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Or worse, showing up inaccurately, with outdated or incorrect information that’ll hurt your sales.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.
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:
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:
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.
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:
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.
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.
Tracking these four types of prompts over time will yield the most helpful insights:
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:
| Prompts worth tracking | Prompts not worth tracking |
|---|---|
| “How does Hootsuite compare to [competitor]?” (Comparison) | “What is social media management?” (Pure definition — won’t convert) |
| “Which social media management platforms integrate with Salesforce?” (Evaluation) | “Is Hootsuite a good company?” (Vanity — brand mention is baked in, nothing actionable) |
Find prompts wherever you normally go to learn about your audience.
To find prompts worth tracking, look at:
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:
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.
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:
| Project management | Task management | Workflow automation | Team reporting |
|---|---|---|---|
| Best project management software for marketing teams (Evaluation) | Best task tracking tools for cross-functional teams (Evaluation) | Best workflow automation software for ops teams (Evaluation) | Best project reporting tools for enterprise teams (Evaluation) |
| Asana vs. Monday.com for project management (Comparison) | Does Asana actually improve team productivity? (Reputation) | Asana vs. ClickUp for workflow automation (Comparison) | Is Asana’s reporting good enough for large teams? (Reputation) |
| Project management software with Slack integration (Evaluation) | Best task management software for small teams (Gap) | Asana vs. Notion for managing marketing workflows (Comparison) | Asana vs. Smartsheet for project visibility (Comparison) |
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.
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.
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.
With our tracker, you can log the following for each prompt:
Customize it to whatever makes sense for your business.
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:
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.
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.
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.
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.
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.
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:
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):
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.
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.
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:
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.
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.
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.
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:
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.
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:
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.
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.
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.
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.
First, publish your data-led content.
You have several options:
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:
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.
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.
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.
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:
| Source | Tips |
|---|---|
| User questions in sales calls | Note the exact language prospects use when describing their problem |
| Queries in Google Search Console | Look for ultra-long queries in your performance report |
| People Also Ask in SERPs | Use a free People Also Ask tool to find common questions about a topic |
| Keyword data in your SEO tool | Filter for question keywords starting with who, what, how, why, which |
Alternatively, you can use Semrush’s AI Visibility Toolkit to find out exactly what your audience is typing into LLMs.
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.
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].
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:
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:
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.
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.
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:
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.
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.
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.
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.
There are three types of ego-bait content you can create:
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.
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.
Curate lists of respected people, companies, or voices in your category.
Examples include:
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.
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.
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.
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:
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.
Podcasts are one of the most underutilized thought leadership channels.
A single appearance can generate:
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.
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.
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.
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.
Depending on your niche, set up and optimize your profiles on review platforms like:
After that, make sure to ask your existing customers to leave positive reviews and ratings.
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.
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:
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.
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:
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:
| Sub-Query | What It Contributes to the AI Response |
|---|---|
| Best electric toothbrushes | Top-rated picks and editorial consensus |
| Best toothbrushes for sensitive gums | Use-case recommendations |
| Oral-B vs. Philips Sonicare | Head-to-head comparison data |
| Best eco-friendly toothbrushes | Value picks and pricing information |
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.
Now that we’ve covered what query fan-out is, let’s clear up a few common misconceptions.
Query fan-out is not:
Further reading: AI SEO Myths, Debunked: A No-BS Guide for Marketers
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.
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.
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.
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.
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.
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.
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:
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:
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.
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:
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:
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.
| Query Type | What It Means |
|---|---|
| Reformulation | A reworded version of the original prompt |
| Comparative | Weighs two or more options against each other |
| Implicit | Addresses a need the user didn’t explicitly state |
| Personalized | Tailored to a specific situation, constraint, or preference |
| Entity expansion | Drills into a specific brand, product, or person mentioned |
| Related | A connected topic the AI anticipates the user might want next |
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:
| Bucket | Description | Example Sub-Query | Content Format |
|---|---|---|---|
| Definitions / Basics | What is X? How does X work? | “how do noise canceling headphones work” | Explainer article, glossary section |
| Comparisons / Alternatives | X vs Y, alternatives to X | “apple airpods max vs sony wh 1000xm4” | Comparison page, head-to-head section |
| Best for X / Recommendations | Best option for a specific use case | “best noise canceling headphones for working from home” | Listicle, buying guide |
| Problems / Troubleshooting | How to fix X, why does X happen | “how to get rid of background noise in audio” | How-to guide, FAQ section |
| Pricing / Value | How much does X cost, is X worth it | “are there any good wireless headphones with noise cancellation under $150?” | Pricing page, value comparison section |
| Social Proof / Discussions | Reviews, Reddit opinions, user experience | “best earbuds for calls in noisy environment reddit” | Review roundup, user feedback section |
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:
Categorize each page by its coverage level:
| Coverage Level | What It Looks Like | What to Do |
|---|---|---|
| Not covered | No page on your site addresses this sub-query at all | Create new content targeting this sub-query directly |
| Partially covered | A page mentions the topic in passing but doesn’t resolve the sub-query directly | Add a dedicated section to the existing page that fully answers the sub-query |
| Fully covered | A dedicated section or page answers the sub-query completely and can be extracted and cited by AI without needing surrounding context | Monitor for AI citations and update regularly to stay current |
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.
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:
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.
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:
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 content surfaces in an AI answer depends on several factors:
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.
| Platform | How Fan-Out Works |
|---|---|
| ChatGPT | Reasons internally, then runs live web searches when a question requires fresh data, comparisons, or current information |
| Perplexity | Combines conversation context with real-time web search |
| Claude | Clarifies intent first; relies mostly on training data |
| Google AI Overviews | Synthesizes Google’s index into condensed, featured-snippet-style summaries |
| Google AI Mode | Breaks complex prompts into multiple searches across Google’s index |
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.
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 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:
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 runs two types of fan-out simultaneously:
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 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.
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.
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.
2026-06-09 05:32:12
You can be a strong brand, publish high-quality content, and still not have topical authority.
Just look at Great Jones, a kitchenware company.
Their Dutch oven (called The Dutchess) is beautiful, well-reviewed, and featured in industry-leading sites like Vogue, the New York Times, Bon Appétit, and The Kitchn.
But search “best Dutch ovens” on Google or ask an LLM for recommendations, and the brand rarely appears.
It’s not that Great Jones lacks content or press.
What’s missing is the pattern — a consistent, positive framing that ties the brand to Dutch ovens across its own site and third parties.
Without this, search engines and large language models (LLMs) can’t confidently connect the brand to the topic, so they default to the names with stronger signals.
Many brands have some version of this gap. And AI search has only made it more visible.
The good news: You can build this pattern.
In this guide, I’ll show you how using the Topical Authority Pyramid, a framework I created to turn your brand into the go-to name in your niche.
This framework builds on conversations with Amanda Milligan, Content and Growth Manager at Semrush, and my work in brand positioning across ecommerce, SaaS, and finance.
