BacklinkoModify

2026-09-10 05:24:18

You’re looking at a page that should be performing better, and you want answers fast.

Maybe it’s a product page that slipped in rankings, a competitor’s post outranking yours, or a client page that isn’t converting.

You don’t need a full site crawl to start diagnosing.

SEOquake’s free Chrome extension audits any page and tells you what’s working (and what isn’t), all without leaving the page.

In this tutorial, I’ll walk you through an SEOquake workflow that takes you from triage to diagnosis in just three minutes:

• Triage: Open SEOquake’s Quick View pop-up for an instant SEO snapshot (1 minute)
• Diagnose & Decide: Switch to the report for deeper analysis and exports. Then, decide whether it’s a page edit or a wider issue that needs a full site audit (2 minutes).

What Is SEOquake?

SEOquake is Semrush’s free SEO Chrome extension for auditing any webpage’s technical signals, on-page SEO, linking strategy, and core Semrush metrics.

Install the extension, open any page, and click the SEOquake icon in your browser for faster, easier SEO research and analysis.

The SEOquake extension provides two ways to audit:

• The Quick View pop-up provides both page- and domain-level metrics for a fast snapshot of performance and on-page and technical issues
• The Full Report goes deeper with 28 on-page SEO audit checks and guidance on what to do for each one

Results are color-coded throughout every report, so you can quickly diagnose pages and prioritize fixes.

Here’s what each color tells you:

• Green is a pass
• Orange is a warning — worth a look, but not an emergency
• Red is a failure — fix these issues first
• Blue provides useful context for checks that don’t have a pass/fail answer

We’ll use these color cues to stay focused throughout the audit.

Note: To show you how SEOquake’s three-minute page audit works, I’ll be analyzing a real product page from UnderFit, a men’s undershirt brand. Want to audit along with me? Install SEOquake for free.

Triage Any Page’s SEO Issues with the Quick View Pop-Up (Minute One)

Triage is the first minute of SEOquake’s three-minute page audit.

In 60 seconds or fewer, you’ll answer this question: Is anything obviously wrong with this page?

The Quick View pop-up has five tabs:

• Page Info: Title tag, meta description, canonical, and robots.txt status
• Content: Heading structure, keyword density, and image alt text
• Audit: Technical and page-level checks color-coded by severity
• Schema: Detected structured data types and social preview information
• Semrush: Domain-level organic search traffic, traffic cost, and backlinks, plus page-level URL backlinks

I’ll focus on three tabs: Page Info, Audit, and Content (about 20 seconds per tab) to get a fast read on UnderFit’s product page.

Start with Page Info for Core SEO Elements

SEOquake’s Page Info tab is a great starting point because it provides a quick overview of key SEO elements.

I open the pop-up on UnderFit’s page and assess the results.

This is a quick scan, so I don’t overthink it.

The five things I prioritize:

• Title tag and meta description: Are both present? Are the character counts within range?
• Canonical: Is it pointing to the URL I want indexed?
• Robots.txt: Does the file allow crawlers to access the page?
• Schema.org markup: Is structured data detected and valid?
• Link, image, and heading counts: Do the numbers fit the page type?

Then, I quickly scan for anything marked red — or anything that doesn’t line up with on-page SEO best practices.

On UnderFit’s page, SEOquake flagged the Schema.org markup in red.

The reason points to JSON-LD errors, which means search engines may not parse the structured data correctly.

For a product page, that’s a real concern.

Invalid product schema can make the page ineligible for product rich results, including price, availability, and ratings in search.

So, that’s going straight onto my list to check in the Full Report.

The rest is mostly clean aside from two elements:

• Title tag (65 chars): Present but flagged orange — slightly outside the ideal range
• Meta description (182 chars): Present but flagged orange — outside the ideal range

These aren’t blockers, but I note them down.

Scan the Audit Tab for Technical Problems

Some issues don’t surface in Page Info, so I go to the Audit tab next.

SEOquake’s Audit checks for technical SEO essentials, including:

• Crawling & Indexation
• Security & Protocol
• Page Configuration

On the UnderFit page, everything is green or orange.

That usually means there’s no obvious technical emergency. But orange warnings still require a judgment call.

I’ll evaluate anything in orange against what I know about the site and the SEO risk.

For this page:

• XML sitemap: Flagged as missing, so I’ll verify the sitemap exists and includes this URL
• Meta robots: Flagged orange for review. A conflicting directive can block indexation, so it’s worth a closer look.
• Hreflang: Shows zero tags, but I wouldn’t worry about that for a U.S.-only store

With technical issues noted, I move on to the Content tab.

Review the Content Tab for Structure and Keywords

Next, I open the Content tab to check whether the page is structured clearly.

Content structure helps users navigate the page and can make it easier for search engines and AI systems to interpret it, so this is worth double-checking.

In the Content tab, I check:

• Title tag and meta description: Are both present, and are the character counts within range?
• Heading and word counts: Do the H1/H2/H3 distribution and total word count fit what I’d expect for a product page?
• Heading Structure: Is there one clear H1, and do the headings follow a logical hierarchy?
• Keyword Density: Do the top terms match the page topic, and do important terms appear in the title, meta description, and H1?
• Images: How many images are missing alt text or flagged as oversized?

Seeing UnderFit’s heading structure laid out makes me pause.

The H1 appears way down the page, and the hierarchy jumps levels throughout — going from H1 straight to H4, for example.

That’s worth reviewing for accessibility, reader navigation, and overall content clarity.

The image summary also flags that 36 images are missing alt text.

Since images do a lot of selling on a product page, I’ll look into this further.

Missing alt text gives search engines less image information and makes the page less accessible to shoppers.

Neither is a blocker, but I’ll check both in the next step of the audit: Diagnose.

Related reading: 11 Chrome Extensions for SEO That Will Streamline Your Workflow

Diagnose Issues with the SEOquake Full Report (Minutes 2–3)

In the first minute of the audit, I found a few issues on UnderFit’s product page that I want to investigate in SEOquake’s Full Report.

In the next two minutes, I’ll diagnose the cause of each flag. Then I’ll decide whether I can fix the issues at the page level or need to run a site-wide audit.

I click “View Full Report” at the top of SEOquake’s Quick View pop-up to open the panels.

The Full Report has six panels:

• Page Info: Basic page signals, technical snapshot, and public Semrush metrics
• Audit: Technical SEO checks with fix guidance
• Keyword Density: Key terms by phrase, length, density, and prominence
• Links: Internal and external links metrics
• Compare: URL or domain comparison
• Semrush: Domain and page data and metrics

I’ll walk you through a few of the Full Report panels to give you a quick overview.

But when you run your own audit, go directly to the reports that match the flags you found.

Dig Deeper into Technical Issues in the Full Audit Panel

Since SEOquake identified a few technical issues, I’ll start with the Audit panel.

The full Audit covers crawlability, indexation, meta robots, mobile compliance, sitemaps, and schema.

It gives me a clearer sense of how serious each flag is and what to do about it.

I use the menu to jump straight to the flags rather than scrolling through everything:

For each flag, I’ll do the following:

• Click the “i” to see what the check measures
• Check the color-coded status to see what SEOquake found
• Expand “Tips” for guidance on what to do next

For UnderFit, I check the XML sitemap warning first.

Under Technical Compliance, SEOquake confirms that “https://underfit.com/sitemap.xml” exists but is not clearly linked.

That’s definitely going on my fix list, as it could affect this page’s discoverability.

Triage also flagged UnderFit’s Schema.org markup as invalid, so I’ll check the Schema section next.

SEOquake shows green, meaning it’s detected markup on the page.

I need to see if that’s valid, so I click “Test in Rich Results.”

This opens Google’s Rich Results Test, which runs the page automatically.

It detects 3 invalid items.

That confirms what triage suggested, so it goes onto my fix list, too.

After that, I work through the remaining flags the same way — including the heading hierarchy issues and missing alt text from triage — adding actionable items to my list.

Pro tip: Once you’ve worked through the flags, export the full audit as a PDF to share with a developer or client, or to keep a record before you make changes.

Spot Topic Coverage Gaps in the Keyword Density Panel

If a page isn’t performing the way you’d expect, Keyword Density is worth a quick look.

It tells you which terms appear most frequently in the page’s visible content and whether the topic coverage looks right for the page type.

For UnderFit, I start with the Keyword Cloud to get a quick read on what the page is about.

The core product terms are the most prominent: undershirt, fit, fabric, v-neck, cotton. That’s exactly what I’d expect for this page type.

Side note: If you’re auditing a competitor, SEOquake’s Keyword Density report is a fast way to see which topics they’re covering that you aren’t.

Then I dig into the keyword tables for more detail.

The “Found in” column confirms those terms are showing up in the right places — “undershirt” appears in the meta description and H1, while “fit” and “underfit” both appear in the title tag.

Next, I scan the multi-word phrase tables for any missing or off-topic phrases.

Most reinforce the page’s topic: “the invisible undershirt,” “v-neck undershirt,” and “built to last.”

Some odd phrases, like “zoom zoom,” come from interactive elements and image labels in the HTML rather than the main product copy.

That’s normal — tools like SEOquake can pick up text from buttons, image labels, and other page elements, so widget text sometimes appears in the data.

Check Link Health in the Links Panel

Broken, vague, or unhelpful links can make it harder for search engines and users to navigate pages effectively.

The Links panel shows the status of all internal and external links on the page, including dofollow/nofollow, anchor text, and HTTP response codes.

On UnderFit’s page, the report shows one broken internal link and 37 broken external links.

So I go to the External Links table to dig deeper — they’re mostly JavaScript links.

I’ll add those to the fix list to confirm whether they’re functional or actually problematic.

Pro tip: If you’re sharing findings with a developer or content team, export the table as a CSV. It provides them with the affected URLs, anchor text, follow status, and response codes in a single file.

Decide on a Page Fix or Site Audit Using the Semrush Panel

I’ve now worked through the triage flags and have a list of issues to address on UnderFit’s product page.

But before I fix anything, I need to decide whether each issue is specific to this page or points to a broader site problem. The Semrush panel gives me the additional context I need to make that call.

By default, it shows domain- and page-level metrics, including:

• Semrush Rank: The domain’s organic visibility ranking
• SE Traffic: Estimated monthly organic search traffic for the domain
• Root Domain Backlinks: The domain’s total backlink profile
• URL backlinks: Total backlinks to the page

Optionally, I can connect SEOquake to my Semrush account to unlock deeper data.

This includes Traffic Analytics data (engagement metrics such as bounce rate, visit duration, and channel-level traffic) and detailed backlink reports.

Pro tip: To connect your Semrush account to SEOquake, click the gear icon in the top-right corner of the Quick View pop-up, then click “Connect to Semrush” or access the Integrations tab in Settings.

A weak page on a strong domain — like UnderFit, where the domain has solid traffic and a reasonable backlink profile — usually points to a content or page-level problem.

But some flags aren’t cleanly at the page level.

The XML sitemap warning, for example, is almost always site-wide, and JSON-LD errors might repeat across all product templates.

Those aren’t problems I can confirm or fix with SEOquake alone, so I’ll move to a full site crawl with a tool like Semrush’s Site Audit.

Diagnose Any Page in 3 Minutes with SEOquake

As you’ve seen, the SEOquake Chrome extension lets you audit any page in three minutes and walk away knowing exactly what’s working, what’s broken, and what to do next.

Install SEOquake from the Chrome Web Store and run your first audit today:

• Triage using the Quick View pop-up to catch obvious problems
• Diagnose the flags in the Full Report to understand what’s wrong
• Decide whether it’s a page fix or a site-wide issue
• Escalate to Semrush Site Audit when the problem is bigger than the page

To crawl your full site, start a free 14-day Semrush trial and get Site Audit access for 100 URLs per month.

The post How to Audit Any Page in Under 3 Minutes with SEOquake appeared first on Backlinko.

2026-08-20 07:53:56

Website marketing in 2026 no longer works the way it used to.

Traditionally, it meant promoting your site to attract visitors and turn them into customers.

Your website is still central. But much of the customer journey now happens beyond it.

Before someone visits your site, they’ve often already checked AI tools, Reddit threads, and social media.

Those sources influence how they see you.

This means if you’re not visible and credible there, many people won’t reach your site.

But if people encounter you in those places, they arrive on your website with more context and trust.

That means better traffic with a higher intent to convert.

So, how do you reach people across a wider journey, and make your site work harder when they arrive?

Below are seven website marketing strategies that help you do that. Each one comes with action steps you can implement immediately.

(With related reading for each website marketing example when you’re ready to go deeper.)

1. Position Your Website for AI + Human Discovery

Over 65% of buyers now use AI to research options before visiting a website, according to Clutch Research.

Which means by the time they arrive, many have already formed an opinion about your brand.

Your job is to make sure that opinion is accurate AND that your site backs it up.

