You’ve worked hard to build your product catalog. The last thing you want is AI tools like ChatGPT or Google Gemini describing your products inaccurately to potential customers.
AI tools don’t browse your whole store the way a search engine does. They grab what they can find, quickly, and fill in the gaps. For a store with a large catalog, that means incomplete answers, outdated information, or worse, sending shoppers to a competitor.
The new llms.txt feature, available in Yoast SEO for Shopify bridges that gap.
What does it actually do?
It creates a file that tells AI tools which parts of your store matter most: your top products, your collections, your policies, and your key pages. Think of it as handing AI a well-organized store guide instead of letting it wander around on its own.
You switch it on once. We handle the rest.
Two ways to use it
Let Yoast handle it automatically
Turn it on and we’ll build and update the file each week based on your Shopify data. No decisions needed. The file automatically highlights:
Your 10 most-sold products over time
Up to 5 of your largest collections, plus a link to your full product range
Your store policies, including shipping, returns, and privacy
Your homepage, latest blog posts, and most recently updated pages
Any pages you’ve already marked as cornerstone content
Or choose exactly what’s included
If you’d rather have full control, switch to manual selection. You can hand-pick the products and pages you want to feature, and there’s a dedicated spot to add your “About us” page so AI knows the story behind your brand.
Either way, the file updates weekly and removes deleted products automatically.
No technical knowledge needed
Setting this up from scratch would normally mean editing code. We’ve built it directly into your Yoast SEO for Shopify settings so any member of your team can turn it on in seconds. If you already have a redirect set up for /llms.txt, we’ll respect it and let you know, so nothing breaks.
You decide when it’s right for your business
We believe every merchant should have a say in how their content is seen and used as AI plays a bigger role in how people discover products online. That’s why this feature is opt-in.
Turn on the llms.txt toggle in Yoast SEO for Shopify next time you log in to your store.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-03-31 08:04:572026-03-31 08:04:57Introducing llms.txt to Shopify: Give AI a map to your best products
If you tuned into
episode 105 of the Search Off the Record podcast,
you might have heard us diving deep into a topic that is close to our hearts
(and our servers): the inner workings of Googlebot.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-03-31 00:00:002026-03-31 00:00:00Inside Googlebot: demystifying crawling, fetching, and the bytes we process
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-03-31 00:00:002026-03-31 00:00:00New Location for the Google Crawlers’ IP Range Files
Boost posts that are already winning organically, not the ones you hope will catch on.
Paid spend won’t fix weak content. Only boost LinkedIn posts that have social proof.
Your campaign objective tells LinkedIn who to show your post to. Choose your goal strategically so LinkedIn doesn’t optimize for the wrong audience.
Start with one or two targeting filters. Too broad wastes budget on junk impressions, and too narrow spikes costs and limits delivery.
Impressions and clicks are vanity metrics. Rate comparisons between boosted and organic rates tell you what’s actually working.
If you’re not getting views on your LinkedIn posts, you’re losing business.
How do I know that?
LinkedIn is where buyers vet your credibility and compare options before they ever book a call. The platform has become a powerful lead-gen engine.
That’s why LinkedIn can be your highest-leverage channel in B2B, where 89 percent of marketers use it for driving leads.
The challenge, though, is that solid content can still flop.
That’s where boosting comes in. Paid reach behind the right posts breaks you out of the “great content, tiny distribution” trap. Your message suddenly starts reaching the people who truly matter.
Before you hit the Boost button, though, it helps to know which posts are worth putting money behind.
What Does It Mean to Boost a Post on LinkedIn?
Boosting a post on LinkedIn means taking something you published organically and turning it into a paid promotion so more of the right people see it.
Think of it as putting fuel on a fire that’s already burning.
There’s no need to start from scratch in LinkedIn Campaign Manager. All you have to do is pick an existing post from your company page, choose a goal (like more engagement or website visits), define a basic audience, and set a budget.
LinkedIn does the rest, extending your post’s reach beyond your followers. Here’s what that looks like from your Page posts dashboard:
Source: NPD LinkedIn
Here’s how boosting stacks up against your other options:
Organic posts rely on the algorithm and your existing network. If it hits, great. If it doesn’t, it disappears fast.
Building a campaign gives you more control over targeting through advanced marketing metrics, but it requires more setup and management.
Boosting sits in the middle. It’s designed for speed and simplicity, not for hyper-specific targeting or complex funnels.
For a deeper look at LinkedIn’s full toolkit, my LinkedIn marketing guide is a good place to start.
The Challenge of Getting Views on LinkedIn
LinkedIn is the world’s largest professional network with more than 1 billion members.
That sounds like a marketer’s dream, until you try to earn consistent views. The numbers reflect the challenge:
Organic reach is getting squeezed. Richard van der Blom’s 2025 Algorithm Insights Report, which analyzed more than 1.8 million posts, says it has dropped nearly 50 percent.
Most people scroll past without engaging. Socialinsider’s benchmark data shows the engagement rate per impression at about 5.2 percent, meaning about 95 out of 100 people who see a post don’t interact with it.
Timing alone won’t save a post. LinkedIn’s continued push toward relevance over recency means even well-timed content can get buried if the algorithm deems it less relevant to a given user.
That’s exactly why boosting works. It stops the guessing game on distribution and puts paid visibility behind posts that already deserve a wider audience.
When Does It Make Sense to Boost a LinkedIn Post?
Boosting only makes sense when the post does. Put paid spend behind weak content, and you’re wasting marketing dollars.
You should boost a post when:
It’s already showing strong early signals. Comments and saves in the first few hours, for example, tell you the content is resonating.
The post is tied to a hard deadline. Events, product launches, webinars, and hiring pushes all have a window where visibility directly drives action.
You have one clear conversion goal, such as a download or follow.
You need reach beyond your existing network, and organic distribution won’t get you there fast enough.
Hold off on boosting when:
The post isn’t gaining momentum on its own.
The call to action (CTA) is vague. “Thoughts?” is not a measurable conversion goal, for example.
You haven’t defined what success looks like before you spend.
However, just because decision-makers use the platform doesn’t mean they’ll see your post. LinkedIn’s algorithm weighs credibility heavily in distribution, and verified members see up to 50 percent more engagement on their posts as a result.