Topical authority is your site’s earned reputation for expertise on a specific subject. It forms when your brand and topic appear together repeatedly across the sources that buyers, search engines, and LLMs trust.
Think about the brands you automatically connect with certain topics.
Like these:
You didn’t consciously decide to make those associations.
They formed because those brands kept showing up with the same message, in the same spaces, around the same topic.
That’s topical authority — and it’s also how search engines and LLMs learn which brands are most strongly associated with a topic.
Topical authority has traditionally been defined by content volume and breadth of coverage.
Publish comprehensively on a subject, and you’d own it.
That’s no longer enough.
As Amanda explains:
The phrase “topical authority” has been around for a long time, but the thinking around it has evolved significantly. At its core, it’s always been about your brand becoming associated with specific topics. What’s changed is how we try to build that association.
Today, search engines and LLMs look for more than coverage. They look for a clear position on the topic and external evidence that supports it.
To address this, I created the Topical Authority Pyramid:
The Pyramid breaks topical authority into three layers:
Each layer works alongside the others to establish your brand as the expert in your niche and earn more visibility in search engines and LLMs.
Many brands, including Great Jones, have strong foundational authority and scattered proof, but no consistent POV tying it all together.
Here’s how to build all three.
Free resource: Download our free Topical Authority Audit template to audit your topics, score competitor authority, and track your progress. Fill it out as you work through each step below or at your own pace.
Your brand likely already has a topical reputation, whether you’ve shaped it intentionally or not.
Audit it before deciding what to build.
The gap between what you publish and what you want to be known for may be wider than you expect.
This is something Amanda has experienced firsthand:
When I did content audits, I’d inventory every piece of content by topic. You might find you have dozens of pieces on something that isn’t even your priority, and only five on the topic you actually want to own. That mismatch is exactly what a topic audit is designed to surface because what you’ve published is what you’re telling Google and buyers your priorities are.
The fastest way to assess this is with Semrush’s Organic Rankings tool.
Enter your domain to automatically see your brand’s strongest topic associations, organized by the topics getting visibility.
When I did this for Great Jones, their strongest topical associations were “recipes” and “celebrity chefs.”
Dutch ovens barely registered.
Yet, the Dutchess is their primary product.
And “Dutch oven” alone gets over 200,000 monthly Google searches.
Great Jones has a big opportunity to increase their topical authority for Dutch ovens and convert some of this search interest into sales.
These are the kind of topical association gaps you want to surface in this step.
Two more places to look:
Next, review your third-party coverage: mentions, reviews, roundups, and editorial press.
This is where many brands have the biggest gap, and it’s the one AI systems appear to weigh most heavily.
Run these checks:
A quick off-site audit for Great Jones showed me they’ve earned coverage any kitchenware brand would envy: features in major lifestyle publications and partnerships with prominent chefs and influencers.
But when you look specifically at Dutch oven coverage, the off-site gap is obvious.
Most of the top-ranking articles are a few years old (or older):
And the overall sentiment is inconsistent.
For example, in Food & Wine’s Dutch oven roundup, the Dutchess appears under the “Other” section (rather than “Top Picks”) with a caveat about heating issues.
In this Bon Appétit roundup of the best Dutch ovens, Great Jones is categorized under “Dutch ovens we don’t recommend.”
They’re also notably missing from some use-case roundups, like this one from Serious Eats:
In Reddit threads where buyers are actively looking for Dutch oven recommendations, Great Jones rarely comes up.
When it does, many of the threads are from years ago:
Great Jones has real brand equity to build on.
But it’s just not adding up to a solid reputation in Dutch ovens — yet.
You can’t build authority on everything at once.
This step narrows your focus to one topic worth owning based on a few crucial factors:
Start by listing the topics you want buyers, search engines, and LLMs to associate with your brand.
Begin with the obvious ones: the products, categories, use cases, and problems you want to be known for.
Then expand with adjacent topics buyers already care about.
For Great Jones, that might include slow cooking, one-pot meals, kitchen gifting, or cookware care.
Look especially for topics where you already have traction, competitors are weak, or your brand should be associated but currently isn’t.
Once you’ve identified 10 to 15 topics, add them to the “Topic Audit & Scoring” tab in your spreadsheet.
Next, narrow the list down.
Not every topic on your list is worth building a reputation around right now.
For each one, ask two questions:
Do you want to own it? Does it drive revenue, support a product you sell, or build a reputation that brings buyers to you?
How urgent is it?
You should end up with three to five high-priority topics to investigate next.
Now test each shortlisted topic to see who already owns the space and where there’s room for your brand to carve out a position.
For each topic, run four queries on Google and LLMs:
| Query type | What to search | What it tells you |
|---|---|---|
| Head term | The topic as-is (“Dutch ovens”) | Who owns the broad topic; what AI defaults to |
| Best query | Add “best” or a qualifier (“best Dutch ovens under $200”) | Where buyer intent lives; which brands AI recommends |
| Brand query | Your brand + the topic (“Great Jones Dutch oven”) | Where you specifically stand; how AI currently describes you |
| Specific angle | A query tied to an association you might want to own (“Dutch oven for gifting”) | Whether that territory is already claimed or still open |
As you run each query, note:
Record this in the “Query Audit” tab of your spreadsheet.
If a query shows buying intent but the top results barely address it, that’s a topical authority opportunity.
For example, when I search “Dutch ovens” and “best Dutch ovens,” the same brands consistently come up: Le Creuset, Staub, Lodge, and Caraway.
But rarely Great Jones.
And for “Dutch oven for gifting,” ChatGPT didn’t mention Great Jones at all.
Great Jones only appears when buyers already know to look for them.
More importantly, some topics, such as gifting, aesthetics, and non-toxic coating, are not clearly owned by any brand.
That’s where the opportunity is.
After the Query Audit, score your presence on each topic against three competitors on a 0 to 3-point scale.
The score reflects your overall standing across the Topical Authority Pyramid: foundational, POV, and proof combined:
| Score | What it means |
|---|---|
| 0 | Not present anywhere for this topic |
| 1 | Present but weak or negative |
| 2 | Present and positive but inconsistent |
| 3 | Consistently prominent across high-authority sources and AI |
Note: This isn’t a precise measurement. Use your observations, priorities, and market knowledge to guide the score.
Score your brand first, then each competitor.
After your scoring is complete, look for high-priority topics where you scored a 1 or 2 and at least one competitor scored a 0 or 1.
Those are topics where buyer demand is real, you have some footing, and no competitor has locked it down — the conditions for a winnable position.
For Great Jones, “Dutch ovens for gifting” fits the pattern: high priority, room to claim it, and no clear leader.
By the end, you should have one topic to focus on.
You’ve identified one viable topic. Next, decide what reputation to build around it.
Your POV is the specific angle you own inside that space.
It’s what makes your brand distinct to buyers, search engines, and AI systems.
Like these brands — same topic, completely different associations:
Before identifying your POV, map what dominant brands in your space are already known for.
