Tag Archive for: Analytics

Answer Engine Optimization (AEO) is one of the most important topics in search right now.

It’s about making sure your brand shows up inside AI-generated answers — not just on traditional SERPs.

As large language models (LLMs) like ChatGPT, Gemini, and Perplexity reshape discovery, AEO ensures your content gets mentioned and cited where buyers are asking questions.

But here’s the bigger truth: AEO is just one piece of a larger shift.

We’re entering the era of Search Everywhere.

Discovery no longer happens in a single Google results page.

It’s happening across AI chat, overviews, forums, video, and social.

And new data shows just how fast this shift is accelerating.

New research from Semrush predicts that LLM traffic will overtake traditional Google search by the end of 2027.

And our own data suggests that’s likely to be true.

In just the past three months, we’ve seen an 800% year-over-year increase in referrals from LLMs.

We’re seeing tens of millions of additional impressions in Google Search Console as AI Overviews reshape how Google displays answers.

If your brand isn’t adapting, you risk disappearing from the channels your audience is already using.

In this guide, I’ll explain:

• What AEO is and how it differs from SEO
• Why your existing SEO foundation still matters (and what to evolve)
• Practical steps to optimize for answer engines and drive measurable results

What Is AEO and Why Does It Matter?

Answer Engine Optimization (AEO) is the practice of structuring and publishing content so that AI systems — like Google AI Overviews, AI Mode, ChatGPT, and Perplexity — pull your brand directly into their answers.

But AEO goes beyond tweaking a few pages. It’s about making your brand part of the conversation when people ask questions.

That requires three things:

• Publishing content in the right places where AI tools actively crawl and cite
• Earning brand mentions across the web (even without a link)
• Ensuring technical accessibility so AI crawlers can actually parse your content

These engines don’t rank “10 blue links.” They generate answers.

Sometimes they cite sources. Sometimes they don’t. Either way, the goal is to give the searcher everything they need without leaving the interface.

That changes your job. With AEO, you’re not only optimizing for a click — you’re optimizing to shape the answer itself.

Why AEO Matters Now

Traditional search is still a traffic driver. That won’t change overnight.

But discovery is moving fast:

• Success used to mean ranking #1.
• Soon there may be no “#1 spot” at all.
• The win condition is becoming the recommended solution — the brand AI platforms trust enough to include.

The data tells the story:

ChatGPT reached 100 million users faster than any app in history. And as of February 2025, it now has more than 400 million weekly users.

Google’s AI Overviews now appear on billions of searches every month — at least 13% of all SERPs.

And they appear for more than half of the keywords we track at Backlinko:

Answer engines are influencing YOUR audience too. So it makes sense to start optimizing for them now.

How AEO and SEO Work Together

Let’s clear up the biggest question:

“Isn’t this just SEO with a new name?”

In many ways, yes. But there’s a reason everyone is talking about AEO right now.

If you’ve been confused by all the acronyms — AEO, GEO (Generative Engine Optimization), AIO (AI Optimization) — here’s the point:

They all reflect the same shift. Search is no longer only about rankings. It’s about visibility in AI-powered answers.

Terms like AEO, GEO (Generative Engine Optimization), and AIO (AI Optimization) have exploded in interest — because they reflect a real shift.

And with all the acronyms flying around, it can be tough to know who to listen to.

We’re not saying AEO replaces SEO.

But it does help reframe your strategy for how discovery works now — across AI tools, social platforms, and new surfaces beyond traditional search.

From Traditional SEO to Search Everywhere

That means there’s good news:

If you’ve invested in good SEO, you’re already a lot of the way there.

AEO builds on the foundation of great SEO:

• Creating high-quality content for your specific audience
• Making it easy for search engines to access and understand
• Earning credible mentions across the web

These same elements help AI engines decide which brands to reference.

But here’s the difference:

AI engines don’t work exactly like Google.

That means some of your tactics (and what you track) need to evolve.

So let’s walk through how to do that.

7-Step AEO Action Plan

We’re still in the early days of understanding exactly how AI engines pull and prioritize content.

But one thing is clear:

You need to adapt or reprioritize some traditional SEO tactics for Answer Engine Optimization.

The first three steps below cover overarching best practices for AEO.

Steps 4-7 cover optimizing content for answer engines specifically (and how to track your results).

Step 1. Nail the Basics of SEO

As I said earlier, good AEO is also generally good SEO. But not everything you do as part of your wider SEO strategy is as important for answer engine optimization.