Boosting works in a similar way. It amplifies what’s already credible, not what’s struggling to find its footing. Boost your winners, not your wishes.
How to Boost a Post on LinkedIn (Step by Step)
Boosting is straightforward, but the results depend on the decisions you make before you hit publish. Here’s how to do it right.
Choose the Right Post to Boost
Start with posts already showing signs of life.
Look for strong early engagement (especially comments and saves) or a clear spike in impressions versus your usual baseline. If a post isn’t earning attention organically, paid reach won’t magically fix it.
That’s why you should boost only what’s already working.
Select Your Campaign Objective
Open the post from your company page and hit Boost. Then choose the objective that matches what you’re trying to do:
Brand awareness, if you’re launching something new or want to grow your share of voice in a category
Post engagement, if you want to grow followers or keep your brand top of mind
Video views, if your post is a video and watch time is the priority
Website visits, if you want to drive traffic to a landing page or lead capture form
Here’s what that looks like within LinkedIn.
Define Your Audience
Keep targeting focused enough to be relevant, but not so narrow that it limits delivery. Start with one or two core filters: job title or function, seniority, industry, company size, or location.
If your audience is too broad, you’ll buy cheap impressions that don’t convert. If it’s too tight, your costs will spike and your delivery won’t be consistent. Keep in mind that relevance beats reach every time.
Here’s what setting your audience parameters looks like in-platform:
Set Your Budget and Duration
Set a lifetime budget and choose your start and end date. If your post is tied to a deadline-driven event like a webinar, set your end date accordingly.
Start with a modest test budget, and give the campaign enough time to generate meaningful data. A few hours won’t tell you much.
Watch your frequency as your boosting campaign runs. If the same audience sees your post too many times, engagement may drop and your spend will likely be less efficient.
Review and Launch
Before you hit Boost, run through this quick checklist. Make sure that:
Your copy and visuals look exactly as intended.
Your messaging matches your campaign goal.
There are no grammar or spelling errors.
All links are working.
You confirm your audience targeting and budget.
Once everything checks out, it’s time to boost.
Best Practices for Boosting LinkedIn Posts
Boosting isn’t magic. It just gives a good post more distribution, but it can’t rescue a weak one. Here’s how to make sure your post is worth putting money behind.
Lead with Native-First Content
If your goal is to increase views and engagement, it’s best to keep people on the platform. Native formats like video or documents are built for feed consumption. A Metricool study shows video post growth up 53 percent, while clicks on linked content are up 28 percent.
The format should follow your goal. Native content keeps readers in the feed and builds engagement. Links work when you want to drive traffic to a specific destination. Documents are strong for capturing attention before directing readers off the platform.
Test what works, and track the results.
Write Like a Person
Keep your copy tight and human. LinkedIn posts allow for up to 3,000 characters, but that doesn’t mean you should use them all.
Readers might be quickly scrolling through LinkedIn over lunch or during a coffee run. They’ll read what’s worth reading and skip over everything else. So be direct and to the point. Use plain language, and focus your post on one specific point or outcome.
Win the First Line
On mobile, LinkedIn previews cut off at about 200 characters. On desktop, it’s around 300. Sponsored posts can show an even shorter preview. Everything after that lives behind a “see more” click that many people won’t tap.
Your first line is your hook, and its job is to grab the reader’s attention.
A few approaches that work:
Lead with a surprising stat or a bold claim.
Ask a question the reader wants answered.
Open with a contrarian take on something familiar.
Set up a story with an unexpected outcome.
Nicolas Cole’s opening line in the post below is a good example: “Over the last 10 years, I’ve made $10,000,000+ as a writer.” It’s a single stat that stops the scroll. The second line (“The secret?”) creates just enough tension to earn the click.
The hook is just the beginning, though. Once you have a reader’s attention, provide so much value that they keep coming back. For example, you might offer your latest lead magnet.
A strong lead magnet gives readers a reason to act beyond the post itself. The graphic from Pathmonk below covers the most effective options for B2B audiences. It includes:
Odds are your team already has at least one of these in some form.
Use One Clear CTA
Each post should have one job and clearly direct the reader on what to do next, like subscribing or downloading.
The more CTAs you stack, the more you dilute the click. LinkedIn sponsored content formats are built around a single CTA path for good reason.
To get the best results, match your CTA language to your post’s intent. If you want them to download your checklist, say, “Get the checklist.” Saying something like “Learn more” gives the reader no clear direction and no reason to move.
Watch Early Results and Pause Fast
Give a boosted post 24 to 48 hours before drawing conclusions. That’s enough time to collect a meaningful signal but not so much time that you waste spend on something that’s not working. Test ad variations with LinkedIn’s A/B testing workflows and review their performance.
How do you diagnose where your post has gone wrong? The best place to start is your click-through rate (CTR). If you have a low CTR, then there’s an issue with your creative (post copy or visuals). If you have a high CTR but a low conversion rate, the landing page or form you’re using could be the issue.
How to Measure the Success of a Boosted Post
A boosted post’s results can be misleading if you measure the wrong things. Start with the metrics that match your objective:
Engagement: Track your engagement rate by totaling the post’s social signals and dividing by the number of total impressions. Comments matter more than likes because they signal real interest, not drive-by approval.
Website visits: Watch CTR. See how many people are landing on your website from your boosted post. Compare those numbers against a similar organic post to see whether the boost is moving traffic or just generating impressions.
Brand awareness: Look at your follower growth and repeat engagement from the same audience over time. These are signal metrics that tell you whether the right people are paying attention.
From there, look at whether rates moved, not just totals. If impressions climbed but CTR and engagement rate stayed flat, the post reached more people without changing their behavior.
More visibility without action is not a success metric. A boost works when it drives the specific outcome you set your objective around. That’s the only measure that matters.
FAQs
How do I boost a post on LinkedIn?
Go to your LinkedIn company page in admin view, open the post, and click Boost. Then choose your objective, audience, budget, and duration. Keep it simple by focusing on one goal, one or two audience filters, and one CTA.
How much is it to boost a post on LinkedIn?
You can often begin with as little as $10, making it one of the more accessible ways to advertise on LinkedIn. It’s typically best to start small for a few days, and then scale only if results justify it. For a deeper look at LinkedIn advertising costs overall, check out my LinkedIn ads pricing guide.