These are the POVs to avoid. Going after any of them directly means competing for territory another brand has spent years building.
Start with your notes from the Query Audit. The patterns there tell you a lot about which competitors own what.
To go deeper, use the Semrush AI Visibility Toolkit.
The Brand Performance tool tells you which associations your competitors are winning across AI-generated answers (and how your own brand compares).
For Great Jones, the obvious territories are taken:
No brand has clearly claimed gifting Dutch ovens, visual appeal, or beginner cooking.
(Semrush shows Great Jones is leading on design, which gives them a head start.)
These gaps are where your POV lives.
Before committing to a POV, ask three questions:
If a candidate fails any of the three, drop it. It won’t hold up once you start building proof around it.
For Great Jones, “gifting” passes all three questions.
People already buy Dutch ovens as gifts.
Customers already mention its “super attractive,” “modern,” and “beautiful” design in on-site reviews, which aligns perfectly with a gifting POV:
And no competitor has clearly made “gifting” their territory yet.
Your POV should be easy to grasp and repeat.
Writing it as one sentence is the test. If you can’t, it’s likely not sharp enough yet.
For Great Jones, the POV could be:
Each POV targets a different buyer and a different reason to choose Dutch ovens.
This step is where you plan your proof — the concrete evidence that backs up your POV — across your own site and the wider web.
You’re not building anything yet.
You’re mapping what proof you’ll need at each stage of the buyer journey, so you have a clear blueprint to follow.
A POV without proof is just a claim.
To build credibility, you need evidence that backs up two things:
You belong in the category
You’re the go-to brand for the POV you’ve claimed
And you need to reinforce this at every stage of the buying journey with a different kind of proof:
| Buyer stage | What they need to believe | Proof assets that help |
|---|---|---|
| Awareness | This type of solution solves my problem | Research data, industry studies, customer statistics |
| Consideration | This has the qualities I care about | Third-party reviews, expert endorsements, certifications, performance data |
| Comparison | This is the better choice over alternatives | Independent test results, awards, analyst rankings, head-to-head data |
| Active Evaluation | This will work for my specific situation | Case studies, usage data, implementation examples, success metrics |
| Decision | Other people already trust this | Customer numbers, retention rates, repeat purchase data, verified reviews |
To run your audit, go through each belief in the table and identify which proof assets you already have and which are missing.
Use the POV Proof Planner in your template to record your findings:
For Great Jones’s gifting POV, a quick proof audit surfaces:
Before search engines and LLMs can associate your brand with your POV, you need to establish it on your site.
This step is about building that foundation: the hub and supporting pages where your topic, POV, and early proof signals all come together.
Your hub page is the central authority document for your POV.
It defines the topic, explains why it matters, and routes buyers to supporting pages that go deeper.
Side note: If you’ve built pillar pages and topic clusters before, this will feel familiar. The structure is similar, but the organizing principle is proof and belief, not coverage and keywords.
For Great Jones, that could be a “Dutch oven gifting guide.”
It would link to the Dutch oven product page and explain why Dutch ovens make exceptional gifts.
Supporting pages, such as gift basket ideas, a gifting FAQ, and a report on cookware gifting would also be linked.
If you’ve been publishing for a while, you may already have a page that can serve as the hub: a category page, a subcategory page, or an industry-specific landing page.
Supporting pages go deeper than the hub.
Each one proves a specific aspect of your POV at a specific stage of the buyer journey.
Go back to the proof assets you mapped in Step 4 — they tell you what you need to prove and at which stage.
Your supporting pages are how you do it.
For Great Jones, the comparison stage is a clear gap.
To convince buyers the Dutchess is a better gift than the alternatives, they need dedicated comparison pages, backed by awards, endorsements from leading industry sites and public figures, and head-to-head data.
Other supporting pages might include:
Pro tip: Strengthen your hub and cluster pages with on-site trust signals. Include author bios that show real niche experience in the topic, named expert sources or contributors, and an About or editorial page that clearly ties your brand and contributors to the category.
Identify what pages you need, and fill out the rest of the “On-Site Foundation Planner” tab in your template.
Lead with the most important information first — also known as the inverted pyramid.
It makes your pages easier for readers to scan and for machines to interpret.
Then, make sure each page has:
Each hub and supporting page proves something on its own.
Link them together, and you create a proof system.
Follow these internal linking best practices:
A strong POV and foundation won’t get you into AI answers if the association exists only on your site.
This is one of the biggest shifts in how topical authority works, as Amanda explains:
Topical authority isn’t just about what’s on your site anymore. You need third-party sources — coverage, mentions, appearances, even reviews — independently reinforcing the same association. If the only place your brand is tied to a topic is your own content, that’s often not enough to build the pattern that AI systems and search engines need to trust you on it.
This step reinforces your POV in the places buyers and AI systems already trust.
A signature proof point is an original, specific story or finding about your topic.
Something others outside your brand would want to reference, share, or build on.
That could be:
For Great Jones and the gifting POV, the insight has to tie Dutch ovens to gifting.
They might pull data from their own sales — say, a 4x spike in Dutch oven purchases in the two weeks before Mother’s Day — and turn it into a “State of Mother’s Day Gift-Giving” report.
That report becomes a press pitch to lifestyle publications, a video on their YouTube channel, and a thread on Reddit’s r/gifts.
One insight, multiple placements, all reinforcing the same association: Great Jones = gifting.
To find yours, start with your proof assets from Step 4.
Look for patterns in your data, reviews, industry trends, or customer behavior.
Once you have a signature insight, decide where and how to distribute it.
There are four main buckets:
For each bucket, identify the specific publications, platforms, or communities where your insight is most relevant.
Not sure where to start?
Run a search on Google or an LLM related to your proof point and look at the sites that rank and the sources that get cited.
Those are the places worth showing up in. List them in the “Off-Site Proof Planner” tab of your template.
For Great Jones, some of that infrastructure is already in place.
They already have the social media following, media clout, and collaborations with names like cookbook author Molly Baz.
What they need is a focused distribution of insights around their gifting POV.
That might look like:
Related reading: LLM Seeding: A New SEO Strategy to Get Mentioned by LLMs
You’ve built the full Topical Authority Pyramid.
Now check whether it’s starting to influence how search engines and LLMs describe your brand.
Use the “Progress Tracker” tab in your spreadsheet to record what you find at 30, 60, and 90-day intervals.
Coverage tracking tells you whether your topical footprint is growing:
Go back to your Step 2 notes. How many of your four query types surfaced your brand unprompted? Run them again and compare.
Also monitor pages ranking for queries you didn’t directly target, and rising impressions for queries related to your topic.
For Great Jones, the baseline visibility was weak for many non-brand Dutch oven queries.
Showing up in two or three queries at 90 days — especially “Dutch ovens for gifting” — would be a real sign of progress.
Tools that help:
The POV layer tracks language. Specifically, whether mentions of your brand are increasingly paired with your POV.
