistants scan for structure. They need clear data to parse and present your listing as a credible recommendation. Here’s what helps:
Bullet points that break down features and benefits
Consistent formatting across titles, descriptions, and variations
Upfront pricing and availability info
Alt text and backend keywords that reinforce clarity, not clutter
Tools like Rufus can only do their job well if the data they pull from is organized. Schema markup and enhanced brand content (EBC) help, too, but even basic formatting upgrades make a difference.
Don’t bury your benefits in a wall of text. Make them easy to find for both the shopper and the assistant.
Step 3: Strengthen Reviews & Social Proof
AI shopping assistants factor in review volume, sentiment, and consistency when they decide which products to serve. If a listing has clear themes, like “easy to assemble” or “great for travel,” those signals will get picked up.
If you want more of those, start by:
Following up on every purchase with a review request (Amazon’s “Request a Review” tool helps).
Using inserts that ask for feedback in a natural, non-pushy way.
Resolving customer issues quickly to avoid negative reviews.
Finally, surface your strongest reviews and feature them in your A+ content or EBC modules. AI models will likely mention what’s already being repeated and reinforced across the listing.
Brands investing in genuine customer experience will see compound returns as AI adoption accelerates. Those relying on optimization tricks face declining visibility.
Building an AI Visibility Intelligence System
AI shopping assistants update their recommendations continuously. Smart sellers build systematic monitoring to catch shifts before their competitors.
Here’s a sample plan for how to stay ahead:
Week 1: Establish a Baseline
Test 10 customer-style queries for your top products in ChatGPT, Rufus, or other LLMs. Document which products appear and their positioning. Track key metrics like keyword rankings or listing traffic using tools like Helium 10 or Jungle Scout.
Jungle Scout’s Keyword Intelligence tool helps provide visibility for tracked keywords.
Weeks 2-3: Implement Quick Wins
Rewrite product titles and bullet points for your three worst-performing listings. Add structured data where it’s missing and improve your formatting consistency. A/B test benefit-focused language versus feature-focused. Launch a review generation campaign for products with fewer than 50 reviews.
Week 4: Measure Initial Impact
Re-test your original 10 queries and note position changes. Compare traffic and conversion metrics to your week 1 baseline. Identify changes that moved the needle most and use those insights to create an optimization playbook for all products moving forward.
Ongoing Monitoring (Monthly)
Monitor what AI tools recommend when customers ask about your product category and track how customers phrase questions. You can use Rufus search suggestions or ChatGPT conversation starters for this. Finally, connect AI visibility changes to traffic and sales data.
You can also set up Google Alerts for your brand + “best [product category]” to catch when Google’s AI Overviews mention you in public responses.
FAQs
How do AI shopping assistants like ChatGPT select products?
ChatGPT and tools like it do more than “search.” They curate. They pull in product data, reviews, specs, and user feedback to recommend items that match what shoppers ask for. There’s a new wrinkle, too: OpenAI is testing affiliate partnerships, where they’ll earn a cut from recommended products that convert. This incentivizes them to surface products that lead to sales, not just clicks.
What changes should I make first on my Amazon product pages?
Begin with clarity. Rewrite bullets and descriptions to focus on real-world benefits, like what the product does and the problems it solves. Use clean and easily scanned formatting. Finally, check your reviews. If customers call out key benefits, surface those in your listing.
Are keywords still important with AI shopping assistants?
Yes, but not in the old way. Keywords help AI understand context, but keyword stuffing won’t help. Instead, use natural phrasing that matches how customers ask questions. Phrase your content around problems and outcomes, not just specs.
Conclusion
The shift is accelerating: AI shopping assistants are rapidly becoming a main channel for product discovery. Major platforms like Amazon, Google, and Microsoft have already invested billions in AI-powered commerce experiences. Early movers are capturing market share and leaving their competitors in the dust.
The bottom line? Sellers who optimize for AI shopping now will own the conversation when their customers ask for recommendations. Those who wait will find themselves explaining why they’re not worth mentioning.
The best strategy to improve your Amazon listings is to track your progress and take actionable steps to improve. If you haven’t seen improvement within 60 days, you’re likely leaving money on the table.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-09-10 19:00:002025-09-10 19:00:00Preparing For The Rise of AI Shopping Assistants In Search
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).