Related reading: Search Everywhere Optimization Guide

Engineer How the Internet Describes Your Brand

You don’t have to leave your online reputation to chance. You can influence how people talk about your brand online.

That starts with being clear on your brand messaging strategy.

This is the documented language you want consistently associated with your business.

The words, claims, and positioning you want repeated.

At a minimum, it should define:

• A positioning statement
• Primary benefits in plain language
• Differentiators
• Approved phrases and keywords
• Language to avoid

Here’s Slack’s old brand guidelines to give you an idea.

It spells out brand elements such as the company’s positioning, values, and the problem the product solves.

This creates a shared language for everyone in the company.

Yours doesn’t need to be that extensive.

Start with the essentials above and build from there.

What really matters here is consistency.

Everyone who speaks about your brand should have access to this document. From marketing and sales to leadership and partners.

They must use it whenever they write, present, post, pitch, or publish anything about your company.

That repetition ties your brand to the right category in people’s minds.

It also reinforces that same association in large language models (LLMs).

Take ecommerce brand, UnderFit.

One of their repeated benefits is that their undershirt stays tucked all day.

That exact phrasing appears consistently across their site and off it. In reviews, roundups, and community mentions.

And when I asked Google AI Mode about shirts that stay tucked all day, UnderFit appeared in the results.

Those repeated mentions everywhere made the benefit easy for the AI to associate with the brand.

Ready to build your brand messaging document?

Start by finding out what AI is already saying about you so you can reinforce it or fix it.

Open any LLM and ask the same questions your prospects are asking, such as:

• What is “YOUR BRAND”?
• What are the pros and cons of “YOUR PRODUCT?”
• Is “YOUR BRAND” legit?

What you’ll see is your current AI narrative.

Now, evaluate it:

• Is this the category you want to own?
• Are these the benefits you want to emphasize?
• Is anything inaccurate, outdated, or missing?

Once you know where you stand, you’ll know what to address.

Top tip: A quicker way is to use the Semrush AI Visibility Toolkit. You get the full picture at a glance, rather than piecing it together from multiple tools one by one.

Like this:

Structure Your Website for Easy Reading and Parsing

Showing up well across the internet is one part of the job.

But you must pair that with a website that’s easy for humans and LLMs to interpret.

Start with solid website architecture.

That means:

• Clear topical hubs
• Logical internal linking
• No orphan pages or PDFs
• Consistent category structures

Like this:

(Learn more in our website architecture guide.)

Beyond architecture, design each page so its purpose is obvious at a glance.

For example, use skimmable sections, tables where helpful, and clear headings.

Related reading: On-Page SEO: The Definitive Guide

At the code level, use semantic HTML.

(Or proper heading tags, lists, and section elements).

These structural signals define your content hierarchy.

They make it explicit to search engines and LLMs how your page is organized. And what each section represents.

Finally, continue following core on-page SEO best practices.

Strong user experience and properly implemented structured data still matter.

These make it easier for search engines — and likely AI systems — see what each page is about.

If you need a refresher, our definitive guide to On-Page SEO covers everything in detail.

Publish and Surface Proof Assets

Proof assets are the evidence behind your claims, including:

• Case studies
• Reviews
• Certifications
• Methodology breakdowns

They influence how people and AI systems judge your credibility.

The key is to publish strong proof that influences decisions in your niche, as this can vary.

For example, in SaaS, measurable case studies, uptime data, and security documentation often carry weight.

But in ecommerce, buyers look closely at product quality details, reviews, and return policies.

If you’re unsure what proof matters, search your brand name on Google and review the People Also Ask results.

Those questions reveal what buyers want clarified before they decide.

If you want a broader view, use tools like Semrush.

You’ll instantly see repeated questions and concerns at scale, which makes it easier to decide which proof assets to prioritize.

Related reading: If you want a clearer picture of what your audience actually worries about, our audience research guide covers it in detail.

2. Use Videos to Build Trust

There’s something about video that text can’t easily replicate.

It lets people feel like they know you. That familiarity becomes the foundation of trust in you and in your business.

By the time they visit your site, you’re no longer a stranger.

That can make all the difference between cautious browsing and confident conversion.

Establish Credibility Through Long-Form Videos

Long-form usually means YouTube.

But it can also mean webinars, interviews, hosted training, and deep educational dives.

Here’s how to approach it.

First, define what role YouTube plays in your business.

That shapes the kind of content you make, which matters far more than how you make it.

For most brands, the highest-value videos help buyers evaluate before they engage or buy.

That might include:

• Product walkthroughs
• Detailed demos
• Comparison videos
• Q&As addressing objections and questions

Like YNAB’s YouTube channel.

Now if your goal is building authority, lean into educational content.

That’s largely what we do at Backlinko’s YouTube channel.

We create tutorials and explainers to demonstrate expertise.

Compare that to an ecommerce brand like Vat19.

Their content is highly visual and product-focused. That’s because their goal is to sell their products.

One important point:

Don’t leave YouTube in a silo.

Your videos should link back to your site and direct viewers to pages that align with the video topic.

You can do this by referencing the links in the video itself.

Or adding relevant links in the descriptions.

The way food channel Pick Up Limes does it.

Side note: For affiliate or niche publishers, this is especially important. With search traffic declining, YouTube can act as a discovery engine. It can send qualified viewers directly to targeted pages on your niche website. Or straight to the publisher’s site, when appropriate.

Want to take action now?

If you don’t have a channel yet, start with competitor research.

Go Incognito and search for topics in your niche on YouTube.

There, analyze:

• Which videos get the most views relative to subscriber size
• Which topics are repeated across competitors
• What formats perform best (tutorial, opinion, comparison, breakdown)
• What the comments reveal about audience confusion or unmet demand

This gives you two things: signal and gap.

Signals show you what people care about.

Gaps show you where you can differentiate.

From there, you’ll have a clearer picture of the kind of YouTube content that makes sense for your brand.

Plus, how it should support your website strategy.

And when you’re ready to go deeper, we have YouTube guides covering everything from channel positioning to keyword research and content planning.

Reinforce Familiarity with Short Form Videos

Short-form videos build familiarity through repetition.

These are the clips on TikTok, Instagram Reels, YouTube Shorts, and LinkedIn.

Start with one platform.

Instagram Reels, YouTube Shorts, and TikTok are the most practical entry points.

All you need are your phone camera and the in-app editor.

Ignore the advanced setups for now.

In the beginning, momentum matters more than production.

Now the question in everyone’s mind:

What should I talk about?

Start by answering real customer questions.

Get them from sales calls, support emails, search queries, and recurring objections.

Or even better.

Jot down every question you hear about your product or service over the next week.

By the end of seven days, you’ll have a list of topics to talk about.

Not just any topic. But valuable topics that your buyers are genuinely interested in.

Related reading: How to Create a Content Calendar

3. Capture Google Discover Traffic

Google Discover is Google’s personalized content feed. The cards you see when you open the Google app on your phone.

Curated by Google, it often feels more like editorially crafted content than a traditional search result.

It’s worth optimizing for because it can send serious traffic spikes.

Like when Backlinko had two articles in Discover in a single month.

Create Google Discover-Focused Content

Google Discover shows content based on your interests and browsing behavior.

So the articles that appear usually match topics you’ve recently searched for or engaged with.

For example, if you’ve been searching for home workouts lately, Google might recommend an article like:

“The 15-Minute Routine That Replaced My Gym Membership”

So, how do you get your content picked up?

The good news: As long as your page is indexed, it can appear in Discover.

But getting your content picked up usually comes down to a few specific criteria:

• High-quality images at 1200px minimum
• Enabling max-image-preview:large in your meta tags
• Content that’s timely or tied to high-interest topics

(From what I’ve seen, Discover also tends to favor established sites with strong domain authority and publishing track records.)

To get started with Discover, add a dedicated “Discover lane” to your content calendar.

This is content for interest-based discovery.

These topics typically:

• Tie into something happening now
• Respond to a recent update or shift
• Address a debate gaining traction in your niche
• Connect to a rising interest in your category

When writing for Discover, the content needs a clear angle with stakes.

It should also feel timely. Not necessarily breaking news, but connected to something current.

Like a new regulation hitting your industry, a brewing controversy, or a trend your audience is already tracking.

That sense of “why this matters now” aligns with how Discover picks content.

This doesn’t have to mean creating content from scratch.

Often, this is about reframing.

Say you have content on “Email Marketing for Ecommerce.”

You could reposition that into:

“How Apple’s Privacy Updates Are Changing Email Marketing for Ecommerce.”

Here’s something you can do right now:

Open your recent posts and upcoming content calendar.

Pick one article and assess whether you can tie it to a recent change or development.

Then run it through the checklist below to evaluate its Discover potential.

Package Your Content for a Click in the Feed

In Discover, packaging decides everything.

Your featured image and headline determine whether anyone ever reaches your article.

That’s because when users scroll through the Discover feed, you’re competing for attention in a fast-moving, visual environment.

If your packaging doesn’t stop the scroll, the brilliance of your content is irrelevant. Discover traffic will not reach your page.

So when creating content for Discover, here’s what to do:

Package every Discover-targeted piece of content with a headline that conveys tension, stakes, or newness.

And pair it with a high-quality featured image with a clear focal point

Think of it like an ad.

After all, you’re not just competing with your usual competitors.

You’re competing with everything that person cares about: tech, health, finance, celebrity news, sport, and politics.

From experience, what makes me click often feels like news.

For example, I recently clicked on a Forbes article in my feed because the headline triggered a bit of FOMO.

And the image was something I hadn’t seen before.

If you want to develop a feel for this now, try this:

Open your Google Discover feed and scroll for five minutes.

Notice what makes you stop.

Look at:

• The structure of the headlines
• The clarity and composition of the images
• The tone (urgent, explanatory, surprising, contrarian)

Then, look at one of your recent articles.

Is it packaged to compete?

Would the headline hold its own in that feed?

Would the image stop your scroll?

That’s the editorial eye your Discover content needs if you want more chances to be curated by Google.

4. Optimize for Conversions in a Zero-Click World

“Zero-click” means users get answers directly from other places without visiting your website.

This was already the case with featured snippets. Those boxed answers that appear at the top of Google results

But it’s now more pronounced with people getting answers directly from large language models.

That’s why conversion rate is even more critical for website marketing in 2026.

The people who arrive at your site are already pre-educated and further along in their decision-making process.

You want to make sure that every visitor who does arrive has every reason to act.

Prioritize Revenue per Visitor

With your visitor already pre-educated, they don’t need to browse through many pages before making a decision.

That means the moment they land, your job is to get them to the action.

Whether that’s booking a demo, talking to sales, or completing a purchase.

And the good news?

Conversion rate is something you can improve directly. Starting today.

The first step is to audit your site for barriers between interest and purchase.

The most impactful way to start is by reducing friction and anxiety.

Friction makes the process harder, like slow page speed or confusing navigation.

Anxiety makes the visitor hesitate, like a lack of trust signals or unclear pricing.

That’s where the audit comes in. Here’s how to run a quick one right now.

First, clarify your primary conversion goal.

Is it demo bookings? Trial sign-ups? Or product purchases?

Then, audit your site around that goal.

If your main objective is demo bookings, check:

• Is the demo link visible across your site?
• Is it clearly differentiated from other CTAs?
• Does the demo page explain what happens next?

Now, if you operate in a category with skeptical buyers like finance, anxiety is naturally higher.

That means you need stronger assurance signals, such as security certifications, compliance documentation, and a clear process.

Whenever I audit sites, I always start with the pages closest to the sale.

That’s because these are the pages where the decision is already forming.

Plus, where friction or anxiety does the most damage.

For most ecommerce sites, that’s typically product pages and the cart.

For B2B SaaS, that could include the pricing, demo, and about page.

Use Email to Reinforce Buying Decisions

A buyer’s website experience shouldn’t end when they leave your site.

Keep that “conversation” going using email.

You do that through behavioral targeting.

That means triggering emails based on what someone did on your site.

• Viewed a pricing page? Send a product comparison email with social proof
• Browsed a product multiple times? Send a reminder with a limited-time offer
• Started a trial? Send onboarding tips tied to the features they’ve used

In short:

You follow up while their intent is still warm.

That increases relevance and improves the likelihood that interest turns into action.

Here’s how to put this in motion.

First, check whether your email provider supports behavioral triggers tied to site activity.

Many major email platforms support this natively, including:

• Klaviyo
• ActiveCampaign
• HubSpot

Once connected, you can trigger emails based on actions like product views, pricing page visits, trial signups, or post-purchase behavior.

For example, I bought something from Currys (a UK electronics retailer) in December.

In January, I received an email offering £10 off a future purchase.

That’s post-purchase behavioral targeting, tied to something I actually did.

It felt…personal.

And that’s not just a nice feeling. Psychologists call it reciprocity:

When a brand gives you something of value (like a personalized discount), you’re subtly more inclined to return the favor.

If behavioral targeting is new territory for you, start with one trigger.