Run POV-specific prompts monthly and check the wording.
For Great Jones, that’s searches like “Dutch oven wedding gift” or “best Dutch oven to give as a gift.”
And when the Dutchess shows up in reviews, comparisons, and “best of” listicles, watch for the language around it.
Is it being called “a great house-warming gift,” “splurge-worthy,” or “the kind of gift that gets displayed”?
That’s the POV landing.
Tools that help:
The proof layer tracks third-party confirmation.
Are media mentions, third-party pages, and niche communities backing up the POV you want to own?
Start with your proof point.
Are others citing or referencing it? That’s a signal your off-site distribution is working.
Then, go broader.
Run [Your Brand] + [POV] queries on Google and an LLM.
Check whether you’re appearing in more third-party sources associated with your POV.
Are buyers recommending you unprompted in Reddit or niche communities? Are your hub pages attracting links from relevant sites?
When your brand appears, is it being described in relation to your POV?
For Great Jones, that might be a gift guide naming the Dutchess as the go-to Dutch oven for wedding gifts.
Tools that help:
Great Jones proves that great press and a great product aren’t enough for topical authority.
If search engines and LLMs don’t have clear associations attached to your brand, showing up online will be a struggle — no matter what Vogue thinks of you.
But that’s fixable.
The Topical Authority Pyramid gives you the framework:
Once your first topic takes shape, expand.
Follow the Topical Authority Pyramid for your next topic, claim more territory, and deepen your authority in adjacent spaces.
Do this well, and search engines and LLMs may just start recommending you by default.
Want a repeatable way to monitor your AI visibility over time? Our AI visibility audit guide walks you through it step by step.
The post How to Build Topical Authority in the AI Search Era (7 Steps) appeared first on Backlinko.
2026-05-27 04:56:06
PR and SEO used to be separate disciplines.
Now you can’t afford to keep them siloed.
Google and LLMs both rely on third-party signals — backlinks, brand mentions, expert commentary, and coverage in trusted publications — to decide which brands deserve visibility.
PR and SEO both generate those signals, but most teams still operate independently.
When they do collaborate, it’s usually to treat PR as a link-building opportunity rather than a real partnership.
This leaves authority on the table.
But the real gains happen when these teams operate as one.
In this article, you’ll learn a five-step playbook for turning PR and SEO into an always-on authority engine.
I also spoke with two digital PR experts about how they’re partnering with SEO to build more authority across search, media, and LLMs.
Free resource: Download our PR + SEO Outreach Planner to align pitching, prioritize outlets, and track results. It includes a pitch ownership guide for deciding who pitches what and when.
An always-on PR and SEO partnership starts with shared intelligence.
Without it, you get predictable gaps:
The biggest authority wins don’t come from PR and SEO staying in their own lanes.
They come from each team sharing insights that shape angles, assets, and placements.
For PR, this could be:
A sudden spike in journalist inquiries or media coverage around a topic
A new phrase or framing gaining traction among industry voices
Recurring themes across newsletters, conferences, or trade publications
Britt Klontz, digital PR consultant and founder of Vada Communications, says the strongest results come when PR and SEO combine their strengths at the ideation stage:
The best collaborations with SEO happen when PR is brought in early, before an asset or campaign is completed. We used to ask, ‘Can PR promote this?’ Now we ask, ‘How do we build something together that will help with search, media, and brand visibility from the start?’
To facilitate this partnership, build a regular channel for PR to flag insights to SEO.
This could be a shared Slack channel, spreadsheet, or standing agenda item.
For example, when I was the editor of the Hootsuite Blog, our PR team notified us that LinkedIn was shutting down its “Elevate” feature and suggested we should write a blog post about it.
No search volume existed yet, but we created the content anyway.
The post started gaining backlinks and driving a surprising amount of demo requests almost immediately.
Months later, the search volume appeared. And our post ranked #1.
Today, it still ranks near the top of the SERPs for terms like “LinkedIn elevate alternatives.”
AI tools like Claude also use the blog post as a top source for relevant prompts:
That’s the power of PR and SEO sharing information and acting on it quickly.
Rankings, backlinks, and AI citations that would have gone to a competitor built lasting authority for Hootsuite instead.
SEO has signals PR can act on too, including which topics are heating up and editorial gaps.
When conducting keyword research for PR, SEO should flag two things:
That’s why Rola Tfaili, communications manager for North America at Xero, brings SEO into her process from the start:
I want SEO insights — like emerging search trends, keyword gaps, and audience intent — to directly shape our PR narratives and campaign angles from the outset, before content is developed.
Here’s how you can do the same.
Not all keyword tools show you trends over time, so I’ll use Semrush for this step.
Note: If you don’t have a subscription, sign up for a free trial of Semrush One, which includes Semrush Pro and the AI Visibility Toolkit.
Search any term in the Keyword Magic Tool and look at the “SERP Features” column.
Two features in particular signal strong PR potential:
Next, use the Keyword Overview tool’s 12-month trend graph to confirm whether a topic is gaining momentum, seasonal, or fading.
A consistently rising trend is your strongest signal — media interest is likely to be building as well.
Pro tip: Don’t overlook existing topics. A trending term you already own is a valuable opportunity. PR can pitch it to journalists as a timely angle, repurpose it into new formats, or use it as a hook for a broader campaign.
For LLMs, you need a tool like Semrush’s AI Visibility Toolkit that shows actual prompt data, not just search queries.
This gives you insight into the exact prompts your competitors are earning AI visibility for, but you aren’t.
Those gaps are worth flagging to PR, especially if competitors are being cited as authorities on topics your brand should own.
Use your shared doc or Slack channel to provide real-time insights, so neither team works from stale data.
Topics that show up in both PR’s emerging trends and SEO’s keyword data are your highest-priority opportunities.
An AI-ready asset is built to be found, cited, and trusted by search engines and AI models (while being valuable to humans).
This is also called answer engine optimization (AEO), which is the process of creating and structuring content for AI systems.
It can include optimizations like:
When you combine PR’s distribution power with SEO’s technical expertise, you get assets that earn visibility across search, media, and LLMs.
Original data has long helped brands earn backlinks — now, it helps you build AI visibility too.
A collaborative workflow for this asset would look something like this:
SEO identifies the topic based on search demand and content gaps, and PR validates whether the angle is pitchable and shapes the findings into quotable hooks.
Together, they design the study so it’s structured for citations, with a clear methodology, front-loaded stats, and branded visuals that are easy to share.
SEO content teams might be tempted to create this type of asset on their own, then ask PR to pitch it.
But Britt says if PR is involved earlier, they can help answer questions like:
That kind of information can make an asset more useful and impactful.
Pro tip: Give your asset a unique, branded name — like ‘The State of X Report’ or ‘The X Index.’ If journalists mention it without linking, people can still search for it and find you.
Don’t limit original data to a blog post.
High-value assets should have their own crawlable landing page — no gates, no PDF-only content.