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
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.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-09-05 19:00:002025-09-05 19:00:00Getting Cited in LLMs: A Guide to LLM Seeding
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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“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.
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In the world of large language models (LLMs) in 2025, that’s a complicated question.
This article breaks down why by covering:
How LLMs like OpenAI and Gemini currently use search engines.
What search marketers should assume about where AI is heading.
The types of executional work that align with GEO.
What all of this means for prioritization and investment.
How LLMs stay current: Grounding and RAG
One of the fundamental challenges for the creators of LLMs (LLMs) like OpenAI or Claude is timeliness.
Their training data is static, locked to a specific cutoff date.
For example, the GPT-5 model’s knowledge cutoff is Sept. 30, 2024.
It’s more recent than GPT-4o’s cutoff of Oct. 1, 2023, but still not up to the present day.
Updating that training data is extremely costly, and it’s increasingly under public scrutiny – both for the resource-intensive nature of the process and the potential copyright issues it raises.
In my view, these large-scale training updates are becoming less and less likely over time.
So how do OpenAI, Claude, or Gemini keep their answers current?
They use retrieval-augmented generation (RAG), where the model enriches its responses by effectively “browsing” the web. ChatGPT relies on Bing, while Gemini draws from Google.
(There are signs Gemini doesn’t always use live results, but rather cached ones – that’s a whole other article, and one Dan Petrovic has already written smartly about.)
Grounding is a similar concept here, so for this article, we’ll treat it as the same “timely” method, even though there are important nuances in implementation.
What does this mean for SEOs and digital marketers deciding how much to invest in GEO?
Quite simply: we still need to prioritize traditional SEO first. RAG is a limited resource, and research shows:
Nearly 90% of ChatGPT search citations matched Bing’s Top 20 results.
It’s also important to note: when ChatGPT cites your brand, it doesn’t just pull from your website. It pulls from sources across the web.
The bottom line: you still need to master traditional SEO fundamentals to rank in LLM-driven search.
If you don’t have the authority to break into the Top 20 results, plus a diversified outreach strategy for press mentions and brand visibility, it will be much harder to surface in generative search.
As a low-risk, forward-looking, brand-focused SEO, you must plan for a future where generative search dominates, driving most traffic and revenue.
At that point, we must assume our websites and digital properties function primarily as enriched data feeds for LLMs.
It’s also critical to clearly define our brand for both Google and Bing, as strong, unambiguous entity signals will only grow in importance.
Optimizing your data infrastructure and strengthening brand signals – through consistent press mentions, directory listings, and owned media – are essential but resource-intensive tasks.
They demand coordination across departments that rarely collaborate and often require dismantling entrenched processes.
Because many businesses hesitate to make these foundational changes, you’ll need to account for the time required to execute the work and the time required to gain stakeholder alignment.
The work required to make your website as LLM-friendly as possible falls into two main buckets: technical and brand.
Technical tasks
Implementing thorough schema markup
This is a contentious topic.
LLMs don’t directly use schema markup in their training data (it’s stripped), and in their RAG process, everything is tokenized and likely broken into n-grams.
I’m not suggesting schema markup is a direct way to influence visibility in LLMs.
It’s a vehicle for helping Google and Bing understand:
Your website.
Its relationships.
Its products.
This builds your brand and search engines’ recognition of it, which should improve your visibility in results.
Technical copywriting
On navigational pages – like product collection pages or company listing pages if you’re a marketplace – create technical copy (done via AI with smart prompting if you’re working at scale) that summarizes the available resources.
For example:
“Our stationery includes 5 A5 dotted journals, 2 N1 blank journals, 25 stickers featuring animals, 4 stickers with curse words (all vinyl for weatherproofing and waterproofing), and 1 lapel pin.”
Notice how direct and technical this is. The clear formatting ties back to dependency hops in natural language processing.
I’m calling it out specifically because it’s one of the most direct ways for search engines and other bots to see and navigate the full scope of your website.
JavaScript fallbacks
This has always been important but has fallen by the wayside in recent years.
Training data for LLMs is static HTML. For the most part, they don’t render JavaScript.
Make sure to have functional JavaScript fallbacks.
Address technical debt
This will depend on your organization. It could mean:
Having a clear product sunsetting process.
Updating the codebase.
Removing the ghost codebase still sitting on your site from eight years ago that everyone built on top of rather than deleting.