Pick a high-intent action — say, a pricing page visit — and create a follow-up email tied to that behavior.

Keep it simple. Like a short message featuring a relevant case study or addressing a common objection.

Get that one working and expand from there.

5. Lead with Humans Behind Your Brand

The internet is awash with AI content.

Which means there’s a human premium right now, and you can claim it.

Put a recognizable voice, face, and personality behind your brand, and you stop being a stranger.

That familiarity reduces perceived risk. And reduced risk makes decisions easier.

Which means by the time someone reaches your website, the balance may already be tilting your way.

Publish Thought Leadership Content

Thought leadership content shows your brand’s clear point of view on how things should be done in your category.

(And it can also add original viewpoints that AI systems may reference.)

That often includes:

• Named frameworks
• Category-defining opinions
• A distinctive point of view

This content can live under your company name in blog posts, reports, and social threads.

But it becomes more powerful when expressed through a visible human.

They might post on LinkedIn, front your YouTube channel, or represent your company on podcasts and panels.

In doing so, they scale the human side of your brand.

To get this rolling today, start by identifying who will represent your brand publicly.

Often, it’s the founder or CEO.

Richard Branson is The Virgin Group. Noah Kagan is AppSumo. Brian Dean was closely linked with his company Backlinko, before (and after) its acquisition.

But it doesn’t have to be the founder.

Any employee can be a brand advocate if they:

• Have strong domain knowledge
• Can communicate clearly
• Are willing to be visible

For example, Leigh McKenzie, Director of Online Visibility at Semrush, is the public face of Backlinko today.

He shows up on LinkedIn with Backlinko content, appears on the YouTube channel, and signs the newsletter.

One more important point:

Make sure there’s an obvious association between the individual and your company.

For example, their podcast appearances should reference their role. Or their LinkedIn bio should mention your brand.

Rita Cidre’s LinkedIn profile does this well.

It clearly shows her role as Semrush’s Head of Customer Education & Community..

6. Build Visibility on Third-Party Sites

Most of the trust that brings someone to your website was built somewhere else.

In a forum thread, a Reddit discussion, or a review platform.

The same is true for AI systems.

The confidence it requires to mention your brand is shaped by what they find in those spaces.

Which means website marketing in 2026 isn’t just about your turf.

It’s also showing up on other people’s.

Because if the conversation about you happens elsewhere — and it does — you want to be part of it.

Related reading: Reddit Marketing in 3 Hours/Week

Support Niche Communities

Niche communities run on trust.

And trust is earned by showing up as a contributor, not a promoter.

The brands that do this well aren’t there to sell. They’re there to help and support the community.

The first step is to identify the communities that influence buying decisions in your category.

These might include Reddit subreddits, Discord servers, Slack communities, private industry forums, Facebook groups, or local groups.

For example, in personal finance, communities like Bogleheads are deeply influential.

Conversations there shape how people think about investing strategies, platforms, and risk.

If you’re a local business, your equivalent might be a city-specific subreddit. Or a local Facebook group.

Platforms like Nextdoor — where people actively ask for recommendations and share experiences — are also worth considering,

Related reading: Local SEO: The Definitive Guide (+ Free Toolkit)

Once you’ve identified 3-4 communities, spend the first week observing.

Notice:

• The language people use
• The tone that’s accepted
• How brands are treated
• What kind of promotion gets ignored or criticized
• Who the respected contributors are

Then, after a week or two, start participating as a competent professional in your field.

• Answer questions without linking to your product
• Clarify misconceptions calmly
• Share perspective from experience, even when the best choice isn’t yours

Eric Bandholz, founder of Beardbrand, does this well.

He shows up in grooming and men’s lifestyle communities, weighing in thoughtfully.

Often without mentioning his brand at all.

And whenever there’s a question, complaint, or doubt about Beardbrand specifically, he’s often there to answer and support the community.

7. Repurpose High-Performing Ideas

AI has made content repurposing much easier.

The danger is that you start multiplying everything without a clear direction.

The thing is, repurposing works best when you reinforce the right ideas.

So focus on depth and resonance rather than being everywhere for the sake of it.

Find the Ideas Worth Multiplying

Intentional repurposing starts with a proven idea. One that’s already shown it connects.

You can find those ideas in several ways:

• A blog post driving unusually strong conversions
• A YouTube video with higher retention than average
• A LinkedIn post generating saves and discussion
• A newsletter topic with abnormal click-through rates

Once you’ve found one, you can repurpose it in different directions.

You can turn long-form content into many formats. Like from a comprehensive report into industry-specific articles and social media posts

Or, one short-form content into many formats.

This is when you post a lot of social media content throughout the week.

And then you repurpose the idea or topic of the post that got the most likes.

This way, you’re letting your audience validate the idea before you invest heavily in it.

Dan Koe does this well.

(He talks about it in this video)

He frequently tests ideas on X.

The post that generates the strongest responses becomes a newsletter.

That newsletter evolves into YouTube videos and other content assets.

Now, finding the right idea is only half of it.

The other half is platform focus.

Choose two to four primary platforms.

For some teams, that might be:

• Blog + LinkedIn
• YouTube + Email
• Shorts + Long-form video + Email

At Backlinko, we often start with long-form blog content.

Then we selectively expand strong topics into YouTube, LinkedIn, and email.

Once you know what to expand and where to take it, use AI to accelerate execution.

Claude Artifacts is particularly useful.

For example, you can upload an article and get it to turn the article into a 10-slide carousel.

From there, your options open up.

• Take one slide and turn it into an image for a LinkedIn post
• Edit the full set into an Instagram carousel
• Or pull individual frames as talking points for a short-form video script

You’ll still need to customize it to your brand and make it your own. But it’s a great way to get you off a blank page fast.

Automate Your Website Marketing

This is the reality of website marketing in 2026:

Some of your most valuable visibility and trust-building work happens on third-party sites.

The trust you build on those platforms can highly influence how your website performs. Design your strategy around that, and you’ll see stronger results.

The good news is you don’t have to do all of this manually.

Our AI automation guide shows you which marketing workflows to automate and how you can set them up.

The post 7 Website Marketing Strategies for 2026 appeared first on Backlinko.

2026-08-19 19:04:49

You can have an impressive website, great content, and a seamless user experience.

But you might still be invisible to search engines and AI tools.

That’s because Google and ChatGPT don’t just look at your website. They also look at what other credible sources say about your brand to understand who you are and whether they should rank or recommend you.

Every time a trusted publication, community, or expert mentions your brand, it builds credibility for your business.

In this guide, I’ll cover why brand mentions matter in more detail, and I’ll give you four ways to earn more high-value mentions to boost your visibility.

I’ll also show how to track these mentions and report ROI to leadership.

What Are Brand Mentions?

A brand mention is any reference to your brand by a third party. It could be in an article, a community thread, a YouTube video, a podcast, or anywhere else online.

You can build two main kinds of brand mentions: linked and unlinked mentions.

Linked mentions include a clickable link to your website along with your brand name. For example, this article mentions Fitbit and links out to the brand’s product page.

Unlinked mentions refer to your brand without any links. For example, this Reddit user mentions Fitbit but doesn’t add any links to the brand’s website.

The key difference: Linked mentions can pass authority and referral traffic to your domain. Unlinked mentions help establish brand context for search engines and AI systems, without the direct traffic benefit. Both are useful.

Web vs. LLM Brand Mentions

Brand mentions on the web aren’t the same as getting brand mentions in AI search. Like when ChatGPT mentions your brand in its response to a user’s question.

For starters, LLM mentions vary for individual users based on their personal context, browsing history, and other factors. And they don’t exist outside of those conversations — nobody else sees those specific mentions.

In contrast, a web mention is static and shows up the same way to potentially thousands of users.

Getting mentions in AI responses is more like the goal, while getting brand mentions across the web is one of the ways you can reach that goal. Since the more consistently you appear in trusted, relevant sources, the more likely you are to show up in AI search results.

So in this guide, we’re focusing on web brand mentions. For more on getting mentions in LLMs specifically, check out this article on building AI mentions.

Why Brand Mentions Matter Now More Than Ever

Brand mentions have always played a part in gaining online visibility. But here’s why they matter even more right now:

They Signal Authority to Search Engines

Search engines evaluate your brand in part based on what the wider web says about you.

They consider who mentions you, in what context, and whether those mentions are consistent.

For example, Google recognizes brands as distinct entities in its Knowledge Graph, a database that maps relationships between people, companies, topics, and concepts.

Brand mentions are one of the primary ways Google builds and refines that entity-level understanding of your brand.

They Expand Your Visibility to New Audiences

When a website, podcast, or community thread references your brand, you become visible to a whole new audience.

That’s how brand mentions can help you reach people who aren’t actively looking for you.

Take this video by The Budget Dermatologist as an example.

The video recommends better-value alternatives to a well-known skincare product. It mentions a brand called Timeless and explains why it’s a good alternative.

In the comments, people share how they purchased Timeless after discovering the brand through this video.

In this example, the brand mentions in the video can provide useful signals for search engines and LLMs, while also influencing purchases from an audience the brand might not be directly targeting.

They Help LLMs Understand and Feature Your Brand

When LLMs recommend your brand, they’re largely drawing on language from trusted sources where your brand is mentioned across the web.

Here’s proof of this in action:

An article on the Six Minute Mile website describes the Adidas Adizero Evo SL with wording like “elite-level performance in a lightweight, no-frills package” and saying they’re ideal for “workouts and long runs.”

When Claude talks about the same shoe, the LLMs describe it in almost identical terms.

So, brand mentions on websites can be repeated almost word for word by AI tools in their responses to users.

What Makes a Good Brand Mention?

Three factors determine the quality of a brand mention: authority, relevance, and specificity.

Authority

Authority refers to the credibility of the source where you’re mentioned.

Being mentioned in highly credible sources matters because:

• It shows up in places your customers are likely to be looking
• AI systems and search engines may put more weight on mentions from these sources

A feature in a trusted industry publication sends a stronger signal than a vague reference on a low-traffic blog with no topical relevance.

Let’s look at two good examples of high-authority mentions for SteelSeries, a gaming accessories brand.

This brand was mentioned in:

• Wirecutter article: Highly credible website with in-depth reviews
• IGN’s YouTube video: Popular channel with high topical relevance

Relevance

Relevance for brand mentions works on two levels:

• Context: Are you mentioned in the relevant topical context?
• Source: Is the source that’s mentioning you also connected to your industry?

If you’re mentioned in an article covering a theme you want to be known for, it strengthens your topical authority.

It’s also important that the source where you’re mentioned is also relevant to your industry.

Reformation, a fashion brand, represents a great example of this.

The brand is mentioned in articles on outfit ideas and sustainable fashion — themes that align with its positioning. Both these mentions also come from fashion-centric publications.

Specificity

Specificity is the third critical aspect for the quality of brand mentions.

A genuinely helpful mention shares specific insights like:

• Who you are
• What you do best
• Why customers should choose you over others

That’s exactly what this Reddit post does for Descript, a video editing tool:

The post discusses the tool’s standout features and specific use cases.

As a result, search engines and AI systems get clear, usable signals about what Descript does and what it’s best for. These kinds of brand mentions help them understand when and where to rank or recommend Descript to users.

4 Tactics to Earn Valuable Brand Mentions

Now that we know how brand mentions can benefit your business, let’s turn to the bigger question: How do you earn brand mentions consistently to reap all these rewards?

Here are four tactics for building brand mentions regardless of where you’re starting from.

1. Create Data-Driven, Citable Content

Original research is one of the most reliable ways to earn brand mentions. And it keeps working long after you hit publish.

When you present original research around a question your industry is actively debating, mentioning your brand becomes the only way to cite that data.

SparkToro research, in partnership with Datos, presents a great case study here.

The research answered a question that is hotly debated in the SEO community right now: what share of searches happens on Google compared to other platforms?

The key finding was that Google still accounts for just under 74% of searches, while ChatGPT has less than 3% share. This counterintuitive finding gave SEOs and marketers evidence to anchor their arguments.

As a result, practitioners cited this study and mentioned Sparktoro across multiple platforms, including news articles and Reddit threads.

(A bonus of this method is that you also often pick up quality backlinks, too.)

How to Do This for Your Brand

First, go where your audience actively discusses their concerns and queries. This could be Reddit threads, industry Slack groups, and even your own sales calls.

Look for the questions that keep coming up in these conversations, but for which there’s no evidence-backed answer. This is the question you’re going to answer with your research.

You can conduct research in a few different ways:

• Run a survey: Works best when you have an existing audience or customer base to poll
• Build an index: Works best when you have proprietary platform or product data that others can’t access
• Conduct a controlled experiment: Works best when you can isolate a single variable and document it repeatably
• Synthesize third-party datasets: Works best when public data exists but hasn’t been combined or reframed in a useful way

Before you hit publish, work backward and think about what would make your research asset easy for anyone to reference.