Use the same URL each year for recurring assets to build authority. Then, link these pages to related content on your site (and vice versa).
This way, search engines and AI see your topical coverage as connected, not random.
Free tools that solve a specific pain point earn AI visibility, backlinks, and return visits long after launch.
This includes calculators, templates, checklists, and interactive assets.
The gap here is usually distribution.
SEO can build and optimize tools, but without PR’s contacts and timing, even the best ones can be limited by organic performance.
A strong hook helps, too.
Britt says an asset is easier to promote when it “blends search insights with something more personal, like proprietary data, a strong point of view, or a story angle that is relevant right now.”
The payoff is an asset that is reported on and shared widely across channels.
NerdWallet’s tariff calculator is a good example of this in action.
It launched as tariffs dominated headlines — and earned media coverage because of it.
A branded podcast can generate tons of coverage, review articles, and inclusion in “best podcasts on X topic” listicles.
Getting your experts on other podcasts is also valuable for building authority and visibility.
Third-party mentions get your brand and subject matter experts into the conversation, both in search engines and LLMs.
PR typically drives guest placements, but SEO can identify which shows already rank or get cited by AI for your target topics, so you’re pitching the ones that build the most authority.
When published on your site and optimized properly, press releases can become standalone, crawlable assets that increase your AI mentions.
In fact, press release citations in LLMs grew 5x between July and December 2025, according to Muck Rack.
To get the most out of press releases, both teams need to contribute.
Rola has seen the benefit of this collaboration firsthand:
For key assets like press releases, we integrate SEO insights early — before content is developed — and include SEO in the review process to ensure we’re maximizing visibility.
PR shapes the story and the hook. SEO makes sure the on-site version is crawlable, optimized, backed by citable data, and linked to related assets.
So the press release doesn’t just generate buzz, it feeds your broader authority.
Explainers are easy-to-digest resources (usually articles or videos) that simplify complex topics or highlight key info about your brand.
They help journalists and LLMs write accurately and consistently about you — especially if your category is niche or complex.
SEO can use keyword and prompt data to identify the questions your explainers should answer and structure them so AI can parse and cite individual sections.
PR knows which questions journalists and analysts ask most often — and where the current gaps are in how your brand gets described.
The format can vary:
(Bonus points for all three.)
Brands are 6.5x more likely to appear in AI answers through third-party signals than their own content, according to AirOps.
This means PR and SEO have a real opportunity to work together to build more visibility across search and LLMs.
Rola sees this as an important shift for PR teams:
When we align closely with SEO to ensure our key messages land in credible, third-party outlets, we’re not just generating press; we’re helping position the brand to appear in AI search platforms. That intersection between PR, SEO, and now AEO is where I think we’ll see the most measurable impact moving forward.
When your experts are quoted consistently — on your own site, social media, and in trusted publications — Google and LLMs begin to associate them (and your brand) with that topic.
The biggest coordination gap is knowing where to focus.
SEO has the data on which topics have the most search and AI demand — and which publications are already earning citations for them. PR knows which journalists and outlets are most receptive and what angles resonate.
Together, they can pinpoint the exact publications and topics where a placement will improve results for both teams.
Then shape the commentary accordingly.
Concrete, data-backed quotes with a specific stat or firsthand insight are far more citable than generic thought leadership — especially for AI, which favors specificity it can extract and serve directly in an answer.
Getting your experts quoted online is a strong start — but it works best when paired with the other authority-building sources below.
Further reading: 5 Best HARO Alternatives (Expert Review)
Review sites like G2, Yelp, Google Reviews, and Trustpilot are trusted by AI for the same reason they’re trusted by humans.
They aggregate specific, unbiased information about products from verified users.
And AI frequently cites them for product recommendations:
Reviews across multiple sites also strengthen your brand’s authority signals.
It gives AI detailed evidence of what category you belong in, your core features and pricing, and why you should be trusted.
Forums work similarly — AI pulls from Reddit threads and Quora answers when users ask for honest recommendations or firsthand experience.
Brands that show up authentically and positively in these conversations earn another layer of trust signals.
You can’t control these mentions, but consistently showing up as a helpful, knowledgeable voice in your category’s communities builds the kind of organic mentions AI models trust.
PR and SEO should jointly identify which review sites and forums matter most in your industry.
Keep review profiles current and monitor relevant forum conversations for opportunities to contribute genuinely.
Further reading: How to Build a Brand Subreddit: Full Setup Guide (+ Examples)
A Wikipedia page gives Google and AI a neutral, third-party source of facts about your brand.
It also helps establish your brand as a recognized entity in Google’s Knowledge Graph.
It’s a common source for Google’s Knowledge Graph, and it’s baked into LLM training data.
But to qualify for a page, you need to meet Wikipedia’s Notability Criteria.
This includes having significant coverage in reliable, independent sources that address your brand directly and in detail.
PR can help you earn this kind of coverage by pitching stories about your company to journalists in reputable publications.
Once you have a page, you won’t be allowed to edit it directly, as Wikipedia’s rules prevent self-promotion.
But SEO can monitor the page for inaccuracies and flag corrections, and PR can handle reputation monitoring to keep the narrative positive.
Pro tip: Use the same brand name, category language, and positioning everywhere: across your website, social profiles, press releases, and review site listings. The more consistent your language, the more confidently AI and Google can categorize and recommend your brand.
If PR and SEO know what — and to whom — each team is pitching, you avoid mixed messages and misaligned timing.
And your odds of a yes go up.
It doesn’t take much to fix. Just a shared source list, a strategy to split pitching, and a regular check-in to stay aligned.
Pro tip: Download our PR + SEO Outreach Planner to put the tips in this section into action.
SEO has a list of high-authority domains that show up in organic rankings and AI citations. PR has a list of journalists, analysts, creators, and publications that influence their category.
Merging these gives you a single view of every third-party source worth going after.
Build it as a shared spreadsheet with three columns:
Then prioritize.
Any source that appears on more than one list goes to the top. It has double (or triple) the potential to impact your authority and visibility.
Pro tip: Update your list quarterly as sources can shift fast — especially in LLMs.
Create a shared pitch doc to go with your source list. Use PR’s standard pitch brief, or if one doesn’t exist, create one. Include headline stats, agreed-upon positioning language, and target URLs.
Whoever sends the final pitch customizes it to their contact. But using the shared pitch doc as a starting point ensures your basic story stays consistent.
Split Pitching by Strengths
Many high-priority pitches will need both PR and SEO to weigh in. But not all.
Divide the work of pitching based on what each team does best.
Generally, that means structured, technical placements for SEO and editorial, relationship-based placements for PR.