Migrating from an SPA to a more search-friendly framework.
Removing deprecated scripts.
Auditing third-party tags to ensure they’re up to date and still in use.
All of these impact performance.
The technical strength and response time of your website will only grow more important.
Every piece of tech debt is an opportunity to improve.
If there’s only one takeaway, it’s this: keep investing the majority of your time and budget in traditional SEO, while dedicating a smaller portion to technical and brand tasks like those outlined above.
Look closely at the 1-5% improvements you’ve been putting off – things like:
Correcting the HTML heading hierarchy to match the site’s visual hierarchy.
Fixing internal links so they point directly to final URLs instead of redirect chains.
Cleaning up your XML sitemap.
Removing deprecated libraries and unused WOFF files.
This “spring cleaning” and tech debt cleanup should be a priority.
Add in the brand work as well, since it strengthens traditional search today and also lays the groundwork for an LLM-led search future.
If you don’t already have regular reporting in place for stakeholders and leadership, create it now.
There’s a perception that large language models are evolving rapidly and changing everything at once.
That isn’t entirely true – but we do need to plan.
Establishing a cadence of reporting and education means that when real shifts do happen, your stakeholders will already be aligned and ready to support the work.
Finally, treat GEO/AI optimization as roughly 20% work.
This means building systemic schema layers across your organization and creating structured connections in the machine’s native language – code.
Start with:
Conversations.
Proofs of concept.
Pilot implementations.
Done properly, this work should have no negative impact on your business metrics, and it builds support for more holistic optimization over time.
Going all in on LLM-specific tactics isn’t the best use of your resources today.
Instead, treat it as complementary work – something that strengthens your technical and brand foundation while preparing you for a future where generative search plays a central role.
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It’s fragmented across search engines, social platforms, paid ads, and AI tools, creating a complex user journey that’s harder than ever to track.
As behavior shifts with these new technologies, search marketing is evolving in response.
Yet while the platforms, tools, and touchpoints keep changing, the core principles of effective marketing remain the same.
Marketers who stay grounded in these fundamentals will be best equipped to adapt and grow.
Here are nine timeless marketing principles that will hold steady – no matter how search evolves.
1. Focusing on search intent: Why people search
Where people search and find information will continue to change over time as preferences for LLMs, social media, or video content shape where people go for answers.
Focus on the why behind a search – the intent driving it.
The key question is whether your content aligns with that intent.
If it doesn’t, you’re overlooking a critical driver of user behavior.
When content matches search intent, users immediately recognize its value and engage, which is why intent should remain central to your marketing strategy.
2. The lasting value of brand recognition and loyalty
Even as AI continues to drive change in how companies reach their audiences, brand recognition and loyalty remain important pillars of long-term engagement and growth.
Discovery channels are shifting as people find brands through social media, search engines, paid ads, email, and more.
That’s why it’s important to continually reassess the customer journey and understand where your audience is finding you.
After discovery, your job is to highlight your unique value – what sets you apart from competitors and how you provide real value to your audience.
Keep asking yourself:
Why should someone choose my brand?
What makes us stand out?
The clearer and more consistently you communicate this in the spaces that matter, the more you’ll earn trust, recognition, and reliability – all of which shape how people respond to your brand.
Brand loyalty isn’t automatic. It’s something you earn by building real relationships with your customers and consistently providing value.
Loyalty creates long-term stability and growth, even as platforms and algorithms continue to shift.
While search intent and brand recognition can attract new visitors, loyalty turns impressions into conversions and builds lasting customer lifetime value.
3. Knowing and understanding your audience
Beyond search intent and branding, truly knowing your audience is essential for long-term marketing success.
Without that insight, you risk falling into “spray and pray” campaigns that waste resources and fail to connect.
Building clear audience personas helps you decide not just what campaigns and content to create, but how to present them in ways that resonate.
That means understanding who your audience is, what motivates them, their pain points, their values, and where they spend their time.
These insights form the foundation of a strategy built to genuinely connect with your audience.
5. Customer service and experience drive perception
Today, customer service is inseparable from brand experience.
Every interaction – whether answering a support ticket or replying to a social media comment – shapes how people perceive your credibility and value.
Testimonials and reviews create a powerful feedback loop: one story sparks another, influencing how others view your brand.