Asana’s State of AI at Work report is a great example of packaging original research well for citations.

Let’s break down why it’s so easy to cite:

• Standalone headline: Find one specific insight or data point that summarizes your research. Asana leads with a striking one: Organizations aren’t fixing broken work; they are automating the chaos.

  

• Section-level findings: Each chapter or section should have its own quotable claims. Asana’s report has five chapters, each focused on one theme with key insights around it.

  

• Clear methodology: Your approach and sample size tell people how to attribute your research, like, “according to a survey of 300 B2B marketers.”

Thanks to this structure, Asana’s research data is easy to reference.

As a result, it has earned brand mentions from news outlets, industry publications, and LinkedIn users.

The report also gained 87 linked mentions from domains ranging from trade press to high-authority industry blogs.

Pro tip: Make your visuals easy to share. If your data is easy to screenshot, people can organically share the research and mention your brand.

2. Partner With Complementary Brands on Co-Created Assets

When two or more brands build a campaign together, every conversation about it mentions the brands involved in the same context.

AEO Conf 2026, co-hosted by Graphite, AirOps, and Webflow, shows how this plays out in practice.

This campaign brought together three complementary brands in the AI and web publishing space. Each brand serves a different slice of the same target audience.

Before the event, promotional posts from speakers and brand profiles generated mentions and visibility among each other’s audiences.

That meant that people who followed only one of these brands discovered the other two.

After the event, attendees posted recaps sharing their key takeaways from the conference. And each of these posts mentioned Graphite, AirOps, and Webflow together.

When the same brands are repeatedly mentioned together across independent sources, like in this case, LLMs start to associate them with the same topic.

This is called co-occurrence, and it’s one of the ways web mentions translate into LLM visibility.

How to Do This for Your Brand

Aside from hosting in-person events, you can also create co-branded giveaway campaigns.

They generate a high volume of brand mentions quickly in two ways:

• Audience participation
• Press coverage

Plus, a strategic PR push can generate more authoritative brand mentions beyond social media.

Poppi’s partnership with PopUp Bagels shows how this is done. The two brands co-created a limited-edition cream cheese flavor.

Creators and customers then posted about it on social media, tagging both brands.

The brands also ran PR around the launch. News outlets covered this campaign directly, creating high-quality mentions for each brand.

3. Feature Other Brands and Experts in Your Content

When you feature an expert or a brand in your content, they have a reason to share it with their audience. That earns you a brand mention and visibility in a community you didn’t originally show up in.

Slate, a platform designed for social media managers, nailed this through multiple interviews with Zaria Parvez, a leading voice in this space.

The brand first posted about a webinar highlighting Zaria’s insights.

Thanks to their long-term relationship, Zaria also shared a snippet of another interview she did with Slate. This post went out to her audience of 150,000+ LinkedIn followers, creating a high-value mention for the brand.

How to Do This for Your Brand

You can feature experts and brands in your content in a few different ways.

For starters, expert roundups are great for curating firsthand expertise from multiple contributors.

Be sure to pick a theme where your audience really needs expert insights, like our guide on choosing a digital marketing agency.

This piece features insights and advice from experienced marketing leaders for one specific question: how to choose a good marketing agency.

Benchmark reports are another good format for featuring experts.

Instead of just presenting data, invite experts to add their commentary and contextualize why the findings matter.

That context makes the report more useful. And it gives each contributor a reason to share it because their perspective is part of the story.

I found a great example of this in G2’s State of AI Sales Intelligence in Prospecting.

The report features experts from nine companies, including ZoomInfo, Cognism, and 6sense.

Each contributor shares practitioner-level insight to explain G2’s findings.

Interview series and podcasts work well because the format gives guests more room to demonstrate their expertise.

And when your content reflects someone’s expertise, they’re more likely to share it with their audience.

Klue, a competitive intelligence platform, takes this approach with their podcast “Coffee and Compete.”

Every episode is titled after the guest’s expertise.

For example, “Starting Your Win-Loss Program w/ Dylan D’Urso” is framed as an episode about Dylan’s expertise.

As a result, Dylan shares the premise of his episode and mentions Klue organically.

What matters across all these formats is how you feature each contributor.

A one-line quote buried in a list gives someone little reason to share your content. But a feature that genuinely puts the expert’s insight at the center of your piece makes it easier for them to want to share it.

Pro tip: Make it easy for contributors to share your content. Send them a ready-made asset, such as a pull-quote graphic or a short clip, that they can post directly on their socials.

4. Contribute Subject Matter Expertise Through Digital PR

Contributing your expertise to someone else’s platform is another great way to earn high-value brand mentions.

When a credible journalist quotes you or a creator interviews you and talks about your brand, your brand mention benefits from that outlet’s credibility.

An example of this comes from Kailey Bradt, CEO of Sonsie Skin, appearing on the Shopify Masters podcast.

Her brand was mentioned in the YouTube video description, chapter markers, show notes, and the Shopify blog. These are all useful potential sources for search engines and AI tools to pull from to understand what your brand does.

How to Do This for Your Brand

When pitching your expertise to any outlet, offer something genuinely useful to their audience, like:

• A counterintuitive finding
• A practitioner’s take on an emerging trend
• A specific framework that challenges the status quo

For example, Ryan Anderson, CEO of Filevine, shared a specific practitioner playbook on the SaaStr podcast.

This brand mention comes from sharing what SaaStr’s audience wants to learn rather than what Filevine sells.

Remember that the outlet you pitch matters as much as your angle.

Chasing the biggest publications in your industry might not always be the right move.

A smaller, highly relevant outlet that your audience actively engages with can be just as effective (and sometimes more so).

Before pitching, map where your audience actually goes for information: whether that’s podcasts, publications, YouTube channels, or other similar channels.

Dive deeper: Audience Research: Stop Guessing What Your Buyers Care About

Natalie Marcotullio, Head of Growth and Product Marketing at Navattic, provides a good example here.

She consistently shows up on specific channels that her audience (B2B SaaS marketers and PMMs) relies on, like:

• In-person conferences like Above the Fold
• YouTube channels like Product Marketing Adventures
• Webinars and content assets with Navattic’s complementary brands

Each placement reached the same audience through a different context. And each one generated a high-value brand mention for Navattic.

How to Track and Report on Brand Mentions

To build a repeatable strategy for brand mentions that actually improve your online visibility, you need to know:

• Which tactics are paying off
• Where you’re losing ground against competitors
• How your brand mentions lead to business outcomes

Let’s look at three methods for tracking brand mentions across the web and in AI search.

I’ll also show you how to report on each one to present meaningful findings to your leadership team.

Google Alerts and Manual Searches

Google Alerts is the simplest place to start — and it’s free.

Go to Google Alerts and set up alerts for your brand name, including common misspellings, your product names, executive names, and your top competitors.

Set the frequency to “At most once a day” so you get a single daily digest instead of a constant stream of emails.

An important note: Google Alerts can miss many mentions.

The tool may not reliably pick up community forums, subreddits, or niche industry publications.

So, supplement these daily reports with a weekly manual pass. Search your brand name across Google News, relevant subreddits, and YouTube to catch what slips through.

How to Report This

Use Google Alerts data to track how many mentions you’re picking up each week or month, and note which sources and contexts are driving them.

Keep a separate list of your highest-authority placements.

A simple monthly summary might read: “This month we earned [X] brand mentions across [channels]. The highest-authority placement was [source], which reaches an estimated [audience size] monthly readers.”

That gives stakeholders a consistent number to track over time.

Plus, it creates a story about where your brand is showing up rather than how often.

Social Listening Tools

If you want to understand where and how your brand is mentioned, you need a social media monitoring tool.

Tools like Semrush Brand Monitoring, Brandwatch, or Mention track conversations across social networks, forums, blogs, news sites, and review platforms.

They give you context like:

• What people are saying about you
• Whether the tone is positive or negative
• How your presence compares to competitors

With the Semrush Brand Monitoring app, you can filter brand mentions by keywords, sentiments, source (blog, social media, etc.), language, and other criteria.

How to Report This

Use social listening data to build a monthly summary of core metrics such as mention volume, sentiment, and source breakdown.

Your report could present insights like: “This month we earned [X] brand mentions across [channels]. [Y%] of mentions carried positive sentiment, with the highest concentration coming from [blogs/forums/social].”

If you ran a campaign during the reporting period, flag whether mention volume spiked and whether sentiment shifted alongside it to show how your work contributed to those results.

Dive deeper: See how your brand’s mention volume compares with competitors over time by tracking your share of voice.

Backlink and Mention Monitoring Tools

Use a tool like Semrush to track when new pages link to your brand.

Semrush’s Backlinks tool shows which pages link to your site. But not all of these are brand mentions, since some links might use non-branded anchor text.

To find backlinks that are also brand mentions, filter the results by anchor text.

Click “Add filter,” select “Anchor,” and enter your brand name.

This will show you only the backlinks where your brand name is in the anchor text.

How to Report This

Focus on two things when reporting this data:

• The authority of the sites mentioning you
• The diversity of your mention sources

A mention from a single high-authority publication is valuable. But a pattern of mentions across many credible sites can have a stronger impact on your AI search visibility.

A useful way to frame this in reporting is something like: “This quarter, we earned [X] new mentions across [Y] unique domains, including [list two or three notable ones].”

Make Your Brand Impossible to Ignore with Strategic Mentions

Brand mentions have always been a signal of trust.

When you show up consistently in trusted publications, expert roundups, and community conversations, search engines and AI tools learn to recognize and recommend you in the right context.

But the optimization process doesn’t stop there. Our AI optimization guide shares more tactical advice on how to show up consistently in AI search.

The post Brand Mentions in 2026: How to Earn and Track Them Across the Web appeared first on Backlinko.

2026-08-14 05:23:04

Your brand could be ranking #1 on Google, but still be invisible to AI.

Absent from conversations your customers are having with large language models (LLMs) about your category.

Or worse, showing up inaccurately, with outdated or incorrect information that’ll hurt your sales.

Without prompt tracking, you’d never know.

Prompt tracking (sometimes called LLM visibility tracking) is the practice of monitoring how your brand shows up in AI answers over time, through mentions or citations.

It’s different from traditional SEO rank tracking, which tells you where your URLs appear on search engine results pages (SERPS) for specific keywords.

With rank tracking, you ask, “How close are we to position 1?”

But LLMs don’t answer questions with a static list of 10 blue links.

They pull from massive amounts of information to generate a unique response every time, tailored to the user and the context of the conversation.

You can even ask the same question twice and get two different answers.

In this search experience, it matters less whether your brand is mentioned first, and more that it says true positive things about you to the right people — consistently, across many runs of similar prompts.

But without a system to monitor it, you’re flying blind.

Prompt tracking gives you the directional intelligence to spot AI visibility gaps — the queries you’re consistently not showing up for — and close them.

This guide shows you exactly how. You’ll walk away with a free tracking template, a step-by-step system, and two real-world expert setups you can steal.

Free template: Download our prompt tracking spreadsheet to start understanding your brand’s AI visibility across LLMs ASAP.

Why Brands Need to Track Prompts

According to a study from Orbit Media, 55% of US internet users rely on AI as their primary or frequent research tool. Thirty-two percent use it for product recommendations.

Translation: A growing share of buyers are learning about you in AI tools. Without ever visiting your website.

Overall AI visibility scores tell you whether you’re showing up. Prompt tracking tells you where and how.

It can help you understand:

• The types of questions you’re showing up for and where they fall on the customer journey (ToFu, MoFu, or BoFu?)
• The questions your competitors are pushing you out of (and your share of voice on important topics)
• The sentiment around your brand mentions
• The questions you’re getting cited for, but not recommended (sometimes called ghost ranking)

This level of data helps you spot specific trends and gaps in your AI visibility over time.

Then, you can prioritize exactly what to fix.

Take Gong, the sales call intelligence tool.

Their AI visibility score is a respectable 65.

Free tool: Get your own score using Backlinko’s free AI visibility score checker.

They show up for prompts at all three stages of the funnel.

With prompt tracking, Gong can focus on conversations most likely to drive revenue and stop spending time and money tracking ones that won’t.

If their mention rate stays low on key BoFu topics over time, that’s a signal to update on-site or third-party content.

They can also see which relevant topics competitors are owning while they’re absent.

For example, Gong’s Engage product helps with lead generation.

But Salesforce and Hubspot consistently own these prompts.

This tells Gong two things:

1. Their audience isn’t aware of their lead generation use case
2. They need more content around it to train AI platforms to mention them

Sentiment prompts like ‘Is Gong worth the price?’ and prompts that surface ghost ranking can reveal similar trends and gaps.

The important thing is to track data over time.

AI answers are non-deterministic. The same prompt can return different brands across different runs.

Regular, repeated tracking is how you get meaningful signals you can act on.

When LLM Prompt Tracking Isn’t Worth It (and When It Is)

Prompt tracking isn’t for everyone.