Your company may want to organize these tasks differently depending on industry or org structure, but here’s what I suggest:
| SEO | PR |
|---|---|
| Pitch for inclusion in industry listicles | Pitch journalists and editors on newsworthy content |
| Fix unlinked brand mentions | Offer expert commentary to reporters |
| Reach out to sites with broken or outdated links | Submit to industry awards |
| Identify warm contacts from referring domains | Brief analysts at firms like Gartner and Forrester |
| Monitor AI citations for new outreach targets | Explore sponsored placements in newsletters, podcasts, and trade publications |
Meet quarterly or monthly — whatever works for your schedules — to decide who is going to pitch what, to which outlets, and when.
This will help prioritize high-impact efforts and reduce accidental duplication of work.
Map outlets to objectives and target KPIs to determine ownership.
Every time you meet, review results from the last period. Prioritize more of what’s working and cut what isn’t.
Further reading: Journalist Outreach: 9 Steps to Earn High-Authority Links
PR and SEO usually track different metrics, like mentions and outlet quality vs. rankings and organic traffic.
The fix isn’t merging into a single dashboard.
It’s building a shared lens for evaluating what each asset actually did, no matter which team owns it.
Britt recommends that both teams agree on a shared set of questions to evaluate each asset:
As Britt puts it:
The best shared work usually helps with more than one thing at a time, like visibility, authority, discoverability, and brand credibility.
Getting in front of your audience more often — and in the places they care about — is one of the main advantages of having PR and SEO collaborate.
Track these metrics to see if it’s working:
For AI specifically, track how often your brand appears in AI answers for queries you care about.
You can manually check your top questions and prompts in LLMs to see if your brand is mentioned, but this gets tedious at scale.
The AI Visibility Toolkit is helpful here. It automates tracking so you’re not manually checking every LLM for every query.
You get an overall AI Visibility score for your brand, which measures how often you’re mentioned in AI systems compared to other brands.
The Competitor Research tool shows how your AI visibility stacks up against competitors, which is one of the clearest ways to show leadership whether you’re gaining or losing ground.
It also tracks your Share of Voice across AI platforms, a single metric that reflects the combined impact of your PR and SEO efforts.
This is where you show if your brand is becoming a trusted source online.
Start by tracking new referring domains.
New backlinks matter too, but new domains are more meaningful because they represent more unique sources vouching for your brand.
Reporting on your website authority is also helpful. This is a third-party estimate of the level of trust search engines are likely to assign to your domain, based on your backlink profile and other signals.
Different SEO tools calculate it differently (and call it different things).
So, focus less on the score and more on the direction it moves over time.
Note: Meaningful changes to your Authority Score can take 3-6 months to appear.
The AI Visibility Toolkit tracks your mentions, citations, and cited pages over time, and tells you percentage increases and decreases.
When your authority score and AI mentions are both climbing, you’ll know your PR and SEO work is paying off.
Expert commentary placements, direct requests from journalists, and new journalist relationships are also worth tracking.
Increases in any of those areas are a strong signal that you’re gaining trust.
Google Alerts can catch mentions to help you track expert commentary placements, but a tool like Semrush’s Brand Monitoring gives you a more comprehensive picture.
It lets you track any query (SME names or other keywords) and provides:
Did improving visibility and authority have any impact on your business goals and revenue?
PR and SEO sometimes sit at the top of the funnel, so this can be tricky to answer.
Start with these metrics to prove demand:
Track your referral traffic to show the number of visitors who visit your site directly from media coverage.
Even if numbers are low, they’ll tell you which topics make your audience want to know more about you. Then you can publish more on those in the future.
Tracking assisted conversions shows you conversions where organic search or referral traffic appeared somewhere in the buyer’s journey, but not necessarily as the last click.
PR and SEO content may not convert on the first visit, but it still influences the buyer’s journey.
This metric captures that concept.
Find this in GA4 under Advertising > Key event attribution paths, and switch to “Source/Medium” to see which specific outlets have the most impact.
As AI search has decreased click-through rates, branded search queries have become one of the clearest signals that your PR and SEO efforts are building real awareness.
It’s a metric Britt prioritizes for exactly this reason:
I track branded search lift because it’s a sign that coverage or visibility made someone curious enough to go look up the company by name. That matters to me because not every asset will result in direct clicks.
The metric is also important to Rola:
Branded search lift connects awareness and intent, showing how media exposure actually drives people to seek out your brand.
Google Search Console tells you how often people search for your brand by name and how many of those searches result in a click to your site.
Look for spikes around major coverage dates to directly tie increases to your PR and SEO efforts.
The brands earning the most trust right now aren’t doing it with PR or SEO in siloes.
They’re showing up consistently across media, blogs, review sites, search engines, and AI because all of those channels feed the same authority signals.
That takes more than a “quick sync” before campaigns. It takes an always-on partnership.
You don’t need to overhaul everything at once.
Start small:
When you’re ready to go deeper on how to optimize your brand’s presence in AI, check out our complete guide to AI optimization.
The post PR and SEO: How to Build More Authority Together (5 Steps) appeared first on Backlinko.
2026-05-01 03:59:14
Your analytics dashboard tracks clicks, but it doesn’t convey the complete picture.
When a buyer reads an AI answer that mentions your competitor, or scrolls through a Reddit thread where your brand doesn’t appear, that’s lost visibility. And it won’t show up anywhere in your traffic data.
Share of voice (SoV) captures what traffic metrics can’t.
It measures your brand’s visibility against competitors across channels where buyers actually research and make decisions.
While SoV spans social, PR, and paid media, search is where most brands should start. It’s the channel where buyers with the strongest purchase intent show up, and it’s the easiest to measure competitively. That’s what this guide focuses on.
I’ll walk you through four steps to measure your share of voice in organic and AI search. Then, I’ll show you how to turn that data into decisions that move the needle where it matters.
Share of voice measures your brand’s visibility relative to competitors across multiple marketing channels.
That includes organic and AI search, social media, review sites, communities, and more.
Traditionally, brands used SoV to track their share of ad spend in a market.
Now it’s evolved into something even more valuable. It can measure your brand’s presence across every touchpoint where buyers research and make decisions.
In simple terms: SoV tells you what percentage of the conversation you own in your category, compared to competitors.
This guide focuses on search SoV — both organic and AI — because that’s where buyer discovery is shifting fastest and where the measurement tools have matured enough to give you actionable data.
I find that search SoV also tends to be the foundation: once you understand your visibility in organic and AI results, layering in other channels becomes much simpler.
While there’s no universal benchmark for SoV, establishing one for your brand comes down to:
Beyond these two factors, look at the broader market shifts within your category.
High SoV in a declining market can be a vanity metric. The real win is growing your share as the category grows.
Both SEO and AI SoV answer the same question: What percentage of category demand does your brand own?
But they measure different search contexts.
SEO SoV calculates your slice of traditional organic search traffic.
You track 100 target keywords. Those keywords generate 50,000 total monthly visits across all ranking sites. You capture 15,000 of those visits.
That’s 30% organic share of voice.
AI SoV measures brand mentions in LLM responses from ChatGPT, Perplexity, Google AI Mode, and similar tools.
For example, you test 100 category-related prompts. Your brand is mentioned in 45 responses and cited in 15. Your competitor shows up in 30 responses with 10 mentions.