Can you boost carousel posts on LinkedIn?
Not if it’s a multi-image carousel. Boosting doesn’t support posts with more than one image. If you want a “carousel feel,” use a document or PDF post and promote it through Campaign Manager instead.
Conclusion
LinkedIn marketing doesn’t need to be a mystery. The platform is one of the most powerful tools your business has for reaching real decision-makers, and the right approach can make it a game-changer.
Start by publishing content your audience actually wants to read. Then use boosting to put paid reach behind what’s already earning attention organically. That way, the right people see your post on your timeline, not whenever the algorithm gets around to it.
Consistent social media measurement is what separates marketers who scale from those who guess. Track your rates and compare them against your organic baseline. When something isn’t working, cut it fast.
Use data to make smart boosting decisions, and you’ll earn more qualified attention that leads to real business results.
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AI search is changing how visibility works. Users are getting direct answers instead of clicking links, which means fewer chances to drive traffic. In this shift, AI citations are becoming the new gatekeepers, deciding which sources get featured in answers. Over the past year, search has moved from ranking pages to selecting sources, pushing us from traditional SEO toward AI-driven visibility.
In this article, we’ll explain what AI citations are, how they work, and how you can earn them.
AI citations are references that search engines include in AI-generated answers, enhancing credibility and visibility
This shift in visibility moves from traditional SEO ranking to AI-driven inclusion as a key factor for brand presence
AI tools retrieve information from diverse sources, with citations coming from both top-ranking and deeper pages
To earn AI citations, create valuable, structured content and establish topical authority across your niche
Tools like Yoast AI Brand Insights help track your AI visibility and citation presence across platforms
What are AI citations?
Citations have always been a way to show where information comes from and why it can be trusted. The same idea now applies to AI-generated answers.
ChatGPT cites resources in its answer
AI citations are the references that search engines and AI tools include to support the answers they generate. When a tool like ChatGPT responds to a query, it often points to specific pages or sources that back up the information. These references act as signals of credibility, helping users understand where the answer is coming from and giving them a way to explore the original content.
In simple terms, if your content is cited, it becomes part of the answer itself, and not just another link in the results.
AI citations vs the blue link era
If AI citations determine what gets included in answers, it’s worth asking how this differs from how search used to work. Because this isn’t just a feature update, it’s a shift in how visibility itself is earned.
In the traditional model, ranking higher meant getting more clicks. In AI-driven search, being selected as a source matters just as much, if not more.
Aspect
Traditional SEO
AI citations
Visibility
Blue links
Ai-generated answers
Traffic
Click-driven
Influence-driven
Authority signal
Backlinks
Credibility and accuracy
User action
Visit website
Consume instant answers
This doesn’t mean traditional SEO is going away. Rankings, indexing, and backlinks still play a critical role. However, how that value gets surfaced is changing. Instead of just competing for position on a results page, you’re now competing to be part of the answer itself.
Do check out Alex Moss’s talk at BrightonSEO, 2025, on the evolution of search intent and discoverability.
Where do AI citations come from?
Before you try to earn AI citations, it’s important to understand where they actually come from. Because you’re not just competing with other blog posts, you’re competing with an entire information ecosystem.
AI models pull their answers from a mix of sources:
Web content: Blog posts, guides, landing pages, and long-form articles
Structured sources: Platforms like Wikipedia, documentation hubs, and product data feeds
Forums and UGC: Discussions from Reddit, Quora, and Stack Overflow
First-party data: Brand websites, help centers, and official resources
How the sources are selected is quite interesting. A recent analysis of Google’s AI Overviews found that citations don’t strictly come from top-ranking pages. In fact, only about 38% of cited sources rank in the top 10 results, meaning a large share comes from deeper pages or alternative formats.
Another key insight by CXL: AI models tend to prioritize clear, early answers within the content, with a significant portion of citations pulled from the top sections of a page rather than from deeper sections.
The takeaway is simple. AI systems are not just ranking content; they are selecting the most useful pieces of information across formats and sources. That means your content is competing not only for rankings but also for clarity, structure, and trustworthiness across this entire ecosystem.
Types of AI citations
Not all AI citations look the same. Depending on the query and intent, AI models pull in different types of sources to support their answers.
Broadly, you’ll see three main types:
Informational citations
These are the most common. AI tools refer to blog posts, guides, and educational content to explain concepts or answer questions. If someone asks, “what are AI citations,” the sources cited will typically be long-form, explanatory content.
Informational citations made by ChatGPT
Product citations
These show up in commercial or comparison queries. For example, “best SEO tools” or “top project management software.” Here, AI models cite product pages, listicles, and review-based content to support recommendations.
Product citations by Google AI mode, the model shares both online and offline options
Multimedia citations
AI doesn’t rely solely on text. Videos, images, and other visual formats can also be cited, especially when they better explain something than text alone. Think tutorials, walkthroughs, or demonstrations.
Multimedia citation for a query by ChatGPT
How AI citations impact brand credibility
AI citations don’t just drive visibility. They shape how your brand is perceived before a user even visits your website.
When your content is cited in an AI-generated answer, some of that trust transfers to your brand. You’re no longer just another result on a page; you’re part of the answer itself. And that changes how users interpret your authority.
This also means buyer decisions are starting earlier. Users may form opinions, shortlist options, or even make decisions directly from AI responses, without ever clicking through. If your brand isn’t cited, you’re not part of that consideration set.
There’s also a strong signal of relevance at play. Being included in AI answers suggests that your content is not just optimized, but genuinely useful in context. It tells both users and algorithms that your brand deserves to be surfaced.
Over time, this creates a compounding effect. The more your content is cited, the more your brand becomes associated with specific topics. That repeated exposure builds familiarity, authority, and trust.
How AI citations work: a complete breakdown
So far, we’ve talked about what AI citations are and where they come from. But how do AI systems actually decide what to cite?
Let’s break it down.
A diagram by AWS showing the conceptual flow of using RAG with LLMs
At a high level, most AI-powered search systems follow a retrieval-and-synthesis process, often powered by approaches such as Retrieval-Augmented Generation (RAG). In simple terms, they don’t just generate answers; they find, evaluate, and assemble information from multiple sources before deciding what to cite.