Plenty of businesses spend time and budget on it and still walk away with data they can’t use.

Prompt tracking is NOT worth it when:

• Your audience isn’t using AI to find solutions in your category
• Your site isn’t set up to be crawled by AI
• You don’t publish content regularly
• You’re not looking for competitive or brand narrative insights
• You need a single KPI to report upward
• You don’t have bandwidth to act on insights

TL;DR: Prompt tracking yields valuable insights — but only if you’re set up to act on them.

If you are set up, the returns can be significant.

Take Gong, the sales call intelligence tool. It’s a great candidate for prompt tracking.

It has a full content engine that publishes across multiple channels (blog, reports, video, media coverage, audio, and more).

They compete in a crowded category where comparison prompts are common.

And they serve a buyer (sales leaders) who increasingly uses AI to evaluate software.

(Seventy-one percent of B2B software buyers now rely on AI chatbots for product research, according to G2, up from 60% in 2025.)

On the other hand, a local HVAC company that gets all its leads from Google Business Profile (GBP) and word-of-mouth doesn’t need prompt tracking. At least not yet.

Even if AI is overlooking them, building a content engine from scratch just to fix that isn’t a realistic investment.

Building Your Prompt Set: What to Include and Why

Don’t try to track every possible prompt your customers could be using.

Instead, focus on prompts you actually care about getting mentioned or cited in.

They should map directly to your product offering, audience pain points, and moments close to purchase.

Types of Prompts to Include

Tracking these four types of prompts over time will yield the most helpful insights:

• Evaluation prompts: “Best tool for x use case” and specific feature queries
• Reputation prompts: “Is x product worth the price?”
• Comparison prompts: “Alternatives to x product,” “x tool vs. x tool,” “best x tools”
• Gap prompts: Priority topics your competitors are pushing you out of

The first three are focused on understanding how your product is being recommended in buying conversations.

The last one is about understanding your competitive landscape and where you could improve.

Pro tip: Don’t just track one prompt for each type. Looking at answers for a single prompt is just noise. Reviewing a cluster of prompts over time is a real signal.

Margaret Kapitany, Offsite SEO Lead at Hootsuite, shares how she focuses her prompt set:

The prompts worth tracking are the ones that most closely mirror how a potential buyer would actually ask their AI for help, especially close to a purchase decision. For me at Hootsuite, that means prompts that cover comparison, evaluation, and recommendation queries from a social media manager or CMO, phrased the way they’d talk to a colleague or trusted industry peer.

What that looks like in practice at Hootsuite:

Where to Find Prompts Worth Tracking

Find prompts wherever you normally go to learn about your audience.

To find prompts worth tracking, look at:

• Keyword research: Commercial and Transactional intent queries like “best x software”
• Google’s “People also ask” (PAA) boxes: Comparison and evaluation questions like “Best alternative to x” or “does x integrate with y”
• Perplexity’s related questions: Similar to PAA, comparison and evaluation questions
• Reddit, Quora, Facebook Groups in your industry: Repeated questions, especially ones that compare options or express frustration
• Sales call transcripts: Repeated questions asked right before or during a purchase decision
• Semrush prompt suggestions for your brand: Queries tied to buying decisions that you or your competitors are showing up for

Pro tip: For every question you uncover, do a quick gut check: “If someone asked an LLM this, would I want to see my brand show up? Would I be upset if it didn’t?” If the answer is “Yes, and yes,” keep it.

Let’s return to Gong as an example of how to find prompts.

Keyword research shows me the questions people are asking Google about my category, how popular they are, and the language they use.

If I use a tool like Semrush, I can filter to Commercial or Transactional intent keywords (the ones labeled “C” or “T”). And add the most popular and relevant ones to my prompt tracker.

Then, I can dig through Reddit forums my audience frequents to find repeated frustrations and buyer queries.

I would take “Sales enablement tech stack suggestions,” “AI tools for sales enablement.” I’d ignore the queries about SMBs or startups because those aren’t Gong’s target audience.

Next, I’d extract the comparison or evaluative prompts Perplexity surfaces when I prompt it with terms related to my business.

For Gong, I used “best conversational insights tool for sales” to find some good candidates:

• Gong vs. Chorus (or any other competitor on this list)
• Which conversational insights tool has the best ROI for enterprises?

Pro tip: When writing prompts, don’t agonize over exact wording the way you would with keywords. LLMs cluster semantically similar queries together. Track a few natural variants, like “best sales enablement software” and “top sales enablement tools.”

These sources are solid, but you’re still inferring and collecting them manually is slow.

Semrush’s AI Visibility tool gives you what none of these sources can: real LLM prompt volume data.

The exact prompts your audience runs in LLMs and how often they’re using them, ranked by frequency.

This grounds your prompt set in real demand that’s always up to date.

You can be sure you’re tracking questions your users are actually asking.

From this list for Gong, I’d choose to track prompts under “AI-Driven Sales Enablement” and “Sales Coaching and Enablement Tools,” as they’re BoFu queries related to my product.

Organize By Product or Use Case

To build your first prompt set, start small.

All you need is 20-30 prompts over 4-6 broad categories that align with your product offering or use cases.

Ensure every category includes a mix of your four types of high-value prompts and add them as tags.

For a B2B SaaS company like Asana, this prompt tracking setup could look like the following:

Pro tip: Use branded prompts only for comparison and reputation tracking, as they can inflate your visibility score. Keep the rest of your prompt set unbranded so you actually learn where you’re getting found, not just where you’re already known.

This setup lets you easily see which categories you’re winning and losing in over time.

Or, what types of answers you need to do a better job of showing up in.

Example: Hootsuite Prompt Set

You may decide to add more tags or organize your prompts in a different way as you expand.

For example, Margaret uses multiple different tags — not just categories — for her prompt set for Hootsuite.

We’ve built out prompts in three ways:

• Funnel stages, with most of our attention on conversion
• Our target industries, with tailored terminology
• Intent type: comparative, evaluative, integrative (e.g. “works with X tool”), and problem-led (“how do I solve Y”)

Almost all of our prompts carry multiple tags, e.g., BoFu + Healthcare + Evaluative. That tagging is what lets me slice the data later.

When leadership asks for numbers, she can report on which industries Hootsuite is most visible in, or cross-reference visibility for BoFu prompts with direct traffic trends.

Margaret’s setup shows an important lesson: However you build your prompt set, structure it so you can answer the questions you’ll want to ask later.

How to Track Prompts: Step-by-Step Process

All you need to get started with prompt tracking is a spreadsheet and 30 minutes a week.

Free template: Download our Prompt Tracking Template by Backlinko to follow along with the steps below.

Step 1. Set Up Your Tracking Sheet

With our tracker, you can log the following for each prompt:

• The prompt itself
• Category
• Tags (e.g., type, industry)
• The LLM you’re testing it in
• Whether your brand was mentioned (yes/no)
• Whether you were cited (yes/no)
• Sentiment of the mention (positive, neutral, negative)
• Competitors mentioned
• The date

Customize it to whatever makes sense for your business.

Step 2. Run Each Prompt Across Multiple LLMs

Different LLMs pull from different sources, so answers will be different across all of them.

Tracking prompts from only one LLM won’t give you a full picture of your brand’s presence in AI search.

Ideally track prompts in all major LLMs, including:

• ChatGPT
• Gemini
• Perplexity
• Claude

If you need to save time, review Presenc AI’s 2026 platform demographics report to identify which LLMs your audience actually uses and focus on those.

Pro tip: Make sure you’re using a temporary chat to run your prompts. Your regular chat window will serve you an answer that takes into account everything it knows about you from past conversations. You want to track what an LLM might recommend to anyone, not just you specifically.

Step 3. Run Each Prompt at Least Twice Per Session (Optional)

Answers will vary run to run. So if you have the time, run each prompt 2-3 times per session for more reliable data.

You’ll catch when your brand shows up one out of three times (a 33% mention rate).

If you don’t have time, don’t worry. You’ll still see trends over time.

Step 4. Log Competitor Mentions, Too

Competitor data is half the value of prompt tracking.

Seeing a competitor get mentioned consistently in a category you’re performing less well in is a signal.

Maybe they published a new comparison page. Or were included in an influential report.

Look into their strategy to see what they’ve done to improve and learn from them.

Step 5. Track Weekly. Action on Data Monthly.

Regular prompt monitoring on a weekly basis is often enough to catch shifts.

It gives LLMs enough time to crawl and learn from new or updated content.

But don’t make any decisions with only one week of data.

A single week of low visibility in a category could be a fluke. Four weeks of it is a trend worth acting on.

(We’ll cover what to do when you spot one in the “How to Read and Act on Prompt Data” section below).

In this example, Asana shows up with a low citation rate for two LLMs only one week out of four.

Rushing to fix that ASAP could turn out to be a waste of time.

Step 6. Upgrade to an Automated Prompt Tracking Tool

Manual LLM visibility tracking is a great way to validate that you can actually get some useful insights from the practice.

But if you want to grow your prompt set beyond 20-30 prompts, it’s going to start taking much longer.

A tool like Semrush can help you move faster and suggest actionable opportunities based on your data.

To get started, open the Visibility Overview dashboard, enter your domain, and click “Check AI Visibility.”

You’ll see a summary of how often LLMs mention your brand, which competitors are mentioned alongside you, and a breakdown across each LLM.

Scroll down for a list of prompts where you’re already getting mentioned. Click “Opportunities” to see your gap prompts.

Click “Monitor” on the prompts you want to include in your prompt set.

Pick the LLMs you want to monitor, paste in your prompts, and hit “Start Tracking.”

You can add tags by intent, topic, or campaign so you can slice the data later.

Further reading: See how the top AI visibility tools stack up on pricing, LLM coverage, and reporting features.

Example: How an Agency Tracks Prompts for E-Commerce Brands

When you’re tracking hundreds of prompts across multiple clients, a basic spreadsheet won’t be enough.

Jonny Nastor, Founder & Head of Strategy at Digital Commerce Partners, knows this firsthand.

He built a prompt tracking map based on the theory that most buyers ask LLMs about a specific job they need done or task to accomplish. Then filter by their specific situation (a.k.a. constraints).

For example, when shopping for smart doorbells, a buyer might search “best video doorbell with no monthly subscription fees.”

The job to be done is “best doorbell.” The constraint is cost (low fees or no subscription).

He calls it a “Constraint Map.” Every intersection of job and constraint in the map becomes a prompt.

He gets ideas for constraints from keyword modifier data. In Semrush, you can see these in the Keyword Magic Tool.

He also pairs each prompt with search volume data to roughly understand its popularity and prioritize accordingly.

He runs his prompts across ChatGPT, Perplexity, and Gemini automatically through their APIs to log how each one responds.

Depending on the client’s needs, he tracks one or all of the following AI visibility metrics for each prompt:

• How many times the brand was cited by LLMs
• The brand’s recommendation (or mention) rate
• Instances of ghost ranking

For this client, it was only citations on Bing’s AI search.

For each metric, he watches for trends over time, not drawing any conclusions based on one week of data.

He also audits content readiness with a mix of the following (again, depending on client needs):

• If a page exists to address the prompt
• If that page links directly to the specific products that answer the prompt
• If product details (attributes) like price, dimensions, compatibility, etc., are listed on the page
• If the content on the page is extractable by an LLM (e.g., no bot-blocking settings or JavaScript-heavy rendering that prevent AI crawlers from accessing the page)

This tells him exactly what to fix so AI is more likely to recommend the brand.

This system surfaced ~52,000 monthly searches with zero AI coverage for one of Jonny’s clients. And a content strategy for the next quarter.

How to Read and Act on Prompt Data

Generative AI prompt tracking data might seem hard to trust at face value.

LLMs give variable answers even in temporary chats.

Platforms push silent model updates that tweak source weighting.

And training data bias is real. One study found LLMs often favor global brands over local ones, meaning you might be invisible in your category because of the model’s defaults rather than your content.

Margaret at Hootsuite found the changing outputs of LLMs surprising when she first started prompt tracking:

It was way more chaotic than I expected. The same prompt can return a totally different brand list on ChatGPT vs Gemini vs Perplexity. “AI visibility” isn’t just a single thing to optimize for: you’re effectively running parallel strategies, and they don’t transfer cleanly, and answers will fluctuate constantly.

This variance doesn’t mean prompt tracking data is useless.

But it is the reason you need to track trends over time instead of getting hung up on moment-specific snapshots.

Here are a few meaningful signals to watch for.

Increased (or Decreased) Frequency Over Time

A consistent rise in mentions or citations usually means your content efforts are working.

A consistent drop could mean a competitor is gaining ground or a key piece of content has gone stale.

Action to take: If the trend is up, note what you published in the weeks before the lift. That’s your playbook. Keep doing it.