An AI visibility tool can calculate your weighted AI SoV based on mentions and citations.
Try now: Curious to know how often your brand shows up in AI responses? Try our free AI visibility checker to find out.
Here are three reasons why share of voice should be your core KPI when visibility is scattered across platforms.
Your organic traffic data reveals only half the story.
And with zero-click searches on the rise, that half is shrinking fast.
When users get their answers directly from AI Overviews and featured snippets, a huge chunk of your visibility is never captured in Google Analytics.
This makes traffic a lagging indicator of visibility.
Share of voice is a better metric because it measures how visible you are in the consideration set, even when users don’t click your site.
Think of it this way:
A user searches for the “best project management software for remote teams.”
They see an AI Overview listing five tools, including yours. The user reads it, takes no action, and later signs up for a product demo on your site.
Traditional traffic data would show this as “direct traffic” since the person went straight to the website. It wouldn’t capture the discovery that occurred in Google.
But SoV reveals that your brand appeared in the consideration set for this high-intent query.
Your marketing team might be operating in silos.
The SEO team wants more website visits. PR wants more media mentions. The social team wants better engagement.
Each team tracks its own KPIs and optimizes for different outcomes.
But the long-term power of SoV is that it can become the one metric every team rallies around.
When everyone sees how their work contributes to the same visibility percentage, it changes how teams collaborate.
Here’s what this looks like in practice:
This full picture takes time to build.
Start with the foundation by measuring your SoV in organic and AI search.
Once you have that baseline, you can layer in other channels over time.
Let’s see how you can strategically calculate share of voice in four steps.
I’ll use a fictional project management software example to show how each step translates into business insights.
Start by outlining the specific competitors and keywords you’ll track for SoV.
Without clear boundaries, you’ll either miss critical gaps or drown in too much noise.
To map your competitive terrain, pick topic clusters tied to revenue.
For a project management software, I picked these clusters:
Pro tip: Don’t pick these topics solely based on search volume. Choose clusters where gaining visibility directly impacts your bottom line.
One way to assess a topic’s revenue potential is to map it to funnel stages.
Categorize your clusters into three stages:
Your SoV at each stage tells you where you’re winning and losing in the buyer journey.
This allows you to allocate resources for maximum business impact.
Let’s say this project management software segments the SoV by funnel stage.
It reveals that most of the brand’s visibility is concentrated at the top with almost none at the decision stage.
That’s a problem.
They’re educating the market, but invisible when prospects are actually comparing options and reaching for their wallets.
Strategic takeaway: They need to prioritize comparison pages and case studies to shift visibility toward the decision stage.
Now, define who you’re measuring against.
In search, you’re competing for visibility against two key players:
Tracking them gives you the complete picture of who controls visibility in your market and where you can break through.
Create a library of 200-500 queries that capture how people search in your category.
You need both keywords (what people search) and prompts (what people ask LLMs). Together, they reveal your search visibility spectrum.
Collect queries where you’re already visible to your audience.
Google Search Console (GSC) is a good starting point for this since it captures actual visibility through impressions.
Impressions show every time your brand appears in results, even when users don’t click.
Go to the “Queries” tab in the “Performance” report.
Click the “Impressions” column header to sort in descending order, and export this list of keywords.
And if you’re running Google Ads, export your PPC keyword list and filter for terms with conversions or high CTR.
You can also repeat this process with tools like Semrush.
Open your Semrush Position Tracking project (or create one for your domain).
Scroll down to the “Top Keywords” section and click the “View all” button.
Adjust the timeline to your preferred range before clicking “Export” to download the full keyword list.
Pro tip: Export all tracked keywords, not just the top money terms. A keyword with 20 monthly searches might seem irrelevant in isolation. But 50 of these collectively represent meaningful category visibility that SoV captures.
Besides your own data, track where competitors show up.
This tells you where to compete directly and where to claim ground that they’ve overlooked.
You can use Semrush’s Keyword Gap tool to find these opportunities.
Add your domain along with up to four competitors, then hit “Compare.”
Filter to the “Missing” section to find keywords with proven search demand that competitors have validated.
You need to build visibility for these terms.
For example, this project management tool could target keywords like “Gantt chart” and “project management software” to boost its SoV.
After sourcing keywords, look at how people search for your category in AI tools.
Since AI search queries tend to be more conversational, they often mirror how people talk in community spaces.
Browse Reddit, Facebook groups, and Slack communities to see how your audience phrases their needs and pain points.
For example, this post reveals that agencies want project management tools that aren’t “too corporate or complex for creative teams.”
A question like that can translate directly into an AI prompt: “What’s the most user-friendly project management tool for small creative agencies?”
For decision-stage prompts, review sites G2 and Capterra (or those relevant to your industry) offer a lot of insights.
G2, for instance, lists popular alternatives for every tool.
This is a ready-made list of “[You] vs [Competitor]” and “alternative to [Competitor]” queries your buyers are likely running in AI search.
You can dig deeper with Semrush AI Visibility Toolkit to find prompts where competitors show up in AI answers, but you don’t.
Go to “Prompt Research” and add any of your core topics, like “agile project management.”
Click “Analyze” to get started.
The tool lists real prompts that generate AI responses for your category, such as “best productivity app” and “companies that use agile software development.”
Jot down the prompts relevant to your primary cluster.
Then, repeat for each of your 3-5 clusters.
Finally, organize everything in a master spreadsheet with columns for:
Once you’re done measuring SoV, this metadata will become your strategic lens.
Use it to decide which clusters to prioritize, which funnel stages are weak, and where SEO and AI visibility diverge.
Here’s what this looks like for the project management software:
Your SoV equals your estimated traffic divided by the total traffic for all tracked brands, multiplied by 100.
Track both SEO and AI SoV to see the full picture of your brand’s visibility.
Start by checking your rankings for all the keywords in your tracking list. Track your competitors’ rankings for the same keyword set.
Each ranking position gets an average share of clicks, like position 1 getting roughly 27%.
This will help in estimating the traffic share per keyword.
Note: These benchmarks for organic search CTR shift over time. It’s also crucial to mention that organic CTRs have been declining as AI-generated answers absorb more clicks before users ever reach the results.
Multiply each keyword’s monthly search volume by the click-through rate for your ranking position to estimate your traffic for that duration.
Then, run the same calculation for each competitor.
Use this data to calculate your SoV.
Add up the estimated traffic across all keywords for each brand. Divide your total by the combined total for all tracked brands and multiply by 100.
This manual approach can be time-intensive, especially when tracking hundreds of keywords across multiple competitors.
Semrush handles this math automatically once you set up tracking correctly.
Go to Semrush Position Tracking and click “Create project.”
Enter your domain, target search engine, device type, and location.
The location setting matters for SoV tracking because search results vary by location.
If you set the location to the United States, but most of your customers are in New York, your SoV might look different than reality.