I won’t go through all the fundamentals of SEO here. We do that in our guide to the SEO basics.

Let’s focus on what really matters for answer engines.

Make Your Site Easy to Read (for Bots)

• Crawlable and indexable: If AI tools can’t access your pages, you won’t show up in answers
• Fast and mobile-friendly: Slow, clunky sites hurt UX — and your chances of getting cited
• Secure (HTTPS): This is now table stakes, and it builds trust with users and AI systems
• Server-side rendering: Some AI crawlers still struggle with JavaScript, so use server-side rendering as opposed to client-side rendering where you can

Show You’re Worth Trusting (E-E-A-T)

AI wants trustworthy sources. That means showing E-E-A-T:

• Experience: Share real results, personal use, or firsthand knowledge
• Expertise: Stick to topics you truly know — and go deep
• Authority: Get quoted, guest post, or contribute to well-known sites
• Trust: Use real author bios, cite sources, and include reviews or testimonials

Step 2. Build Mentions and Co-Citations

AI systems don’t just look at backlinks to understand your authority. They pay attention to every mention of your brand across the web, even when those mentions don’t include a clickable link.

Backlinks are still important. But this changes how you should think about building your wider online presence.

Audit Your Current Mentions

Start by auditing where you’re currently mentioned. Search for your brand name, product names, and key team members across Google, social media, and industry forums.

Take note of what people are saying and where those conversations are happening.

You’ll probably find mentions you didn’t know existed. Some will be positive, others neutral, and a few might need your attention.

Also run your brand name and related terms through the AI tools themselves.

• Does Google’s AI Mode cite your brand as a source for relevant terms?
• Does ChatGPT know who your team members are?
• What kind of sentiment do the answers have when you just plainly ask the tools about your brand?

For a more in-depth sentiment analysis, use Semrush’s AI SEO Toolkit.

It’ll let you track your LLM visibility (a by-product of good AEO) in top tools compared to your rivals:

The tool compares your brand to your rivals in terms of AI visibility, market share, and sentiment:

And it’ll show you where your brand strengths are and where you can improve:

Keep Building Quality Backlinks

Just because mentions are more important than before with AEO, it doesn’t mean you should abandon traditional link building. Backlinks still matter for SEO, and they often lead to the kind of authoritative mentions that AI systems value.

But expand your focus beyond just getting links.

Aim to Build Co-Citations and Co-Occurences

There are a few different definitions out there of co-citation and co-occurence.

I’ll be honest: the definitions don’t matter as much as the implications. I’ve seen one source define co-citations as the exact thing another source calls co-occurence. So for this section, I’m just going to talk about what these are and why they matter, without getting bogged down in definitions.

The first important way to think of co-citations/co-occurences is simply the mention of one thing alongside another.

In the case of AEO, we’re usually talking about your brand or website being mentioned alongside a different website or topic/concept on another website.

For example, if your brand is Monday.com, you’ll pick up co-citations involving:

• Your competitors (ClickUp, Asana etc.)
• Key terms or categories associated with your business (like “project management software”)
• Specific concepts or questions related to what you do (e.g., “kanban boards” and “how to automate workflows”)

In Monday’s case, there are hundreds of pages out there that mention it alongside ClickUp and Asana in the context of “project management tools”:

This suggests to Google and other AI tools that Monday and ClickUp are both related to the term “project management tools” and are both popular providers of this kind of software.

The other common way to think about co-citations is mentions of your brand across different, often unrelated websites. For example, Monday being mentioned on Forbes and Zapier would be a co-citation involving them.

To sum it up:

• If two (or more) brands/websites are often mentioned alongside each other, AI tools will assume they are related (i.e., they’re competitors)
• If a brand is often mentioned in the context of a particular topic, concept, or industry, AI tools will assume the brand is related to those things (i.e., what you offer)
• If lots of different websites mention a particular brand, the AI tools will assume that brand is worth talking about (i.e., probably trustworthy)

Obviously, there’s a lot more to it, but this is a fairly basic overview of what’s going on.

How to Put This into Action

To build citations, co-citations, and co-occurences:

• Look for opportunities to get mentioned alongside your competitors. When publications write comparison articles or industry roundups, you want your name in that list. These co-citations help AI systems understand where you fit in your market.
• Participate in industry surveys and research studies. When analysts publish reports about your sector, being included gives you credibility (and any backlinks are a bonus).
• Get involved in relevant online communities. Answer questions on Reddit, contribute to LinkedIn discussions, and join industry-specific forums. These interactions create mentions in places where AI systems often look for authentic, community-driven insights.