If it’s down for four or more consecutive weeks, pick one fix for that category:

• Refresh a stale piece with updated data
• Publish a new comparison page targeting the prompts you’re losing
• Pitch a third-party source that’s being cited in that space

Consistent Source Inclusion

If you repeatedly see the same third-party sources cited in your LLM visibility tracking data, stop and take note.

It means the LLMs trust these sources.

And third-party sources are powerful for AI visibility. Airops found 85% of brand mentions come from third-party pages.

For example, if G2, TechRadar, and Capterra keep appearing across your evaluation prompts, those are your priority pitches.

Semrush’s AI Visibility Overview can help identify sources.

You can see your top “Cited Sources” and “Source Opportunities” (where you’re not being mentioned but your competitors are).

Action to take: Make a list of the sources that keep showing up. For each one, check if you’re already featured. If so, is your listing current and accurate?

Then, pick one you’re missing from and pitch it. A contributed piece, product review campaign, or quote request can all work.

Unless you’re correcting a mention, don’t try to pitch competitors’ sites. They’re not likely to accept.

And don’t only focus on categories you’re losing. Reinforcing visibility in categories you’re already winning is valuable too.

Movement from Mention to Citation

If you used to get mentioned and now only get cited as a source, you’ve slipped into what Jonny calls “ghost ranking” territory:

“Your content shows up in the citation panel, but the AI recommends a competitor.”

Like this example from Teva.

An increase in your “ghost ranking count” over weeks means it’s time to investigate.

Jonny knows this first hand:

Our prompt tracking showed our agency was being cited on agency-directory pages (Trustpilot, Clutch, Semrush) but ghost-ranked at an 83% rate. AI was using directory content as the source of truth, then recommending whichever agency had the densest, most-named presence.

Action to take: Find the sources showing up in the citation panel and audit your presence on each one. Fuller profiles, more reviews, and accurate product details all help convert a citation into a recommendation.

For Teva, this means pitching to be included in the cited articles by REI, backpacker.com, and Outdoor Gear Lab.

It also means updating their own pages (including the ones being cited) with more or newer details.

Then, watching to see if they get less ghost rankings over time.

Common Misreads to Watch for

When you first start generative AI prompt tracking, try to avoid getting tripped up by the following false conclusions.

“Our AI visibility dropped this week. Something’s wrong.”

One week of low visibility or mentions is likely natural variability.

Wait for four consecutive weeks before treating it as a trend worth acting on.

“Our score looks low. We need more content.”

A score can be low for multiple reasons, and the fix isn’t always new content.

Sometimes it’s getting included in more third-party sources or forum threads. Or getting more customer reviews.

If you’re just starting out, you may need to go back to your prompts and make sure they’re not too vague or top-of-funnel.

For example, a broad query like “mesh wifi” may return a definition answer rather than a list of brands.

“Our overall visibility improved after adding more prompts to our tracker. We’re doing something right.”

Adding more prompts to your prompt set will usually make it look like your AI visibility has increased. You’re getting mentioned in more prompts.

“Our overall visibility improved after adding more branded queries. We’re doing something right.”

A branded query is one that mentions your brand name. Of course you get mentioned in the answer.

That doesn’t tell you anything useful.

Limit tracking branded prompts to comparison and reputation prompts. And keep them in a separate cluster so you can filter them when measuring your overall AI visibility.

Weekly Prompt Tracking Workflow

Turning prompt tracking data into a strategy is where you prove the value.

Margaret starts by looking at topics where Hootsuite has low visibility.

Then I look at individual prompt answers to see “What does the internet think about us in this category, and how do we change that?” This usually translates into a few concrete questions for further research:

• Where are the third-party listicles, comparison posts, and analyst write-ups that the model is pulling from, and are we represented accurately on them?
• Do we have a first-party comparison or evaluation page that an LLM can confidently cite, written for the actual buyer in that vertical?
• Do we have our customers (ex. case studies, reviews) reinforcing the same message?

From here, she can recommend actions like updating a case study or getting a mention in a third-party listicle.

Here’s a simple workflow you can use to turn your prompt monitoring routine into AI visibility gains over time.

Pro tip: Don’t expect overnight wins. LLMs take time to reflect new content. Watch for directional improvement over weeks and months. You’re not chasing a score; you’re watching whether your gaps are closing over time.

Step 1. Review prompt cluster trends weekly: What’s the visibility score for your BOFU prompts? Has it fallen for your healthcare cluster? Or a specific use case category?

Step 2. Spot recurring gaps: Note any clusters, categories, or topics that have been underperforming for four weeks or more.

Step 2a. Plan one fix per cluster: Use your content strategy brain to determine the most impactful fix.

That might be:

• Publishing a new comparison page to improve a BOFU prompt cluster’s score
• Updating an existing article with fresher data
• Publishing a type of content you haven’t tried yet on this topic (e.g., video, podcast, social post)

Step 3. Identify recurring third-party sources: Note sources AI consistently cites across your categories. Reddit? LinkedIn? G2? YouTube creators? A trade publication?

Step 3a. Pitch one source you’re missing from: If accepted, you’ll build more off-site authority and increase your chances of being mentioned in the answers you care about.

Bonus resource: Pitching a journalist or news outlet? Use our Journalist Pitch Template, designed by PR experts, to get started quickly.

Start Winning AI Visibility with Prompt Tracking

Prompt tracking isn’t a scoreboard. It’s a compass.

The brands that get real value out of prompt tracking aren’t monitoring every possible prompt.

And they aren’t reacting to one bad week.

They focus on bottom-of-funnel prompts and follow the direction of the graph, not the dot.

Now, it’s your turn:

• Build a set of 20 relevant prompts
• Download our free prompt tracking template
• Log and review data for 30 minutes every week

Once you’re up-and-running, dig into our complete AI optimization guide to get tips on how to fix the issues prompt tracking surfaces.

The post Prompt Tracking: How to Find (and Fix) Your AI Visibility Gaps appeared first on Backlinko.

2026-07-20 20:57:45

Digital PR is the single most important strategy to win in AI search.

A study by Muck Rack found that 84% of AI citations come from earned media, third-party sources like editorial coverage, reviews, and forums.

In AI search, third-party validation beats self-promotion.

Publishing onsite content is still important, no doubt. But your AI visibility is mostly shaped by what happens outside your website.

The more frequently your brand name appears across the web, the more likely LLMs are to recognize, trust, and eventually recommend you.

In this guide, I’ll show you six proven digital PR strategies to earn more backlinks and brand mentions, strengthen your offsite authority, and increase your visibility in AI-generated answers.

Why Digital PR Now Shapes AI Visibility

AI systems go well beyond reading your website.

They pull information from dozens of sources across the web, then combine it into a single answer.

The brands that win in AI search are those that appear consistently across those sources.

That’s where digital PR comes in.

It’s how you get featured in the right publications that AI systems already read and reference.

The more places you show up, the more familiar and trustworthy your brand looks to those models.

Here are six digital PR strategies to start building that presence.

Strategy 1: Data-Led PR

Data-led PR revolves around publishing original research and statistical roundups, and distributing the data to relevant publications.

This is one of the most effective ways to build high-quality backlinks.

Why?

Because journalists, bloggers, and marketers are constantly looking for credible data to support their content.

How It Boosts AI Visibility

Backlinks matter a lot for SEO. And they still matter for AI visibility.

Semrush’s study of 1,000 domains found that brands with stronger backlink authority are more likely to appear in AI-generated answers.

Seer Interactive’s study backs this up:

The top two metrics that impact AI visibility are domain authority and high-quality backlinks (from DA 60+ sites).

Data-led content is how you earn those quality backlinks. People love citing fresh numbers.

I helped one of my clients, Resource Guru, put together the “Agency Overworking Report 2025.”

Since it was published, this report has generated 21 backlinks naturally, including coverage in Forbes.

It’s also consistently being cited in AI answers.

How to Do It

First, publish your data-led content.

You have several options:

• Conduct new research nobody has done before: This is an excellent choice if you’re in a growing field like GEO, where data is scarce, and any credible study will attract attention.
• Update existing studies: Find stats that haven’t been refreshed in over two years. Outdated data is everywhere, and writers actively look for newer numbers to replace them. If you can be the source that fills that gap, you’ll earn the citation.
• Compile existing statistics: You don’t always need to generate new data. Aggregating hard-to-find stats into one well-organized resource can be just as link-worthy.

Once your data is live, pitch it proactively.

Find articles in your niche that reference outdated statistics or cite sources that no longer exist. Reach out to the authors and offer your fresher data as a replacement.

Also, pitch your new data to existing statistical roundups in your niche. These pages are usually updated frequently, so the author is more open to new additions.

Here’s an example of a good pitch that I received for my AI SEO stat roundup:

Strategy 2: AI Citation Outreach

AI citation outreach is the practice of securing placements in the specific sources AI models already pull from.

In my experience, it’s the quickest way to start earning brand mentions in AI responses.

How It Boosts AI Visibility

If you can get into the content AI cites, you can influence the answers AI gives.

For example, my agency Position Digital was included in Exposure Ninja’s listicle on “The Best AI Search Optimisation Agencies in 2026.”

And since that listicle was cited by ChatGPT, my agency ended up being recommended in one of the answers.

How to Do It

LLMs cite content differently. Each model has its own preferences, tendencies, and patterns.

For example, I asked AI Mode and ChatGPT to recommend the best SEO tools.

Here’s what ChatGPT said:

And here’s the AI Mode answer:

Similar answers. But totally different citations.

So, you need to tailor your strategy to each AI model you want to target.

1. Research user prompts

Start by mapping out the prompts your target audience is likely to type into AI tools.

AI companies haven’t given us prompt data yet, so we’ll have to settle for educated guesses.

Here are some good places to start:

Alternatively, you can use Semrush’s AI Visibility Toolkit to find out exactly what your audience is typing into LLMs.

2. Monitor the cited pages

Once you have your prompt list, run each prompt through each AI model and note which pages are cited.

If you don’t want to do it manually, Semrush’s AI Visibility Toolkit shows you the sources for each prompt.

You can also use ListBrew to surface “best X” listicles and comparison pages that already get cited in AI answers for your target prompts.

3. Find the contacts

Find the contact information for each opportunity using tools like Hunter and Apollo.

Tip: Prioritize someone who has editorial control over the content, like the author or content editor, rather than generic email addresses like [email protected].

4. Send personalized pitches

Reach out to each prospect and ask to be included in the content.

Keep the pitch short and specific to their piece. To increase your chances, offer something in return, such as:

• A free trial or demo of your product
• A reciprocal mention in one of your own high-traffic pieces

Also, prepare a short blurb that the author can easily copy and paste into the content. It seems simple, but it makes a big difference.

Here’s an example of a pitch I sent during one of my listicle outreach campaigns:

Strategy 3: Reactive PR

Most digital PR campaigns take weeks or even months to prepare and execute.

Reactive PR is about speed — being the first one to respond to breaking news, viral trends, and emerging topics.

How It Boosts AI Visibility

LLM training data has a cutoff date.

When someone asks about a current event, the model can’t rely on what it already knows. It has to search the web for fresh sources.

That’s your window.

When a story first breaks, there are usually only a few credible sources covering it.

If your brand is among the first to publish useful commentary, analysis, or original reporting, your content has a much higher chance of being surfaced and cited by AI systems.

Here’s a solid example:

Search Engine Journal was among the first media outlets to report on Google’s new SEO guidelines.

The piece has since gained over 16,000 readers, 500 backlinks, and several AI citations.

How to Do It

The biggest challenge with reactive PR is spotting trending stories before they become a trend.

By the time a topic is dominating headlines, you’re already too late.

Success comes from identifying emerging conversations while they’re still gaining traction.

A few ways to do that:

• Join online communities: Many trends gain momentum on Reddit, X, and Slack before they receive mainstream coverage.
• Follow industry leaders on LinkedIn: Experts often share breaking news, observations, and predictions here before publishing full articles or reports.
• Follow the major players in your industry: Product launches, partnerships, acquisitions, funding rounds, and policy changes often create PR opportunities. The sooner you spot these developments, the faster you can react.
• Track journalist requests: Platforms like Qwoted, Featured, and JournoRequests can reveal stories that journalists are actively working on before they’re published.
• Use Google Trends and Exploding Topics: These tools help you find emerging topics that your audience is searching for right now.

A secondary layer of this process is monitoring how stories spread across publications.

Many trends don’t appear out of nowhere in major media — they first circulate through smaller blogs and niche industry sites.

Tracking this ripple effect helps you identify narratives early and position your response while the topic is still evolving.

• Set up Google Alerts: Create alerts for important keywords, competitors, and industry topics so you get notified as soon as new stories are published.
• Monitor industry newsletters: Curated newsletters are often one of the fastest ways to discover emerging trends and conversations.

Once you spot an opportunity, move quickly.

Write a report or a commentary piece on your website and social media, and pitch the story to major news outlets in your field.

Strategy 4: Ego Bait

Ego bait involves creating content that features industry experts and influencers.