Campaigns can drive visibility, but audiences still turn to peer reviews on platforms like Reddit to validate those impressions and decide whether to trust you.
Audience sentiment has become its own form of publicity.
With user-generated content (UGC) shaping perception and AI systems relying on reviews and sentiment signals to recommend brands, customer experience is now a direct driver of both reputation and visibility.
A core principle that hasn’t changed is the need for an optimized user experience.
When someone lands on your site, the page should minimize friction in the buying journey.
Whether visitors arrive through ads or organic search, they need clear conversion paths that guide them smoothly forward.
Audiences expect ease and clarity when looking for information or taking action.
Slow load times, unnecessary clicks, or confusing layouts increase drop-offs, abandoned forms, and carts – leaving users frustrated.
A good user experience makes the journey to conversion as effortless as possible.
Done well, it not only boosts conversions but also builds satisfaction and trust.
7. Mobile-first experiences: Meeting users where they are
AI may be transforming how people search, but mobile devices remain the primary way users access and engage with brands.
For many, the first interaction with your brand happens on a phone.
That’s why user experience must extend beyond conversion paths.
It also has to be fully optimized for mobile. Otherwise, you risk frustration, lost trust, and missed conversions.
Mobile users abandon sites that load slowly, require pinching and zooming, use hard-to-tap buttons, or rely on clunky forms.
Even a few seconds of delay or disruptive layout shifts can cause drop-offs.
And because search engines prioritize mobile-friendliness, optimizing for mobile isn’t just about usability. It also directly impacts rankings and visibility.
8. Accessibility is essential
Accessibility is a core part of creating inclusive experiences for your entire audience.
In the U.S., it’s also a legal responsibility.
Making your site accessible means adding features like:
Screen reader compatibility.
Alt text for images.
Strong color contrast.
Keyboard navigation.
If accessibility is overlooked, you risk excluding parts of your audience and facing ADA lawsuits.
But when you design with accessibility in mind, you reach more people, strengthen trust, and ensure everyone can engage with your brand.
9. Quality content and authority still define success
No matter how search evolves, quality content and authority remain the foundation of visibility and trust.
Algorithms may shift and discovery channels may change, but users will always value content that is accurate, relevant, and genuinely helpful.
Take a few minutes to think about your website. Have you noticed your traffic dropping even though rankings haven’t really changed?
You’re not alone.
The rise of zero-click searches on Google and other search engines is upending what we consider SEO success and changing the game. AI Overviews, featured snippets, answer boxes…these give users what they need without clicking through to your website. And for the most part, users have been somewhat satisfied. Almost 44 percent of marketers have seen decreased web traffic since AIOs launched, while 48 percent have seen revenue boosts from ads and affiliate links.
So, how do you stay relevant when Google keeps more traffic for itself? That’s what we’ve been trying to figure out for a while now, and it’s what we’ll share with you here. We’ll break down the zero-click future and give you real, actionable ways to grow your visibility and prove your value to build a thriving brand, even when clicks are scarce.
Key Takeaways
Zero-click searches are reshaping SEO success metrics. Traditional traffic-focused strategies need updating as Google and AI tools answer queries directly in search results, reducing site visits even when rankings remain stable.
Multi-platform visibility beats single-channel dependence. Success requires optimizing for AI citations, featured snippets, and expanding presence across TikTok, YouTube, Reddit, and other search destinations where your audience seeks answers.
Authority and original content drive AI citations. Brands that invest in proprietary research, expert commentary, and structured data are more likely to be quoted by AI tools and featured in zero-click results.
First-party data becomes your competitive advantage. Building direct relationships through email lists, CDPs, and owned media channels protects against algorithm changes and platform dependency.
New success metrics matter more than clicks. Track impressions, brand mentions, AI visibility, and social engagement rather than relying solely on last-click attribution to measure zero-click performance.
What Are Zero-Click Searches?
A zero-click search gives users the answer directly in the search results. Featured snippets, AI Overviews, local packs, and “People Also Ask” boxes are all examples of zero-click search results.
These features are (mostly) great for users because they meet their needs immediately. That improves user satisfaction. Marketers can benefit, too; a zero-click result has the upside of brand visibility in prime real estate. The downside is fewer site visits and opportunities to convert visitors.
If you’re a marketer, understanding this shift is critical. Knowing how zero-click search features work can help you shape your content for inclusion and maintain your relevance, even if traffic declines.