Here’s what that process looks like in practice:
1. Query understanding
Everything starts with intent. The AI interprets what the user is really asking, whether it’s informational, navigational, or commercial. This step shapes what kind of sources it will look for.
2. Retrieval of sources
Next, the system pulls in potential sources from multiple places:
Web indexes
Training data patterns
Live retrieval systems (depending on the model)
This is where your content first enters the consideration set.
3. Source evaluation
Not all sources are treated equally. AI models evaluate them based on:
Relevance to the query
Authority and trust signals
Clarity and structure of information
Entity-level trust (how credible the brand or author is)
When you look at these signals closely, they all point in one direction. Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) play a central role in determining what gets cited. In other words, AI systems aren’t just looking for answers; they’re looking for reliable sources behind those answers.
4. Answer synthesis
Instead of showing individual links, AI combines insights from multiple sources into a single, cohesive answer. This is where your content may be used, even if it’s not directly cited.
5. Citation selection
Finally, the model decides which sources to:
Explicitly cite (with links or references)
Implicitly use (without direct attribution)
This is the step that ultimately determines your visibility.
How this differs across AI systems
While the core process is similar, different AI tools prioritize different parts of this pipeline.
AI systems
How it handles citations
ChatGPT
Leans more on third-party sources and consensus, such as directories, reviews, and aggregator sites, rather than relying heavily on brand-owned content.
Perplexity
Focuses on retrieval-first behavior, pulling from a wide range of web sources and surfacing multiple citations to support transparency (strong emphasis on external validation).
Gemini
Prioritizes brand-owned and structured content, especially pages that are clearly organized and easy to interpret.
Even though the process is complex, the signals that increase your chances of being cited are surprisingly consistent:
Well-organized structure: Clear headings, bullet points, and logical flow make it easier for AI to extract information
Evidence-based reasoning: Content that references data, sources, or supporting claims is more likely to be trusted
Timeliness and relevance: Fresh, updated content often gets prioritized, especially for evolving topics
Authoritative voice and depth: Content that demonstrates expertise and covers a topic comprehensively stands out
Topical consistency: Brands that consistently publish around a topic are more likely to be recognized as reliable sources
The key takeaway here is simple: AI citations are not random. They are the result of a structured evaluation process in which clarity, trust, and relevance determine who is included in the final answer.
So far, we’ve looked at what AI citations are and how models decide what to cite. The next question is the one that matters most: how do you actually get cited?
Because this isn’t just about creating content, it’s about sending the right signals that your content is worth citing. Here are some strategies that can help you do exactly that:
1. Create citation-friendly content
Citation-worthy content goes beyond surface-level answers. It offers original thinking, clear explanations, and real value, helping AI models support their responses with confidence. In other words, it’s not just optimized, it earns references by being genuinely useful.
The following content types consistently get cited by AI models:
Content type
What to write
Why AI loves them
Original research
Studies or data that answer new or unexplored questions
Gives AI concrete evidence to support claims
Case studies
Real-world examples showing how something works in practice
Helps AI justify recommendations with proof
Thought leadership
Opinion-led content with unique insights or perspectives
Adds depth and diversity to AI-generated answers
News content
Timely, accurate coverage of recent developments
Fills gaps where training data falls short
2. Build topical authority (clusters)
AI models don’t just evaluate individual pages; they evaluate how consistently you cover a topic.
If you publish multiple pieces on a specific subject, each addressing different aspects, you signal depth, expertise, and reliability. That’s what topical authority is all about.
And this is where E-E-A-T naturally comes into play. The more consistently you demonstrate experience and expertise in a niche, the more likely your content is to be trusted and cited.
What to do in practice:
Create clusters around a core topic (pillar page/cornerstone content + supporting content)
Cover both broad and specific questions in your niche
Go beyond basic answers, add expert insights, examples, or real-world context
Keep your messaging and terminology consistent across content
AI systems evaluate content, but they also evaluate who is behind it.
Strong entity signals help models understand your brand, your authors, and your credibility within a topic. The clearer these signals are, the easier it is for AI to trust and cite your content.
What to do in practice:
Build clear author profiles with expertise and credentials
Maintain consistent brand mentions across your site and the web
Use structured data (schema) to define authors, organizations, and content relationships
Ensure your “About” and author pages clearly establish credibility
4. Earn external validation signals across the web
AI models don’t rely on a single source of truth. They validate information by cross-referencing multiple sources across the web.
That means your credibility isn’t built only on your website. It’s shaped by how consistently your brand shows up across trusted platforms. The more aligned and authoritative those signals are, the easier it is for AI systems to trust and cite your content.
Think of this as building a web-wide validation layer that reinforces your brand through multiple independent sources.
This is also where traditional SEO practices like link building evolve. It’s no longer just about backlinks, but about earning consistent, high-quality mentions that strengthen your entity across the web.
What to do in practice:
Contribute insights to reputable publications in your niche
Earn consistent mentions across industry blogs, directories, and review platforms
Be active in communities like Reddit, Quora, or niche forums
Run digital PR campaigns that reinforce your brand narrative across sources
5. Keep content fresh and updated
AI models prefer content that reflects current information.
Outdated content is less likely to be trusted, especially for topics that evolve quickly. Regular updates signal that your content is still relevant and reliable.
What to do in practice:
Refresh key articles with updated data, examples, and insights
Add new sections instead of rewriting from scratch where possible
AI models don’t read content the way humans do. They extract answers.
Most AI-generated responses are built by identifying clear, concise answer blocks within content. And increasingly, users prefer this format. In fact, according to a poll by IWAI, 67% of users find AI tools more efficient than traditional search for getting answers. That shift makes one thing clear: if your content doesn’t directly answer questions, it’s less likely to be surfaced or cited.
This means it’s not enough to include answers. You need to structure your content so those answers are easy to find, interpret, and reuse.
What to do in practice:
Lead sections with direct, concise answers before expanding
Use headings that mirror real user queries and intent
Break down complex topics into scannable, extractable sections
Add summaries, definitions, or key takeaways at the start of sections
Anticipate follow-up questions and answer them within the same content
Tracking AI brand presence with Yoast
By now, we know what AI citations are, how they work, and how to earn them. But here’s the real question: how do you know if you’re already being cited? And if not, how do you understand where your competitors are showing up and where you’re missing out?