The goal is to stroke their ego, making them more likely to share your content, mention your brand, or link back to your site.

How It Boosts AI Visibility

Featuring recognized experts can improve AI visibility in two ways.

First, it increases the credibility of your content.

An AI SEO study found that pages containing expert quotes receive 4.1 citations in ChatGPT on average, compared to 2.4 for pages without them.

And we’ve seen it firsthand.

Our article on SEO competitor analysis features insights from 20 experts, and it’s cited by both Google’s AI Overviews and AI Mode.

Expert commentary acts as a trust signal, making the content more authoritative and cite-worthy.

Second, it creates a distribution channel.

When experts are featured in your content, many will share it with their audience, mention it on social media, link to it from their websites, or include it in newsletters.

As those mentions and backlinks accumulate, your brand becomes more visible across the web.

Stronger brand visibility = stronger LLM visibility.

How to Do It

There are three types of ego-bait content you can create:

1. Expert roundups

Use journalist outreach platforms like MentionMatch, Featured.com, and Qwoted to gather expert insights for your content.

Choose the best quotes and feature them on your blog post.

Once it’s live, tag every contributor in your LinkedIn post.

Most will reshare it, comment, or at minimum engage with it, which extends your reach well beyond your own following.

2. Case studies and success stories

Highlight real results from customers, partners, or collaborators.

A well-written case study flatters the subject, gives them something worth sharing, and adds a layer of credibility that generic content can’t replicate.

3. Top experts or influencers lists

Curate lists of respected people, companies, or voices in your category.

Examples include:

• Top SEO Experts in 2026
• Most Influential AI Search Voices
• Leading Growth Marketers in SaaS

Being included in a curated list is often incentive enough for people to share it, especially if the selection feels credible and relevant.

This type of structured content is also highly citable by AI systems.

Strategy 5: Thought Leadership

SEO content is built to rank.

Thought leadership content is designed to establish you as a recognizable voice — both for people and LLMs.

And when you already have an established presence, it’s much easier to market your business.

How It Boosts AI Visibility

Most content gets ignored because it fails to give people a reason to engage.

Thought-provoking content, on the other hand, is designed to challenge assumptions, introduce new perspectives, and spark discussion.

Just look at this “controversial take” from Khanh Linh Le.

This post generated an unusually high number of comments relative to its likes because it challenged conventional thinking and encouraged debate.

Some people agreed. Others pushed back. But everyone had something to say.

That’s the goal: to drive engagement.

Engagement drives shares. Shares drive mentions across blogs, forums, social platforms, and publications.

And when a lot of people talk about you, AI pays attention.

How to Do It

1. Build a LinkedIn presence

The first step is to start posting on LinkedIn.

Why?

Because at the time of Semrush’s study, LinkedIn was the second-most-cited domain in ChatGPT, AI Mode, and Perplexity.

Some tips:

• Publish consistently: AI citations reward relevance and consistency more than virality. The Semrush study found that 75% of cited authors post at least one post per week.
• Take a stance: Share opinions, challenge assumptions, and weigh in on debates happening in your industry. Posts that take a clear stance generate far more engagement than informational updates.
• Use your personal and company pages: Some LLMs like Perplexity gravitate more towards company pages, while others like ChatGPT and AI Mode like citing personal pages.

2. Write guest posts for major publications

Guest blogging is a great way to distribute your ideas and share your expertise in high-authority publications.

When multiple authoritative sources cover the same topic and all point back to you as the originating voice, AI models start to recognize you as the go-to authority on that subject.

At Position Digital, I’ve developed a framework called the Guest Post (GP) Engine.

Here’s how it works:

I publish a blog post on my website.

I then publish variations of that post on other publications, ideally those with a high website authority.

As an example, I wrote about “Content Refreshes” on the Position Digital blog.

I then wrote guest posts on the same topic on Sitebulb and Surfer SEO.

All three articles are now cited by AI Overviews.

3. Appear as podcast guests

Podcasts are one of the most underutilized thought leadership channels.

A single appearance can generate:

• Mentions on the host’s website
• Show notes and transcripts
• Social media clips
• Newsletter features
• Citations from future content creators

If you’re just getting started, don’t chase after big, established shows.

Start small. Build relationships with newer podcasts in your niche.

Smaller shows are easier to get on, their hosts are often more engaged, and the content still gets indexed and cited.

As your reputation grows, the bigger opportunities follow naturally.

Strategy 6: Community Building

Community building is about establishing your brand’s presence in third-party forums, review sites, and customer rating platforms that AI models treat as trusted, independent sources.

This way, your brand is validated not just by what you say about yourself, but by what neutral platforms say about you.

How It Boosts AI Visibility

AI models trust neutral, community-driven sources over brand-owned marketing.

It’s not an opinion. It’s backed by actual data.

According to another AI visibility study by Semrush, LLMs cite Reddit threads about Microsoft products much more frequently than Microsoft’s own blog.

Let that sink in.

And it’s not just forums like Reddit. Review platforms like Trustpilot and G2, where users can share authentic reviews and ratings, carry significant weight too.

Seer Interactive also did a study of 800k AI responses and found that brands with no Trustpilot profile have a median AI citation rate of just 1%.

Brands with even a minimal profile, as few as 1 to 13 reviews, jump to 53.5%.

The implication is clear:

If AI has to choose between what a brand says about itself and what hundreds of independent users say about that brand, it will often favor the latter.

How to Do It

1. Contribute insights on Q&A platforms like Reddit and Quora

These communities are scraped heavily by AI models and frequently cited in responses.

But be careful, Reddit and Quora communities have a low tolerance for overt self-promotion.

Accounts that exist purely to promote a product get flagged, downvoted, or banned.

The key is to participate as a real contributor, not as a marketer.

Be transparent about who you are and what company you represent, but focus primarily on being helpful.

Further reading: Reddit Marketing in 3 Hours/Week: From 0 Karma to Real Cred

A good approach is to contribute meaningfully to 3–5 relevant threads per week.

Only mention your brand when it is genuinely relevant to the question or adds value to the discussion.

You can also host an AMA (Ask Me Anything) session to share your knowledge while building brand awareness.

2. Create optimized profiles in review sites

Depending on your niche, set up and optimize your profiles on review platforms like:

• G2, GetApp, and Software Advice for B2B SaaS
• Trustpilot and Clutch for agencies and service providers
• Tripadvisor and Google Reviews for hospitality and local businesses

After that, make sure to ask your existing customers to leave positive reviews and ratings.

3. Write content on Medium

Medium is another platform that AI frequently cites, according to the Semrush study above.

Use it to republish condensed versions of your best content or share original perspectives that complement your main blog.

Publishing the same ideas on Medium that you’d put on your own site gives those ideas a better chance of being cited.

Show Up Where AI is Already Looking

The quickest path to AI citations is getting into the pages AI already trusts.

Start by using Semrush’s AI Visibility Toolkit to uncover the prompts your audience is typing into AI chatbots.

Then, review the sources each AI platform cites for those prompts and find a way to earn a placement there.

When you’re ready to go deeper into how to optimize for citations and influence AI answers, read this guide on LLM seeding.

The post 6 Digital PR Strategies to Boost AI Visibility appeared first on Backlinko.

2026-06-11 05:06:44

Your content can rank on the first page of Google and still never be cited or mentioned by LLMs.

This makes sense once you understand query fan-out, a background process AI systems use to build answers.

When someone asks ChatGPT or Perplexity a question, it doesn’t default to the best-ranking page.

Instead, it runs related searches behind the scenes, pulling from the most relevant and reliable sources, regardless of position.

If your brand doesn’t show up in those searches (whether through your own content or third parties), you’re unlikely to make it into the answer.

High rankings don’t hurt, of course.

But in AI search, coverage and retrievability are king.

In this guide, I’ll teach you how to optimize your content strategy for query fan-out to help increase your AI visibility.

You’ll learn:

• Why LLMs use query fan-out
• How it behaves differently across major AI platforms
• Why it changes how you create and structure content
• A 6-step workflow for earning more citations in AI search

Free template: Our Query Fan-Out Audit Template includes ready-to-use spreadsheets for logging money prompts, sub-queries, and content gaps — plus a checklist to keep you on track. Download it now to follow along.

First, I’ll dive deeper into how query fan-out works.

What Is Query Fan-Out?

Query fan-out is a process AI search systems use to break a single user query into multiple sub-queries to create the most helpful response.

In other words, the AI “fans” the query out into a series of related sub-questions to build a more complete picture of the topic.

It then pulls information from multiple sources — editorial sites, Reddit threads, comparison and product pages — and synthesizes it into a single comprehensive answer.

AI systems use query fan-out for a few reasons:

• Confirm information: A single source might be wrong or biased. Running parallel sub-queries allows the system to cross-reference multiple sources and find consensus before committing to an answer.
• Handle complex, specific queries: When a question has multiple layers, like comparing two products across price, reliability, and long-term value, fan-out breaks it into manageable pieces that the system can research independently.
• Answer the real question: Someone searching “best toothbrush” probably also wants to know about price, battery life, and durability, even if they didn’t say so. Fan-out anticipates those needs and gathers evidence upfront.

For example, a search for “best toothbrush” might trigger sub-queries like “best electric toothbrushes [year]” and “best toothbrushes for sensitive gums.”

This helps the AI build a more complete and useful answer:

The AI then synthesizes those findings into a single answer that covers everything the user might want to know: top picks, price ranges, use-case breakdowns, and comparisons.

In this way, it anticipates the user’s needs, even though the original prompt (best toothbrush) was just two words.

What Query Fan-Out Is NOT

Now that we’ve covered what query fan-out is, let’s clear up a few common misconceptions.

Query fan-out is not:

• Keyword research: This is the process of finding terms your audience searches for. Query fan-out is something AI systems do automatically, behind the scenes, every time someone asks a question.
• People Also Ask: PAA is a visible SERP feature that shows users what else they might want to search. Fan-out happens in the background whether you can see it or not.
• A fixed set of queries: Only 27% of fan-out sub-queries remain consistent across repeated searches, according to a SurferSEO study. Sub-queries vary by phrasing, user context, and platform.

Further reading: AI SEO Myths, Debunked: A No-BS Guide for Marketers

Why Query Fan-Out Matters for AI Visibility

Understanding what query fan-out is only gets you so far. The real question is: What does it mean for your content strategy?

Here are four shifts that should make you rethink how you approach content.

You Don’t Need Top Rankings to Get AI Citations

Top rankings don’t automatically translate to AI citations.

When AI breaks a query into sub-queries, it pulls the most relevant and complete source for each one, regardless of where it ranks.

ChatGPT cites pages in position 21+ almost 90% of the time, according to a Semrush study.

Perplexity and Google show the same pattern.

AI Retrieves Passages, Not Pages

Rather than directing users to a page, AI systems scan your content and synthesize the exact passage that resolves a query.

This means that the earlier you answer a question, the better your chances of being extracted.

The data backs this up.

44.2% of citations in ChatGPT responses come from the first 30% of a page, while 31.1% come from the middle, and 24.7% from the final third, according to growth advisor Kevin Indig’s analysis of 1.2 million ChatGPT responses.

You’re Competing Across a Whole Topic, Not Individual Keywords

SEO often revolves around individual keywords. Query fan-out revolves around comprehensive coverage.

That’s why broad, well-connected coverage across a topic (think pillar pages and topic clusters) can help you earn more AI visibility.

Pro tip: Pages that rank for fan-out queries (not just the main query) are 161% more likely to get cited, according to a SurferSEO AI Overviews study.

Query Fan-Out Collapses the Buying Journey

We were taught that buyers move linearly — awareness, consideration, decision — and have long optimized content for each stage.

With AI, those stages collapse into one.

A single high-intent question triggers the system to fan out.

It pulls awareness-level context, consideration-level comparisons, and decision-level specifics into one answer.

The entire buying journey can now happen in a single interaction. So your content needs to work across the full funnel, not just the stage you’re targeting.

Pro tip: Want to work through these steps as you read? Our free Query Fan-Out Audit Template has spreadsheets for tracking your money prompts, sub-queries, intent buckets, and content gaps — plus a checklist to keep the full workflow on track.

The Query Fan-Out Workflow: 6 Steps to Earn More AI Citations

This six-step workflow shows you how to earn more AI citations by identifying and targeting high-impact sub-queries.

It’s repeatable, so you can follow these steps for every topic that matters to your business.

Note: Each AI platform handles fan-out differently, from the number of sub-queries it runs to how it cites sources. We cover the platform differences in depth after the workflow.

Step 1: Find Your Money Prompts

Money prompts are the conversational phrases or questions your ideal customer would ask an AI tool when trying to solve the problem your product or service addresses.

Money prompts are:

• Typically long-tail and highly specific
• Tied to a real use case or constraint
• Close to a decision, not just browsing

Think of money prompts as the AI SEO equivalent of money keywords: high-commercial-intent keywords designed to drive sales.