The goal is to become a recognized voice in your space. The more often your brand appears in relevant contexts across the web, the more likely AI systems are to include you in their responses.

Step 3. Go Multi-Platform

Going beyond Google is something top SEOs have been telling us to do for a long time. But AI has made this an absolute must.

Platforms like Reddit, YouTube, and other user-generated content sites appear frequently in AI outputs.

So, a strong brand presence on these platforms could help you show up more often.

The benefits here are (at least) three-fold:

1. Being active on multiple platforms lets you reach your audience where they are. This helps you boost engagement, brand awareness, and, of course, drive more conversions.
2. AI tools don’t just look at Google search results. They pull from forums, social media, YouTube, and lots of other places beyond traditional SERPs.
3. Being active on multiple platforms means you’re less exposed to one particular algorithm or audience. Diversification is just good practice for a business.

Brian Dean did an excellent job of this when he was running Backlinko. That’s why you’ll see his videos appear in Google SERPs for ultra-competitive keywords like “how to do SEO”:

We’re taking our own advice here. In fact, it’s a big part of why we launched the Backlinko YouTube channel:

Here’s some quick-fire guidance for putting this into practice:

• People go to YouTube to learn how to do things, research products, and find solutions to their problems. This makes product reviews, tool comparisons, and in-depth tutorials great candidates for YouTube content.
• Podcast content and transcripts are beginning to surface in AI results (especially in Gemini). Building a presence here is a great opportunity to grab some AI visibility.
• TikTok and Instagram Reels reach younger audiences who increasingly use these apps for search. Short-form videos that answer common questions in your industry can drive discovery, and AI tools can also cite these in their responses to user questions.
• AI tools LOVE to cite Reddit as a source of user-generated answers (especially Google’s AI Overviews and AI Mode). To grow your presence on the platform, find subreddits where your target audience hangs out and share genuinely helpful advice when people ask questions related to your expertise. Don’t promote your business directly — focus on being useful first.
• LinkedIn works similarly to Reddit for B2B topics. Publish thoughtful posts and engage in relevant discussions to help establish your voice in professional circles. These interactions can then get picked up by AI systems looking for expert perspectives.

Step 4. Find Out What AI Platforms Are Citing for Your Niche

What’s a powerful way to understand both what to create and what topics to target?

To simply learn what AI tools are likely to include in their responses to questions that are relevant to your business.

Start by directly testing whether/how your content appears in AI tools right now. Go to ChatGPT, Claude, or Perplexity and ask questions that your content should answer.

In the example below, Backlinko is mentioned (great), but there’s also a YouTube video front and center. And forums are appearing too. These are places we might want to consider creating content or engaging with conversations.

As you do this for your brand, pay attention to the sources they cite:

• Are they commonly mentioning your competitors?
• What platforms do they tend to cite? (Reddit, YouTube etc.)
• What’s the sentiment of mentions of both your brand and your competitors?

As you do this, try different variations of the same question.

For example, you could ask “What’s the best email marketing software?”

Then try “Which email marketing tool should I use for my small business?”

Notice how the answers change and which sources get mentioned consistently.

In the example above, the first prompt mentioned MailerLite, which was absent in the list for small businesses. But the second prompt pushed Mailchimp to the top and mentioned three new options (Constant Contact, Brevo, and ActiveCampaign).

If you were MailerLite and trying to reach small businesses, you’d want to understand why you’re not being cited for that particular prompt.

You can automate this process with tools like Profound or Peec AI. These platforms run prompts at scale, helping you understand how and where your brand appears. But they can be pricey.

That’s why I recommend you spend some time running these prompts manually at first.

By the way:

This isn’t just important for “big brands” or those selling products. You can (and should) do this if you run a blog, local business website, or even a personal portfolio.

For example, consultants and freelancers will find these tools often cite marketplaces like Upwork and Dribbble. If you don’t have a profile on there, you’ll likely struggle to get much AI visibility.

And if you’re a local business owner, you’ll often find specific service and location pages appear in AI responses:

This is useful for understanding the types of content you should be focusing on for AEO. Now it’s time to decide what topics to focus on in your content.

Step 5. Answer Your Audience’s Questions

The way people search with AI tools is fundamentally different from how we use traditional Google search. This changes how you should plan your content.

Traditional SEO taught you to target specific keywords. You’d create a page optimized for “healthy meal prep ideas” and try to rank for that phrase.

But what happens when people are instead searching for “what to cook for dinner when I’m trying to lose weight”?