Why Zero-Click Is Taking Over
Platforms like Google, Bing, and AI-driven tools want to keep users within their ecosystem. By providing instant answers, they reduce the need for users to click through. Social media platforms have also become search destinations; TikTok, Instagram, and YouTube answer queries in-app.
Why are these companies doing this? To serve ads, mainly. Meta and Google can continuously serve you ads based on your search history and behavior by keeping you on their platforms. The longer you’re there, the better the chance that you’ll click an ad and give them revenue.
The downside of the trend is that it pushes brands to compete for attention across multiple discovery channels. You can no longer rely on just paid search or earned media alone. Adapting to this new reality isn’t optional, either. You have to understand where your audience searches and tailor content for those environments.
The Cost of Ignoring Zero-Click
Ignoring zero-click can quietly erode your digital presence until the impacts become impossible to reverse. The most obvious loss is website traffic, but there are other consequences:
Reduced brand visibility: When your content fails to appear in AI overviews, knowledge panels, or other SERP features, someone has to fill that space: your competitors. That can shift user perception and recognition, leaving you wondering where everyone went.
Lower engagement throughout the funnel: Without TOFU (top of funnel) visibility, your middle- and bottom-of-funnel efforts can struggle. Fewer people enter your ecosystem, which makes it harder to build relationships or drive conversions.
Weakened authority signals: AI models and search algorithms favor content that’s already been featured or cited. You risk being left out of future citations if you’re not part of that pool. That can start a spiral that reduces your credibility in the eyes of both machines and users.
Missed data and audience insights: When users find answers elsewhere, you lose the behavioral data from on-site engagement. That limits your ability to refine messaging, test offers, and personalize experiences.
Potential revenue decline: Reduced visibility and engagement inevitably lead to fewer leads, sales, or ad impressions. The financial impact compounds over time.
Failure to adapt to zero-click realities means you give up control over how and where your brand appears in the search experience.
How to Actually Win in a Zero-Click World
We know you should ignore zero-click searches at your own peril. But how do you actually win in this environment? You can succeed by shifting focus from chasing clicks to ownership of the answers that matter to your audience.
Optimize For AI & Snippets
Marketers benefit from higher visibility, and users benefit from faster, clearer answers. Structured content makes it easier for AI and search engines to feature you.
For example, a travel website creates a “Top 10 Things to Do in Milwaukee, WI” guide with schema markup for attractions, ensuring Google can pull quick answers for users who ask for “things to do in Milwaukee.” That gives the user an instant list while showing your brand as a trusted source. In practice, that looks like:
Applying schema markup for FAQs, how-tos, and reviews.
Creating content hubs with strong internal linking.
Adding concise summaries to the start of articles.
Using descriptive headers for each section.
Be Worth Quoting
AI summaries and featured snippets favor credible, unique content that adds value. Marketers gain authority while users get richer information they can trust.
Let’s say a leading SaaS company publishes a report with proprietary industry data. AI pulls statistics from the report to answer users’ questions, associating your brand with expertise. To get started, consider:
Conducting original research and sharing the results.
Adding expert commentary from internal or external subject matter experts.
Including case studies with measurable results.
Using side-by-side comparisons to simplify decision-making.
Double Down On Brand Authority
Being a recognized authority helps you get cited by AI tools and SERPs. Marketers benefit from constant exposure, and users gain confidence in your answers. Pitch newsworthy stories to journalists at reputable top-tier or hyper-relevant industry publications to reap the best benefits. If your brand strategy isn’t taking advantage of considerable outreach, you’re leaving money (and recognition) on the table.
For example, a health clinic might contribute expert articles to high-profile medical sites. As AI tools look for health-related information, your clinic’s name is seen as a trusted source. But how do you act on this? Take steps to:
Build digital PR efforts to secure mentions on authoritative websites.
Get Wikipedia references where appropriate.
Encourage positive user reviews.
Earn high-quality backlinks.
Maintain consistent branding across all content.
Create Click-Worthy Content
Even in a zero-click environment, some users want more detail. Marketers benefit by attracting those motivated visitors, while users gain access to in-depth resources. The trick? Thinking outside the traditional “blog” mindset. Imagine a marketing blog that offers an interactive ROI calculator in an article about ad spend. The snippet could show basic tips, but the tool requires visiting the site. That encourages deeper engagement. To help build said engagement, start by:
Offering exclusive tools, downloads, or templates.