For example, “noise-canceling headphones ” is a keyword.

“What noise-canceling headphones are best for working from home with kids around, and cost under $300?” is a money prompt.

Look for money prompts where your audience asks questions:

• Customer support tickets
• Community forums
• Sales call transcripts
• Internal chat logs
• Google Search Console queries

For example, when I searched for noise-canceling headphones on Reddit, I found multiple money prompts in real users’ posts.

Like this one that asks for the best noise-canceling headphones for telehealth:

And this one asking for durable headphones that will last longer than 2 years:

Forums and transcripts are a good starting point. But you’ll need a dedicated tool to find money prompts using real AI search data.

Semrush’s AI Visibility Toolkit tells you exactly what users type into AI tools, along with the AI’s response.

To show you how it works, I’ll use Bose, a well-known headphone brand, as an example.

Note: I’ll be using Semrush to show you how to complete the query fan-out workflow. If you don’t have a subscription, sign up for a free trial of Semrush One, which includes the AI Visibility Toolkit and Semrush Pro.

First, I searched Bose’s domain in the Visibility Overview tool.

The “Topics & Sources” report revealed over 123.7K prompts where the brand already appears in AI answers.

Filtering by “noise canceling” let me dig deeper into topic-specific money prompts like “noise-canceling headphones for sensory issues.”

Clicking the prompt provides a full breakdown: the AI’s response, every brand mentioned alongside yours, and the exact sources it cited.

Follow the same process for your own domain.

These prompts are your highest-priority money prompts — your audience is already searching them, and AI is already answering them.

Don’t have AI visibility yet? Use the Prompt Research tool.

Enter a broad topic to see the prompts that generate the most AI results in your industry.

As you find relevant prompts, add them to your spreadsheet.

Even a few money prompts give you enough to work with for the next step.

Step 2: Generate Your Fan-Out Set

There are two ways to generate fan-out sets: manually or with a dedicated fan-out tool.

The manual approach is free and helps you understand how fan-out behaves, while tools are faster and better suited to working at scale.

I’ll start with the manual method.

Paste this prompt template into any AI platform to get a fan-out set:

Expand this question into the sub-queries an AI system might search to answer it: [your money prompt].

When I ran my Reddit money prompt through ChatGPT, it returned sub-queries grouped into categories:

• “Core Product Category”
• “Durability & Longevity”
• “Battery & Hardware Lifespan”
• “Reliability & Failure Rates”

Each category is a potential content gap you’ll address in Step 4.

Run your money prompt through multiple AI tools to get a more complete picture, since each platform tends to expand prompts differently.

Pro tip: Manual research is a solid starting point, but outputs can contain inaccuracies or hallucinations. A dedicated fan-out tool simulates how different AI platforms expand your query and returns an organized list of sub-queries you can act on immediately.

For a faster option, Backlinko’s free ChatGPT Query Fan-Out Tool is worth trying.

Install the Chrome extension, open ChatGPT, and ask your money prompt. The extension captures the response in real time and breaks down every sub-query ChatGPT ran behind the scenes.

When I ran a prompt through it, the panel showed:

• Each sub-query the model generated
• The metadata behind the response, including model version
• Every URL cited, categorized by type: sources, products, images, and news

As you gather sub-queries, assign a query type to each — this tells you what kind of content you’ll need to create in the next step.

Use these definitions to categorize them.

Step 3: Bucket Sub-Queries by Intent Type

Bucketing by intent tells you what types of content to create and the ideal format for each.

To categorize a sub-query, answer this question: What does the person actually want to do after getting an answer?

Consider an example from the noise-canceling headphones query fan-out set: “Sony vs Bose Noise Canceling Headphones.”

Someone asking this is weighing two specific products against each other, so it’s a “comparison” query.

The right format for this query is a head-to-head comparison page or table, not a general buying guide or listicle.

The intent isn’t always this obvious, and some sub-queries may fit more than one bucket.

When that happens, place it where the strongest intent lies.

Here’s a general guide to the main intent buckets and what each one calls for:

Step 4: Audit Your Existing Content for Gaps

Once you’ve bucketed your sub-queries by intent and format, check which ones your site already covers and which ones it doesn’t (aka content gaps).

Start by searching your own site.

Type “site:yourdomain.com [sub-query topic]” into Google.

For example, running “site:bose.com noise canceling headphones” surfaces all their pages on that topic.

From here, evaluate each page against the sub-query it should cover:

• Coverage: Does it directly answer the sub-query, or just mention the topic in passing?
• Format: Is it the right content format for the intent?
• Self-contained answers: Can the answer stand on its own, without the reader needing to look anywhere else?

Categorize each page by its coverage level:

For each sub-query, you’ll also want to know which competitors are showing up for your money prompts.

Run your money prompts through AI platforms to gather this information manually. Or refer back to your research from the AI Visibility Toolkit in Step 1.

Click any prompt to see which brands were mentioned and the exact sources the AI cited.

Already showing up alongside competitors? That’s a prompt worth protecting — focus on strengthening your coverage so you stay in the answer.

If competitors are showing up and you’re not, that’s a gap worth closing before they own it.

Step 5: Structure Your Content So AI Can Extract It

Creating the right content is only half the job. The other half is making it easy for AI to find, parse, and use.

Start by filling the gaps you identified in Step 4.

For sub-queries with no coverage, create dedicated pages or sections that target them directly.

For partial coverage, add self-contained answers to existing pages that resolve the sub-query without needing surrounding context.

Then, structure everything so AI can extract it cleanly:

• Address specific questions directly — lead with the answer, not background context
• Use content chunking: Break content into focused sections with clear headings, short paragraphs, and bullet points
• Front-load key information early in the page or section
• Use clear, precise language, including specific product names, figures, and use-case-specific wording
• Add FAQ sections

Here’s what this looks like in action.

Bose has over 63.9K mentions across AI platforms in the U.S. alone:

It helps that they’re a household name. But their content is also built to be extracted.

Their product pages front-load specific claims as scannable elements — “24 hours of battery life” and “legendary noise cancelation” — rather than burying them in copy.

Key specs are organized into structured comparison tables:

And they build dedicated landing pages for use cases like flying, using descriptive, scenario-specific language.

This matters because AI fans out into use-case-specific sub-queries.

When I searched “best noise-canceling headphones for flight anxiety,” AI Mode recommended Bose, using nearly identical language from Bose’s flight landing page.

When a user’s prompt matches the scenario your page was built for, AI systems may be more likely to pull from it.

This is a clear example of that in action.

You don’t need a complete site overhaul to make this work.

Even restructuring a few high-priority pages to address your fan-out gaps can improve your chances of being extracted and cited.

Step 6: Measure Your Performance in AI Search

Once your content is structured and live, track your performance in LLMs.

Start with the money prompts you identified in Step 1.

For each one, you want to know:

• Are you showing up? Is your brand mentioned or recommended in the response?
• Is what it says accurate? Are the claims the AI makes about your brand correct, or is it pulling outdated or wrong information?
• How do you compare? Which competitors appear in the same response, and how are they positioned relative to you?

If you’re tracking manually, run them through multiple LLMs (in a private or incognito window) and record what you find.

But once you’re tracking dozens of sub-queries across platforms, manually tracking gets messy (and time-consuming).

I use Semrush’s Prompt Tracker to automate the process.

It alerts you to changes in mentions for your money prompts, so you don’t have to keep re-running them yourself.

Another helpful tool is the Visibility Overview.

It provides an AI visibility score that tracks how often you’re showing up in AI answers compared to competitors.

The Perception tool tracks sentiment so you know how LLMs describe your brand — and if they mention competitors more favorably.

It also breaks down the factors driving that sentiment.

For Bose, “industry-leading noise cancellation” shows up as a strength, while “over-the-ear models not sweatproof” flags a use-case they could address with targeted content.

Tracking should be an ongoing process.

Revisit your money prompts regularly and update your content as new sub-queries emerge or competitors gain ground.

How Query Fan-Out Works Across Different Platforms

How content surfaces in an AI answer depends on several factors:

• Whether the system searches the live web or draws from its training knowledge
• How many sub-queries it runs
• Which sources it favors, and how it cites them

Understanding those patterns helps you make smarter decisions about content structure, format, and where to focus your optimization effort.

Plus, if a competitor outperforms you in a specific LLM, understanding how that platform handles fan-out can help you figure out why.

Note: Some of the behavior described below is based on how each system describes its own reasoning when prompted. LLMs aren’t always reliable narrators of their own processes, so treat these observations as directional rather than definitive.

ChatGPT

For simple, informational queries, ChatGPT usually responds from its training data without running a live search.

But that changes when the question requires fresh information, comparisons, or real-world data.

When I asked which car I should buy (Toyota vs. Honda) in Thinking mode, ChatGPT spent about 22 seconds reasoning through the question.

Then, it produced an answer drawn from 41 cited sources

That’s query fan-out in action: one prompt, varied sources, and multiple sub-queries running behind the scenes.

By default, you can’t see the sub-queries ChatGPT runs. But I’ll show you how to find them (don’t worry — it’s easier than it looks).

Note: This DevTools method only works in the web version of ChatGPT. You can’t access sub-query data on mobile or in the desktop app.

First, search a money prompt in ChatGPT.

Then, look at your browser’s address bar and copy the slug that appears after chatgpt.com/c/ — that’s the unique ID for your conversation

Next, right-click anywhere on the page and select “Inspect.”

A developer panel will open on the side of your screen:

• Click “Network” at the top of that panel
• Paste the slug you copied into the filter bar
• Refresh the page

Click on the fetch version of the slug (here, it’s the second option under the Name column).

Then, open the Response tab.

Once it loads, press Ctrl+F (or Cmd+F on Mac) and search for the word “queries.”

What appears is the exact set of internal searches ChatGPT ran before producing its answer.

For the Toyota vs Honda prompt, ChatGPT generated queries around:

• Vehicle specifications
• Fuel economy
• Reliability
• Safety ratings
• Long-term ownership costs

Once you have the sub-queries, cross-reference them against your content.

Are you targeting each one? Do your pages use the same language ChatGPT is searching for — “long-term ownership costs” rather than just “value”?

ChatGPT often pulls from third-party sources like Reddit threads, review sites, and comparison pages.

So topical authority matters here — not just what’s on your site, but whether your brand shows up across the sources ChatGPT is likely to retrieve.

Perplexity

Perplexity runs two types of fan-out simultaneously:

1. Internal fan-out — scans your prior conversation history for relevant context
2. External fan-out — searches the external web for relevant information

The final answer draws on both layers, which means your content needs to work for a range of user situations, not just one.

For the Toyota vs. Honda question, Perplexity’s first batch of sub-queries had nothing to do with the cars.

Instead, it checked whether I’d previously mentioned anything that could shape its recommendation.

Like budget constraints, driving habits, or past questions about either brand.

Only after that internal scan did it launch external searches about reliability, ownership cost, and safety ratings.

What this means for your content: Perplexity may pair your page with context you can’t predict: a user’s past questions, constraints, or preferences.

Your content needs to be specific and self-contained enough to remain accurate and useful no matter the surrounding context.

Claude

Claude takes a different approach.

Rather than immediately running sub-queries, it asks clarifying questions first. Then, it generates a response tailored to your answers.

When I asked the Toyota vs. Honda question, Claude presented a preference widget before producing an answer.

Once I responded, it generated a recommendation tailored to my priorities.

Because it clarifies intent before searching, Claude tends to generate fewer, more targeted fan-out sub-queries than other platforms.

The implication for your content: Answer specific, well-defined use cases directly rather than trying to cover every angle on a single page.

Google AI Overviews and AI Mode

AI Overviews appear as concise, AI-generated summaries with sources listed in a clickable sidebar.

They work by synthesizing Google’s existing web index into a tighter, more contained summary.

AI Mode, by contrast, is a dedicated conversational search tab designed for complex, multi‑part questions.

Like AI Overviews, it draws on Google’s index to generate answers, but it offers more interaction and depth.

Neither platform exposes the sub-queries it runs.

But SEOs have found a way to extract Google’s fan-outs using Screaming Frog configured with a Gemini API. Watch Dan Hinckley’s tutorial for a full walkthrough.

For both, the optimization focus is the same: Front-load your answers, use descriptive subheadings, and structure content so individual passages stand on their own.

AI Search Runs on Query Fan-Out — Your Content Strategy Should Too

High rankings alone won’t earn AI mentions.

The brands showing up are the ones covering the questions their audience is actually asking and making that content easy for AI to extract and cite.

You’ve got the query fan-out framework. Now it’s about execution.

Start with one money prompt, map the sub-queries, and audit where your content stands.

Then work through the gaps, one topic at a time.

Next, dive deeper into how to get your brand seen and trusted across AI platforms with our AI search strategy guide.

The post Query Fan-Out: What It Is and How It Affects AI Visibility appeared first on Backlinko.