Pro tip: Start with country-level tracking to establish your baseline. Only segment by region later if local variations impact your business.
Then, click “Continue to Keywords” to manually add or import your keyword list.
Upload the CSV you made in Step 2 to preserve the data by cluster and funnel-stage categorization.
Then, press “Add keywords to campaign.”
Finally, click “Start Tracking” to begin data collection.
Once this setup is complete, Semrush starts collecting daily ranking data for every target keyword.
Check out the results in the “Share of Voice” tab under “Overview” in the Position Tracking dashboard.
You can also add up to four domains to see how you fare against others in the market.
Semrush tracks every brand’s rankings for your keyword set to aggregate the data into SoV percentages.
Important: While SoV is inherently relative and compares your visibility against others, who you choose as competitors shapes how you interpret your SoV.
Your AI SoV shows how often LLMs cite your brand when answering questions in your category.
There’s no standardized way to manually measure AI SoV yet, but this two-step process gets you close:
Once you’ve tested all prompts, count how many times each brand appeared across all responses.
Divide each brand’s total mentions by the total number of prompts tested, and multiply by 100.
Keep in mind: This calculation gives you a directional read instead of a live metric. AI responses vary by session, phrasing, location, and platform. That’s why it’s important to test regularly and track trends over time.
Measuring AI SoV manually for 20 prompts across three platforms is doable. Doing it for hundreds of prompts while tracking how recommendations shift week over week isn’t.
That’s what Semrush’s AI Visibility Toolkit is built for.
Go to the Brand Performance report in Semrush’s AI Visibility Toolkit.
Enter your domain and click “Analyze.”
Pick an AI platform between ChatGPT, Google AI Mode, or Perplexity.
Switch among these tools to identify any significant gaps in platform-specific LLM visibility.
Once the report is generated, you’ll see a pie chart visualizing the distribution of SoV for your competitors.
The tool tests hundreds of prompts related to your category across ChatGPT, Google AI Mode, and Perplexity to measure your AI SoV.
For each prompt, it analyzes AI responses for:
It aggregates this data across all tested prompts to calculate your percentage of total visibility.
You’ll also find a section comparing each competitor against a set of business drivers specific to your industry.
These drivers are the most frequently mentioned topics for your category.
Use this data to identify clusters where you’re stronger and weaker than your competitors.
SEO share of voice measures organic traffic while AI share of voice tracks LLM mentions and citations.
These might not always align.
You can have a strong organic share of voice (ranking on top for many keywords) but a weak AI SoV if LLMs don’t find your content credible.
And brands with more credible content can win a bigger slice of AI SoV even without much visibility in organic search.
Here’s a simple matrix to understand your data:
| High AI SoV | Low AI SoV | |
|---|---|---|
| High SEO SoV | You dominate both traditional and AI search.
Maintain content freshness and expand into adjacent topics to defend your position. |
You rank well, but LLMs don’t cite you.
Implement content chunking to optimize your content for AI search and create citable assets to create credibility that LLMs value. |
| Low SEO SoV | AI tools cite your content even though you don’t rank at the top on organic search.
Improve SEO fundamentals, including title tags, internal linking, site speed, and keyword optimization. |
Focus on depth over breadth.
Create a definitive, well-researched content resource for every core cluster. This is a good start for building visibility on both traditional and AI search. |
Dig deeper: Learn more about building visibility in AI search with LLM seeding.
The final step is turning your SoV numbers into an ongoing tracking system that informs decisions.
Create a baseline dashboard to capture three levels of detail:
Here’s what this could look like for the project management software:
Once your baseline is locked in, set your tracking cadence strategically.
A monthly frequency allows you to spot trends without the need for reacting to noise.
With quarterly deep dives, you can:
This rhythm prevents you from chasing short-term variations and missing critical shifts that impact your category.
Pro tip: Set up notifications in Semrush Position Tracking to get real-time alerts. You’re notified when SoV drops more than a certain threshold in any core cluster.
Not every fluctuation in your SoV requires action.
Here’s how to strategically diagnose gaps in your SoV and prioritize the right tactics to fix them.
Clusters with <10% SoV mean you’re almost invisible.
This is especially damaging in decision-stage queries.
If you have less than 10% visibility when buyers search “best project management software,” you’re not in their consideration set.
At the same time, look for opportunities where competitors dominate, but you can compete.
For example, if your project management tool serves creative agencies but you have zero visibility for “project management for creative teams,” that’s your opening.
Diagnose the cause:
- Search your weak clusters and compare what ranks against what you have
- Check if you lack topic coverage, content depth, or basic optimization
- Look at which competitors dominate and what formats they use
Build topical authority for major business themes.
Create one pillar page with multiple supporting articles.
Build backlinks to your pillar content to establish visibility across every query in that cluster.
For example, if we learn that the project management software needs to gain decision-stage visibility, we could prioritize comparison content.
Build pages targeting “[Your Brand] vs [Competitor]” and category buyer’s guides.
Compare your SoV to actual traffic.
A cluster like “what is project management” might give you a high SoV.
But if only 1% of that traffic converts, you’re likely burning money on the wrong audience.
You’re winning visibility in areas that don’t drive business outcomes. And competitors are capturing high-intent buyers.
Diagnose the cause:
- Check if you’re ranking for awareness content when you need decision-stage visibility
- Look at your traffic-to-conversion ratio by cluster
- Identify if your content attracts the wrong audience (students vs. buyers)
Reallocate resources to high-intent clusters.
Instead of producing more awareness content, shift the budget to bottom-of-funnel content.
This includes comparison pages, case studies, and ROI calculators that target buyers ready to evaluate solutions.
Update existing comparison pages with current data and competitive intelligence.
Keep tabs on competitors gaining ground in your strong clusters.
If a competitor gains over 5% SoV in your strong clusters, it’s an early sign that they’re targeting your territory.
That gap can widen unless you respond to maintain your market share.
Diagnose the cause:
- Analyze what new content or tactics they launched
- Check if they’re winning on review sites, community platforms, or organic search
- Identify if they’re capturing a format you’re missing (video, podcasts, tools)
The fix depends on where your competitors are winning.
If competitors actively feature on review sites, optimize your profiles. Run campaigns to source reviews from happy customers.
If they’re visible on community platforms, proactively engage in communities like Reddit and Slack.
Not all gaps matter equally.
Focus on opportunities that will actually move your revenue pipeline.
Start with high-impact, low-effort wins. Then invest in high-effort moves that compound over time.
| High Impact | Low Impact | |
|---|---|---|
| Low Effort |
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| High Effort |
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Share of voice captures how often you show up across the fragmented platforms where buyers make decisions.
Get started by measuring your current SoV across SEO and AI search with the steps in this guide.
Pick the gap that costs you the most revenue, and strategize the best ways to close it.
Next step: Build your AI optimization gameplan to capture visibility in the fastest-growing search channel.
The post How to Calculate Share of Voice (+ Why it Matters for SEO) appeared first on Backlinko.