As AI-generated answers become a key discovery layer, most traditional analytics tools fall short. They can tell you about traffic, but not whether your brand is being mentioned, how it’s being perceived, or which sources AI systems trust when referencing you. That’s a critical blind spot, especially as AI answers increasingly shape user decisions before a click even occurs.
Yoast AI Brand Insights helps you track and understand your AI visibility, citations, and brand mentions across platforms like ChatGPT, Gemini, and Perplexity, so you can move from guesswork to informed action.
Here’s what it enables you to do:
Sentiment tracking
Understand how your brand is being perceived in AI-generated answers. The tool analyzes keywords associated with your brand and shows whether the overall sentiment is positive or negative, helping you spot tone issues and shifts over time.
Citation analysis (brand mentions)
See when and where your brand is being cited. More importantly, understand which sources AI platforms reference alongside your brand, so you can identify citation gaps and opportunities to improve your presence.
Competitor benchmarking
See how you stack up against other brands mentioned in your prompts
AI visibility is relative. This feature lets you compare your brand’s citations, mentions, and sentiment against competitors, helping you understand who is being surfaced more often and why.
Question monitoring
AI search is driven by queries. With question monitoring, you can track specific brand-related or industry questions and see whether your brand appears in the answers, giving you direct insight into where you’re visible and where you’re missing.
AI visibility index
See your score, which is a representation of different AI signals
Instead of looking at isolated metrics, Yoast combines signals like citations, mentions, sentiment, and rankings into a single visibility score. This gives you a clearer picture of how your brand performs across AI systems over time.
The bigger picture here is simple: Yoast AI Brand Insights helps you understand your position in this new ecosystem, so you can strengthen your presence, close gaps, and ensure your brand is part of the answers your audience is already consuming.
FAQs on AI citations
AI citations can feel complex at first, especially as search continues to evolve. Here are answers to some of the most common questions to help you navigate them better.
Are backlinks different from AI citations?
Yes, they serve different purposes. Backlinks help your pages rank in traditional search, while AI citations determine whether your content gets included in AI-generated answers. In short, backlinks drive visibility on SERPs, while citations drive visibility within answers.
No, AI systems don’t always include citations. When responses are generated purely from pre-trained knowledge rather than retrieved sources, citations may not appear.
To test this, I tried the following prompts on ChatGPT:
Out of these, citations appeared in about half of the responses.
A clear pattern emerged:
Queries involving products, recommendations, statistics, or recent events were more likely to trigger citations
Queries focused on definitions or general knowledge often did not include citations
This shows that citation behavior depends heavily on the query type, intent, and context. Not every answer requires a source, but the more specific or evidence-driven the query, the more likely citations are to appear.
How do I direct AI models to the most important content on my website?
You can’t directly control what AI models choose to cite, but you can make it easier for them to understand and prioritize your content.
One effective way to do this is by using llms.txt, a feature in Yoast SEO. It creates a structured, LLM-friendly markdown file that highlights your most important pages, helping LLMs better understand your site when generating answers.
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Think of it as a way to clearly communicate which content matters most, so when AI systems look for reliable sources, your key pages are easier to interpret and surface.
AI citations: The currency of the AI-driven web
AI citations are changing how users discover and trust information. They don’t just complement rankings; they reshape them by deciding which sources become part of the answer itself. In many cases, users no longer need to click to explore. If your content is cited, you’re visible. If not, you’re invisible.
This shift also changes what we optimize for. It’s no longer just about traffic; it’s about trust, relevance, and inclusion in the answer layer. As we explored in our recent read, Rethinking SEO in the age of AI, the central question for SEO is evolving. It’s no longer just, “Can Google find my website?” It’s now, “Does the AI have a reason to remember my brand?”
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The March 2026 SEO Update by Yoast is part of our monthly webinar series covering the latest developments in search and AI. Hosted by Carolyn Shelby and Alex Moss, this month’s session explored how AI is reshaping search, Google’s latest moves, and what brands should prioritize now.
AI is moving beyond standalone apps, integrating into messaging platforms (like Claude’s Telegram/Discord support) and desktop environments (e.g., Meta’s My Computer). This shift makes AI more accessible but also blurs the lines between search and daily tools.
Why it matters: Brands must ensure their content is discoverable across multiple surfaces, not just traditional search engines.
Actionable takeaway:
Optimize for conversational queries and structured data to improve visibility in AI-driven tools.
Google’s patent for AI-generated landing pages
Google filed a patent describing a system that replaces traditional SERPs with AI-generated landing pages. This could signal the end of the “10 blue links” era, forcing brands to rethink how they measure visibility.
Why it matters: If Google shifts to AI-generated pages, traditional ranking metrics may become less relevant. Brands will need to control their narrative across multiple sources to ensure accuracy in AI responses.
Actionable takeaway:
Audit your content for clarity and structure (e.g., avoid excessive JavaScript, use clear headings).
Diversify your presence beyond your website (e.g., social media, YouTube, newsletters) to reinforce authority.
Markdown as a preferred format for AI
Markdown is gaining traction as a lightweight, AI-friendly format. WordPress.org now offers Markdown versions of pages, and tools like Cloudflare’s crawl endpoint make it easier for AI to parse content efficiently.
Why it matters: While Google downplays Markdown’s importance, other AI tools may rely on it for grounding responses. Simplifying your content structure could improve visibility in AI-driven search.
Actionable takeaway:
Consider offering Markdown versions of key pages (e.g., FAQs, product descriptions) to help AI extract content.
Avoid hiding critical information in images or complex JavaScript, as AI may not process it efficiently.
Google Search Console adds branded vs. non-branded filter
Google Search Console now includes a filter to separate branded and non-branded queries. This helps brands identify confusion in search intent and optimize accordingly.
Why it matters: If non-branded queries drive traffic, it may signal an opportunity to refine messaging or target new audiences.
Actionable takeaway:
Use the filter to identify gaps in your content strategy (e.g., if branded queries dominate, expand into non-branded topics).