The answer might involve healthy meal prep as a solution, but it’s a completely different prompt (not a search) that gets to that answer (not a SERP).

When you run these queries through Google’s AI Mode, you see two totally different sets of sources and content types.

For the “healthy meal prep ideas” query (which is a perfectly valid and searchable term), the focus is listicles, single recipes, and YouTube videos. And the format is categories (bowls, wraps, and sandwiches etc.) with specific recipes:

But for “what to cook for dinner when I’m trying to lose weight,” the sources are primarily lists, forum results, or articles specifically around weight loss.

In this case, the format of the answer is largely broad tips for cooking healthily and then some general cooking styles or meal types, rather than specific recipes:

As more users realize they can use conversational language to make their searches, longer queries will become more common. This makes this kind of intent analysis critical.

These longer, more specific queries represent huge opportunities. Most companies aren’t creating content that answers these detailed questions.

The more specific the question, the more likely you are to show up when AI systems look for authoritative answers. You want to own the long-tail queries that relate directly to your product or expertise.

But:

You obviously can’t reasonably expect to create content for every single long-tail query out there. So how do you approach this in an efficient way?

How to Choose the Questions to Answer

Start by listening to the actual questions your customers ask.

Check your customer support tickets, sales calls, and user feedback. These real questions from real people often make the best content topics — because they’re the same kinds of questions people will ask these AI tools.

Don’t have any customers? No problem.

Use community platforms to find these conversational queries. Reddit, Quora, and industry forums are goldmines for discovering how people actually talk about problems in your space.

Step 6. Structure Your Content for Answer Engines

AI systems process information differently than humans do. They break content into chunks and analyze how those pieces relate to each other.

Think of it like featured snippets but more granular, and for much more than just direct questions.

This means the way you structure your content directly impacts whether AI systems can understand and cite it effectively.

One Idea per Paragraph

Keep your paragraphs short and focused on one main idea.

When you stuff multiple concepts into a single paragraph, you make it harder for AI systems to extract the specific information they need.

Also avoid burying important information in the middle of long sentences or paragraphs. Front-load your key points so they’re easy to find and extract.

And guess what?

It also makes it easier for your human readers to understand too. So it’s a win-win.

Use Clear Headings

Use clear headings and subheadings to organize your content logically.

Think of these as signposts that help both readers and LLMs navigate your information. And make sure your content immediately under the headings logically ties to the heading itself.

For example, look at the headings in this section. Then read the first sentence under each one.

Notice how they’re all clearly linked?

This is a common technique when trying to rank for featured snippets. You’d have an H2 with some content that immediately answers the question…

…and this would rank for the featured snippet for that query:

This is still a valid strategy for traditional search. But for AEO, you need to have this mindset throughout your content.

Don’t make every H2 be a question (this will quickly end up looking over-optimized). But do make sure the content that follows your (logical) headings is clearly linked to the heading itself.

Break Up Complex Topics into Digestible Sections

If you’re explaining a complex or multi-step process, use numbered steps and clear transitions between each part.

This makes it easier for AI systems to pull out individual steps when someone asks for specific instructions. And it’ll make it much easier for your readers to follow.

Also write clear, concise summaries for complex topics. AI systems often look for these kinds of digestible explanations when they need to quickly convey information to users.

Include Quotes and Clear Statements

Include direct quotes and clear statements that AI systems can easily extract.

Why is this worth your time?

Because pages with quotes or statistics have been shown to have 30-40% higher visibility in AI answers.

So instead of saying “Email marketing could be an effective channel for your business,” write “Email marketing generates an average ROI of $42 for every dollar spent.”

Use Schema Markup

Schema markup gives you another way to structure information for machines. This code helps systems understand what type of content you’re presenting.

For example, FAQ schema tells algorithms that you’re answering common questions. HowTo schema identifies step-by-step instructions.

You don’t need to be a developer to add schema markup. Many content management systems (like WordPress) have plugins that handle this automatically.

Make It Scannable

Use formatting like bold text to highlight important facts or conclusions and make it easier for readers to skim your content. This helps both human readers and AI systems identify the most important information quickly.

This has always been a big focus of content on Backlinko. We use lots of images to convey our most important points and add clarity through visualizations:

And we use clear headings to make our articles easy to follow:

The goal is to make your content as accessible as possible to both humans and machines. Well-structured content performs better across all types of search and discovery.

And if your content is enjoyable to engage with, it’s probably going to do a better job of converting users into customers as well.