Add visuals, charts, and examples that don’t appear in SERPs.
Think Beyond Google To New Search Frontiers
Search is everywhere. Your audience is looking for answers in places like TikTok, Reddit, YouTube, Instagram, and AI assistants. Expanding your reach to touch those places involves being proactive. Repurpose existing blog content into short videos. Answer niche questions in online communities or forums. Optimize for video search on YouTube. Format all content for AI readability.
Diversifying your search presence ensures you don’t depend on a single platform’s algorithm. Users benefit from getting answers in the format and channel they prefer. Think of cooking brands that post recipe videos on TikTok for quick inspiration but provide detailed video instructions on YouTube and long-form written directions on their blog for those who want step-by-step guidance.
Need platform-specific tips? Try implementing the following:
TikTok: 3-second hook + trending hashtag + text overlays with key terms
Reddit: Target 10K+ member subreddits, provide 150+ word helpful responses
YouTube: Add timestamps, chapter markers, and upload transcript files
How To Track And Measure Zero-Click Success
Measuring success in a zero-click world requires a shift from last-click attribution to metrics reflecting visibility and influence.
Start with impressions in Google Search Console to see how often your content appears in SERPs. Monitor AI visibility with tools like RankScale or BrightEdge to identify when your content is cited in AI Overviews or snippets. You can also use social listening tools to track brand mentions across the web and social platforms. Pay attention to referral traffic from AI tools as a sign of indirect engagement.
Adding social engagement to reporting helps measure how often others share or discuss your answers. For NP Digital clients, we often combine these data points into a custom dashboard to track both traditional and emerging search performance. This helps identify which tactics keep your brand visible, even when others aren’t clicking through.
Looking for a place to start? Set up the following:
GSC alerts for 20 percent impression drops on top keywords
Monthly scorecard: 1 point per featured snippet, 2 points per AI citation
Baseline metrics: Track impressions, average position, brand mentions
First-Party Data: Your Secret Lifeline
First-party data is one of the most valuable assets you can own, especially in the zero-click era. When platforms control visibility, having a direct line to your audience lets you reach them without depending on changing algorithms or SERP features.
Building this database often starts with gated content like whitepapers, templates, or exclusive tools to encourage email and SMS opt-ins. Every sign-up gives you an owned channel to nurture.
A Customer Data Platform (CDP) can unify insights across those touchpoints (email, purchase history, webinar attendance) into one profile. This makes it easier to segment audiences and send targeted, relevant content.
Microsoft’s Customer Data Platform allows companies to deliver a personalized B2B experience.
Interactive content like quizzes and surveys can help boost sign-ups while providing valuable insights into user preferences and intent. Pair this with regular, high-value email communication that delivers tips or updates to hit what your audience actually cares about. Of course, none of that matters if you’re not tracking what works. As you implement, consider the following implementation checklist:
Exit-intent popups on your top 10 pages with topic-specific lead magnets
A/B test opt-in placement: sidebar vs. mid-content vs. bottom
Progressive profiling: Collect 2-3 data points per interaction
Target: 2-3 percent email signup rate from organic traffic
Why does all this matter?
Marketers reduce vulnerability to external platform changes while users get more personalized, useful content based on their real interests and behaviors. Over time, this will strengthen loyalty, improve conversions, and create a direct relationship that no search update can ever take away.
FAQs
What are zero-click searches?
They are searches where users get their answers directly in the results without visiting a website. They can include structured snippets, AI Overviews, FAQs, and more.
Is zero-click traffic increasing?
Yes. Search engines and AI features are designed to give answers faster, reducing the need for clicks. In addition, companies are prioritizing search results that keep users on their platforms instead of going off-site for answers.
How do I get value from zero-click searches?
Prioritize visibility, authority, and multi-channel presence. Structured data and unique, authoritative content can help provide this kind of value to your audience.
Conclusion
Thriving in a zero-click future means focusing on being seen and trusted wherever the answers are delivered. Publish content that earns citations and create experiences worth engaging with. Developing a content strategy that meets your customers everywhere they search is only half the battle. To create lasting impacts, you’ll need to track the metrics that reflect real visibility and do everything you can to capitalize on those numbers.
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