Monitor for unexpected branded queries, which may indicate confusion or misalignment with user intent.
Google Maps integrates AI for search
Google Maps is testing an AI-powered chat feature that lets users ask questions (e.g., “Find a Starbucks on my route”). Early feedback suggests it’s not yet as accurate as traditional search, but this could evolve quickly.
Why it matters: AI-driven local search could change how users discover businesses, making it critical to optimize for conversational queries.
Actionable takeaway:
Ensure your Google Business Profile is up to date with accurate hours, locations, and services.
Use natural language in your content to align with how users phrase questions.
Universal Commerce Protocol (UCP) expands
Google’s Universal Commerce Protocol (UCP), an open standard for AI-driven e-commerce, added new features like cart management, catalog search, and identity linking (for loyalty programs). This aims to streamline shopping within AI platforms.
Why it matters: UCP could become a standard for AI-powered commerce, making it essential for e-commerce brands to adopt early.
Actionable takeaway:
Explore UCP integration to improve visibility in AI-driven shopping experiences.
Optimize product schema and ensure your Merchant Center data is accurate.
Zero-click search doesn’t mean zero influence
Rand Fishkin’s keynote at the Industrial Marketing Summit highlighted that while zero-click searches are rising, brands can still influence AI responses by maintaining a strong, consistent presence across multiple platforms.
Why it matters: AI relies on corroborating signals (e.g., repeated mentions of your brand across trusted sources) to validate information. A single website isn’t enough, so you need a multi-channel strategy.
Actionable takeaway:
Repurpose content across platforms (e.g., LinkedIn, Substack, YouTube) to reinforce your brand’s authority.
Ensure your messaging is consistent across all channels to improve AI’s confidence in your content.
What to focus on in 2026
The March 2026 update highlighted several priorities for search strategy:
Optimize for AI-driven search: Use structured data, clear headings, and consistent messaging to improve visibility in AI responses.
Build brand authority across channels: Diversify your presence beyond your website to reinforce your narrative in AI-generated content.
Prepare for agentic commerce: Adopt protocols like UCP and optimize product schema for AI-powered shopping.
Avoid low-quality AI-generated content: Focus on high-value, human-centric content that aligns with user intent.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-03-27 09:02:132026-03-27 09:02:13The March 2026 SEO Update by Yoast recap
Keyword cannibalization happens when multiple pages target the same keyword and intent, causing them to compete and dilute your rankings.
Spot it quickly with a site search in Google, the Pages view in Search Console, or a keyword cannibalization report in your SEO platform of choice.
Fix it by choosing a primary page and then merging overlapping content and 301-redirecting weaker URLs to consolidate authority.
If merging isn’t an option, reoptimize each page around a distinct keyword intent.
Prevent keyword cannibalization going forward with a keyword map that assigns one primary keyword and intent per URL.
Keyword optimization is a core part of most digital marketing strategies. While it is a pillar of good SEO, it can backfire if keyword cannibalization sneaks in.
Repeating keywords across multiple pages turns those pages against each other in search results. Because Google doesn’t know which one to prioritize, they both lose ground.
Think of it this way: If you’re looking for “the best running shoes” and see two articles from the same site with near-identical titles, you won’t know which to click.
That’s keyword cannibalization, and it happens more than you might realize. This guide covers what it is and how to fix it before it drags down your rankings.
What Is Keyword Cannibalization?
Keyword cannibalization is when multiple pages on your site target the same search query, leading them to compete rather than reinforce a single strong result.
The consequences pile up quickly:
It dilutes your authority across multiple URLs, so none of them stand out as the “best” result.
Click-through rate (CTR) can take a hit when Google serves the wrong page for a given search intent.
Google gets mixed signals about which page should rank, which often leads to unstable positions.
Because signals are spread thin, nothing ranks as high as it could.
What Are Examples of Keyword Cannibalization?
Here’s a real-world example of keyword cannibalization: a site search for “email marketing” on MoEngage.com.
The results show multiple MoEngage.com blogs ranking for the same keyword. That’s a textbook cannibalization problem, and it’s dragging down the performance of every page in that cluster.
If a search of your site also reveals keyword cannibalization, don’t worry. My own blog has had the same issue. Here’s a historical example:
Two posts were splitting authority and muddying which page Google should rank. We’ve fixed it since, which is exactly what this guide will show you how to do.
How Do I Find Cannibalized Keywords?
If you think your site is suffering from keyword cannibalization, here’s how to find out for sure.
Do a Quick Google Site: Search
Type this into Google: site:yourdomain.com “target keyword”
This brings up every page on your domain associated with that keyword. If you see multiple pages that look like they’re trying to rank for the same term (or answering the same intent), you likely have a cannibalization problem.
Pick a query you want to investigate (or use the filter to type it in).
Click into the Pages view.
Look for more than one URL getting impressions and clicks for the same query.
If two (or more) pages are trading impressions for the same keyword over time, Google’s basically saying, “I’m not sure which one is the best match.”
Use SEO Tools to Spot Overlapping URLs
You can also use keyword research tools to simplify things and get comprehensive data for better keyword planning. Tools like Ubersuggest, Semrush, and Ahrefs support keyword research and can help you spot URLs that are competing for the same queries.
Start with Ubersuggest if you want a straightforward audit:
Enter your domain URL into Ubersuggest.
Go to the Site Audit section.
Review flagged issues for duplicate keywords and pages competing for the same search terms.
Ahrefs and Semrush also have helpful functionality:
Ahrefs’ Site Explorer: Plug in your domain, then check which pages are ranking for the same keyword.
Keeping everything in one place makes cannibalization easier to spot before it becomes a problem.
If two pages are competing for the same keyword, you have two basic options: Merge them into one stronger page, or redirect the weaker URL to the primary one so all authority flows to a single source. We’ll have more on this later.
A keyword map makes those calls obvious rather than reactive and helps new content get planned against existing pages before conflicts develop.
Common Causes of Keyword Cannibalization
Keyword cannibalization tends to happen when content grows fast, but strategy doesn’t keep up. Here are some common triggers:
Too much overlapping content. Publishing multiple posts on the same topic from slightly different angles causes each new post to chip away at the relevance of the last one. “SEO tips” versus “SEO best practices” is a classic example.