Step 7. Track Your Visibility in LLMs

How often are tools like ChatGPT, Perplexity, or Gemini mentioning your brand?

If you’re not tracking this yet — you should be.

Tracking your visibility in AI-generated responses helps you understand what’s working and where you need to focus your efforts.

But where do you start? And what should you track?

Manual Testing as a Starting Point

Start with manual testing. This is the simplest way to see how you’re performing right now.

Ask the same questions across different AI platforms, like ChatGPT, Claude, Perplexity, and Google (both AI Mode and AI Overviews). Take screenshots of the responses and note which sources get cited.

Do this regularly, and you’ll start to see patterns in which types of content get mentioned and how your visibility changes over time.

Honestly though: you’re going to struggle to get a lot of meaningful data doing this manually. And it’s not scalable. Plus, so much of what an AI tool outputs to a user depends on the previous context, like:

• Past conversations
• Previous prompts within the same conversation
• Project or chat settings

This makes it challenging to get truly accurate data by yourself. This is really more of a “feel” test that, in the absence of dedicated tools, can provide a very rough idea of how answer engines perceive your brand.

Use LLM Tracking Tools

For more comprehensive tracking, dedicated tools can automate this process.

Platforms like Semrush Enterprise AIO help you track your brand’s visibility across AI platforms like ChatGPT, Claude, and Google’s AI Overviews.

It shows you exactly where you stand against competitors and gives you actionable steps to improve.

Competitive Rankings is my favorite feature. Instead of guessing why competitors might rank better in AI responses, you get actual data showing mention frequency and context.

Another option is Ziptie.dev. It’s not the most polished tool yet, but they’re doing some really interesting work — especially around surfacing unlinked mentions across AI outputs.

If you already have Semrush, then the Organic Research report within the SEO Toolkit does provide some tracking for Google AI Overviews specifically.

You can track which keywords you (or your competitors) rank for that have an AI Overview on the SERP. If you don’t currently appear in the overview, that’s a keyword worth targeting.

Tracking the keywords you do rank for in these AIOs over time can help you gauge the performance of your AEO strategy.

Why Talk to Your Boss (or Clients) About AEO?

You’ve seen the steps. Now you need a story.

AEO isn’t just a tactical shift — it’s a way to explain what’s changing in search without resorting to hype.

AEO helps you frame those changes clearly:

• Traditional SEO still works
• Your past investments are still paying off
• But the bar is higher now
• Visibility means more than rankings
• Your brand needs to be mentioned, cited, and trusted across every channel

AEO gives you the framework to explain what’s changing and how to stay ahead of it.

You Need to Start Now to Stay Visible

This space is evolving fast. New capabilities are rolling out monthly.

The key is to start tracking now so that you can benchmark where you are and spot new opportunities as AI search matures.

Grow your presence by adding a AEO approach on top of your SEO efforts:

• Continue optimizing for strong rankings and authority (AI still leans on this)
• But now, prioritize content and signals that AI engines are more likely to reference directly

Want to learn more about where the world of search is heading? Check out our video with Backlinko’s founder Brian Dean. We dive into how search habits are changing and how you can build a resilient, multi-channel brand.

The post Answer Engine Optimization (AEO): How to Win in AI Search appeared first on Backlinko.

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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.

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.

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.

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:

How are they doing this?

Marie Martens, co-founder of Tally, writes:

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.

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

For page-by-page analysis, you can use Google’s PageSpeed Insights.

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:

1. Make sure your brand exists in these systems
2. 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.

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).

Watch for imbalances.

Strong mentions but weak citations? Focus on authority signals from Playbook 2.

Cited often but rarely mentioned? Ramp up your community and sentiment work.

Also: watch your competitors. When someone jumps in AI visibility, reverse-engineer what changed.

New PR coverage? More reviews? A pricing update?

The brands winning AI search aren’t waiting for perfect strategies. They’re testing, learning, and adjusting faster than their competition.

The AI Visibility Window is Open

In addition to listing your brand, AI platforms influence what buyers see, trust, and choose.

And right now, AI visibility is anyone’s game. Only a few brands in each industry have cracked the code of being both mentioned and cited.

That means even established giants can be outmaneuvered if you move faster on AI strategy.

So while competitors debate whether AI search matters, you can build the presence that captures tomorrow’s buyers.

The Seen & Trusted Framework gives you the direction.

Run both playbooks. At once.

The post AI Search Strategy: The Seen & Trusted Brand Framework appeared first on Backlinko.

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