Duplicate keyword targeting. This could be two writers independently picking the same target keyword or refreshing an old post that accidentally overlaps with a newer piece. Solid keyword research and clear ownership by intent prevent this.
Poor internal linking. If you don’t clearly link to the main page using consistent anchor text, Google has to guess which URL matters most. That can lead to unstable rankings.
Product or category pages competing with blog content. When a category page and a blog post both target the same commercial keyword, Google may rank the wrong one or rotate between them, hurting conversions.
The most direct way to prevent cannibalization is to make sure no two pages are competing for the same query. Each page should have one primary keyword tied to a distinct search intent.
Rather than stacking pages around “SEO tips,” point each one at a distinct query. “SEO for beginners” targets a different reader than “advanced SEO strategies,” for example, even though the topics are related.
Each page stays on brand while targeting various short and long-tail keywords relevant to your industry.
A few tools can help you with this. Google Trends and Google Search Console are free starting points for spotting demand and query data. Ubersuggest, AnswerThePublic, and Moz Keyword Explorer are great options for going deeper into keyword ideas and competitive gaps.
2. Track Keyword Rankings and Performance for Anomalies
A keyword strategy only works if you’re watching how those keywords perform over time.
The goal here is to spot early signs of keyword cannibalization before it drags traffic down.
Watch for these anomalies:
Rank swapping: The same keyword bounces between two URLs (Page A ranks, then Page B, then back again).
Split signals: Impressions and clicks for one query get spread across multiple pages in Google Search Console.
Unexplained CTR drops: You’re still ranking, but the page Google displays aren’t the best match for the query, so fewer people click.
Sudden dips after publishing or updating: A new post goes live (or an old one gets refreshed), and another page’s rankings and traffic slide.
Consistent tracking helps you see which keywords are performing and which may be caught in a cannibalization loop.
Use Search Console for query and page overlap. For rank volatility over time, Ubersuggest, Semrush, and Ahrefs all track keyword movement well.
3. Focus on Topics and Search Intent First and Keywords Second
If you chase keywords without mapping them to a specific search intent, you’ll end up with multiple pages answering the same question in slightly different ways. That’s when Google starts bouncing between URLs, and none of them becomes the clear winner.
Start by identifying the topic and the intent behind it: Is someone comparing options, looking for a how-to, or ready to buy? Build one strong main page for that intent, and use supporting content to cover subtopics rather than duplicate the core answer.
Quora and Reddit also showcase real audience questions, and Google’s “People Also Ask” results show you what searchers want answered.
Shifting focus from keywords to topics tends to produce content that covers a subject more thoroughly, which can support stronger organic reach over time.
4. Do Regular Content Audits
A quarterly content audit helps you catch overlap early, and it’s worth running one after any major content push or site update.
Review your top topic clusters and flag any pages that target the same keyword or answer the same question. If two posts meet the same criteria, one of them is probably redundant.
Are you prioritizing topics and keywords that align with your current marketing goals?
Add one final check: Do we have one clear primary page for this intent?
If not, you know what to fix. Merge or refocus your content so Google (and readers) see a single best answer.
5. Consolidate Competing Pages
When you spot two pages competing for the same keyword and intent, the fix is usually to merge them into one stronger piece.
For example, if you have “best SEO tools” and “top SEO software” going after the same search, combine them into one updated, evergreen guide.
Keep the strongest sections from each post, fill any gaps, and organize the structure with clear headers. A table of contents helps if the page runs long.
Then set up a 301 redirect from the old, weaker URL to the new primary URL. That way, authority flows to one page, and Google has a clear winner to rank.
6. Reoptimize Page-Level SEO
After you’ve picked your priority page, make it painfully obvious to Google (and readers) that this is the best match for the query.
Start by revisiting the on-page fundamentals:
Rewrite the title tag to reflect the primary keyword and the specific intent behind the query.
Tighten your H1 and H2s so they reinforce one clear topic. If your headers drift into adjacent topics, you’re basically inviting overlap with other pages.
Refresh the intro and key sections to answer the query quickly, then support that answer with deeper subtopics.
Check the body copy for mixed intent. If the page is informational, for example, don’t randomly pivot into “buy now” language halfway through.
Internal linking does the rest. Link to the priority page from related posts using descriptive anchor text and update older overlapping pages so they point to the priority URL, starting with intros and high-traffic sections.
This is how you consolidate signals without merging everything.
7. Use Canonical Tags
Canonical tags are Google’s tiebreaker signal. When you have two (or more) very similar pages, a canonical URL tells search engines which version you want treated as the primary one.
You’re basically saying, “These pages are related, but this is the page that should get the ranking credit.”
This is ideal when you need multiple versions to exist:
Product pages with filtered or sorted variations (same core content with different parameters)
Location or language versions that are mostly the same
Near-duplicate landing pages for campaigns where consolidation isn’t practical
Add a canonical tag on the duplicate/similar page that points to the preferred main URL. That helps consolidate signals like links and relevance, reducing the odds of Google ranking the wrong page.
One important note: canonicals are a hint, not a guarantee. If the pages aren’t truly similar, Google may ignore the tag. For pages with heavy content overlap that you need to keep live, canonicals are generally a reliable option.
Addressing Keyword Cannibalization Proactively
Prevention is more efficient than fixing cannibalization after the fact. Most accidental overlap happens when teams publish quickly without a shared record of which topics and keywords are already covered.
Start with a simple keyword map and content calendar. One shared doc where every URL is tied to a primary keyword and a clear intent is all you need. That alone prevents the most common source of cannibalization: two pages answering the same question in slightly different ways.
Before you publish anything new, run a quick site search (site:yourdomain.com “target keyword”) for a quick Google gut-check.
If a page already exists for that keyword or intent, you have options: Refresh or expand the existing piece, or write a supporting article that targets a different subtopic instead of competing head-on.
Then keep an eye on performance in Google Search Console.
Watch for multiple URLs ranking for the same query in Search Console, or rankings that bounce between pages over time.
Internal linking is also a key part of prevention. Link related articles to the priority page using descriptive anchor text so Google understands which URL is the authoritative source on that topic.
Follow these steps consistently, and each topic on your site has one strong page behind it rather than several weaker ones splitting the same ground.
Keyword cannibalization happens when multiple pages on your site target the same keyword (and usually the same search intent). Instead of helping you rank more, those pages compete. That can dilute authority and confuse Google, keeping either page from reaching its full ranking potential.
What’s the difference between keyword stuffing and keyword cannibalization?
Keyword stuffing is cramming too many keywords into a single page to try to manipulate rankings. Keyword cannibalization is spreading the same keyword across too many pages. Both hurt your SEO, but in different ways. Stuffing makes one page look spammy to Google. Cannibalization makes multiple pages compete against each other, so none of them ranks as well as it could.
How can I prevent keyword cannibalization?
The best way to prevent keyword cannibalization is to be proactive. Use a keyword map so every important URL has one primary keyword and intent. Before publishing anything new, run a site search (site:yourdomain.com “keyword”) to see what already exists. Then use internal links to point related posts to the main page for that topic. Finally, watch Search Console for queries that trigger multiple URLs.
How do I solve keyword cannibalization?
Start by identifying overlapping URLs in Google Search Console by filtering a query and checking the Pages tab. Pick the strongest page to serve as the primary one based on rankings, links, and conversions. From there, either merge competing content into that page and 301 redirect the weaker URL, or re-optimize the secondary page around a different keyword and intent. Update internal links to reinforce the primary page, and add a canonical tag if you need to keep both URLs live.
Keyword Cannibalization Conclusion
Targeting the same keyword across multiple pages means competing with yourself, and that split weakens all of them.
The fix is straightforward: Pick a “winner” page for each topic and intent, then consolidate or retarget everything else. Focus each post on a distinct keyword and intent. Where topics overlap, a single comprehensive page will almost always outperform a cluster of thin ones.
The structure that makes this easiest to scale is pillar pages supported by topic clusters. This gives each page a clear role, keeping overlap manageable and providing Google with consistent signals as your site grows.
Start with a content audit and a keyword map. From there, the path forward is straightforward.
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Reddit is rolling out new Dynamic Product Ad features, including a shoppable Collection Ads format and Shopify integration, the company announced today.
What’s new.
Collection Ads: A new Dynamic Product Ad format that pairs a lifestyle hero image with shoppable product tiles in one carousel, bridging discovery and purchase. Early adopters following best practices are seeing an 8% ROAS lift.
Community and Deal overlays: Reddit-native labels like “Redditors’ Top Pick” and automatic discount callouts surface social proof and pricing signals without extra work from you.
Shopify integration: Now in alpha, this simplifies catalog and pixel setup for new DPA advertisers, automatically matching products to the right users and context.
The numbers. Reddit DPA delivered an average 91% higher ROAS year over year in Q4 2025. Liquid I.V. reports DPA already accounts for 33% of its total platform revenue and outperforms its other conversion campaigns by 40%.
Why now. Reddit has seen a 40% year-over-year increase in shopping conversations. Also, 84% of shoppers say they feel more confident in purchases after researching products on Reddit.
Why we care. The new tools, especially the Shopify integration, lower the barrier to getting started with Dynamic Product Ads. Reddit might still be viewed by some as an undervalued paid media channel, but there’s an opportunity to get in before competition and costs rise.
Bottom line. Reddit is increasingly a serious performance channel for ecommerce, and these tools make it easier to get started. If you’re not yet running DPA on Reddit, the combination of undervalued inventory and improving ad formats makes this a good time to test.
AI search citations favor a small set of formats. Listicles, articles, and product pages drive over half of all mentions across major LLMs, according to new Wix Studio AI Search Lab research analyzing 75,000 AI answers and more than 1 million citations across ChatGPT, Google AI Mode, and Perplexity.
The findings. Listicles led at 21.9% of citations, followed by articles (16.7%) and product pages (13.7%). Together, these three formats made up 52% of all AI citations.
Articles dominated informational queries, cited 2.7x more than other formats.
Listicles captured 40% of commercial-intent citations, nearly double any other type.
Why intent wins. Query intent — not industry or model — most strongly predicts which content gets cited. This pattern held across industries, from SaaS to health.
Informational queries skewed heavily toward articles (45.5%) and listicles (21.7%).
Commercial queries were led by listicles (40.9%).
Transactional and navigational queries favored product and category pages (around 40% combined).
Why we care. This research indicates that you want to map content types to user goals rather than just creating more content. Articles educate, listicles drive comparison, and product pages convert. Aligning content format with user intent could help you capture more AI citations and increase visibility.
Not all listicles perform equally. Third-party listicles accounted for 80.9% of citations in professional services, compared to 19.1% for self-promotional lists. That seems to indicate LLMs prefer neutral, editorial comparisons over brand-led rankings.
Model differences. All models favored listicles, but diverged after that.
ChatGPT leaned heavily into articles and informational content.
Google AI Mode showed the most balanced distribution.
Perplexity stood out, with 17% of citations coming from discussions like Reddit and forums.
Industry patterns. Content preferences shifted slightly by vertical:
SaaS and professional services over-indexed on listicles.
Health favored authoritative articles.
Ecommerce spread citations across listicles, articles, and category pages.
Home repair showed the most even distribution across formats.
A quiet but important policy update is coming to Google Shopping ads next month, requiring some merchants to verify their accounts before running ads featuring political content.
What’s changing. From April 16, merchants running Shopping ads with certain political content in nine countries will need to verify their Google Ads account as an election advertiser. Google will also outright prohibit some political Shopping ads in India.
The countries affected. Argentina, Australia, Chile, Israel, Mexico, New Zealand, South Africa, the United Kingdom, and the United States.
Why we care. Shopping ads aren’t typically associated with political advertising — this update signals that Google is broadening its election integrity efforts beyond search and display into commerce formats. Merchants selling politically themed merchandise, campaign materials, or other related products in the affected countries need to act before the April 16 deadline.
What to do now.
Review the updated policy language to determine if your Shopping ads feature content that falls under the new restrictions
If affected, apply for election advertiser verification through Google Ads before April 16 to avoid disruption to your campaigns
The bottom line.This affects a narrow but specific set of merchants — but the consequences of missing the deadline could mean ads being disapproved or accounts being flagged. If you sell anything with a political angle in the listed countries, check your eligibility now.
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