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Reach is down. Engagement is shifting. And we’re probably measuring the wrong thing.
Every few months, the same topic reappears. Reach on social media is down. Algorithms are suppressing organic content. Nobody's seeing anything anymore. The feeds are broken.
And sometimes, the data agrees. Sometimes, it doesn't. What it almost never does is tell a simple story - because marketing measurement has never been simple!
Two new studies put some useful numbers behind the social media measurement challenge. Buffer analysed over 52 million posts across 10 platforms. Metricool looked into 673,658 LinkedIn posts from more than 63,000 accounts. Together, they complicate the narrative in the most useful way possible.
What ‘success looks like’ has changed
Yes, some platforms are showing declining engagement rates. Instagram's median engagement rate fell from around 7.4% in 2024 to around 5.5% in 2025 - a 26% decline.
But here's what that number doesn't tell you: Instagram has increasingly steered users toward views as its primary success metric, which means the traditional engagement rate formula may simply be measuring less of what Instagram is actually optimising for – it was designed for a feed of static images. It doesn't capture saves ("I want to come back to this") or sends ("I want someone else to see this"), neither of which shows up in a public count. The metric didn't break. The platform moved on, and the formula didn't follow.
Meanwhile, Facebook's median engagement rate rose to around 5.6% in 2025 (up from around 5.0% in 2024), Pinterest climbed 23%, and X, despite remaining at the bottom of the engagement-rate rankings, saw a 44% relative gain.
So, is social media performance dying? Not across the board. What's really happening is that each platform is evolving its own definition of what success looks like - and the core metrics we've been using for years aren't keeping pace.
"Engagement" isn't one thing
This is the part most reporting gets wrong. When we talk about engagement rates across platforms as if they're comparable, we're mixing up fundamentally different measurements.
LinkedIn, for example, includes clicks in its engagement rate, while most other platforms don't. The Metricool LinkedIn data highlights this issue; look at the year-over-year numbers for LinkedIn Company Pages: impressions down 10%, likes down 13%, comments down 17%, shares down 11%. On the surface, it looks like a platform losing steam. And if those were your only metrics, you'd probably be worried.
But clicks, the interactions that don't show up publicly on your posts, rose by 5%. Every time someone swipes through a carousel, watches a video, or follows a link, LinkedIn tracks it. When you include those invisible interactions, overall engagement rate went up, from 12.21% to 13.90%.
LinkedIn now surfaces impression metrics for comments, meaning the conversation itself is being measured for reach. And posts don't just generate engagement; they drive profile views and follower conversions that don't register as "engagement" in most reporting dashboards, but are often the actual business outcome the post was trying to achieve.
A post that gets 200 likes and converts 40 profile visitors into followers is doing more useful work than a post that gets 500 likes and sends nobody anywhere. Most analytics tools report the first number. Virtually none report the second.
People are interacting with LinkedIn content more than ever. Just not in ways anyone else can see. And that's not just a LinkedIn story.
Instagram’s most valuable engagement signals — saves and sends — are invisible to everyone except the creator and the algorithm. A save tells Instagram that this content has enough value that someone wants to return to it. A send tells Instagram that this content is worth someone's social capital to share privately.
These are higher-intent signals than a like, and they're exactly the ones the traditional engagement rate formula was never built to capture.
And then there’s the role of comments; not just as engagement, but as content in their own right. The original post is often only the starting point; the real narrative, social proof, and persuasion increasingly unfold in the comment thread.
Across nearly two million posts from 220,000+ accounts on Threads, LinkedIn, Instagram, Facebook, X, and Bluesky, posts where creators reply to comments consistently outperformed those where they don't — on every platform studied.
The estimated engagement lift: Threads +42%, LinkedIn +30%, Instagram +21%, Facebook +9%, X +8%, Bluesky +5%.
The Like button: a great 2009 answer. Just not a 2026 one.
The Like button turned 17 this year. When it launched in 2009, there were no carousels, no Reels, no Stories. The feed was a chronological stream of status updates and photos. Engagement meant just one of two things: you pressed a button, or you left a comment.
Now, swiping through a carousel is engagement. Saving a post to return to later is engagement. Sending something to a colleague with a note is engagement. Clicking through to someone's profile after reading their post is engagement. None of these behaviours fit into the original model, and yet they're now among the highest-intent signals a platform receives about whether content is actually working.
The platforms have been quietly reweighting their algorithms around these user behaviours for years. What's changed is that the gap between what the algorithms are measuring and what most social media managers are reporting. The conversation has not moved on from asking about followers and likes.
The format gap
Both studies independently arrived at the same finding: the formats that dominate in posting volume are almost never the formats that perform best.
On LinkedIn, images account for around 49% of all Company Page posts, and video another 25% — so nearly 75% of all content. Yet carousels, which make up just 7.6% of posts, earn a 49.52% engagement rate. Images earn 5.77%. Video earns 6.91%. Metricool's headline finding: carousels get 11x more interactions than images, yet images are posted 6x more than carousels.
Metricool also found that LinkedIn posts including a direct question see 77% more comments than average, and posts with a specific call to action to comment see an 80% increase. That's not a hack, it's simply designing content to invite a response rather than passive consumption, and measuring whether the conversation happened, not just whether the post was seen.
There’s a similar story on Instagram — and it’s really a reach story. Reels get 36% more reach than carousels, putting your content in front of people who’ve never heard of you. But carousels earn 109% more engagement per person reached, meaning the people who do see them are going much further in. These aren’t competing findings; they’re pointing to two different strategies for two different goals. Reels are for reach — discovery, new audiences, the top of the funnel. Carousels are for depth — getting the people who already follow you to go further in. The mistake is treating “best format” as a single answer when the platform is really asking: best for what?
And top tip, add music to your carousel and Instagram will treat it as a Reel.
And it's worth mentioning polls on LinkedIn, which reach nearly three times more people per post than any other format and are simultaneously the least-used format of all.
Part of the tension here is operational: we're often not creating content that aligns with the engagement outcomes we're actually worried about. Instead, we're defaulting to what is easiest, familiar, or historically “standard” within teams.
Reach: the right question
So is reach actually collapsing? The honest answer is: it depends what you’re posting, and why. Organic reach has undeniably become harder on some platforms — LinkedIn impressions for Company Pages fell 10% year-on-year, and algorithm-driven feeds mean fewer posts surface to non-followers by default. That’s real.
But reach is not evenly distributed — it’s format-dependent. LinkedIn polls reach nearly three times more people per post than any other format on the platform, yet they’re the least-used format of all. On Instagram, Reels consistently outperform carousels on raw reach. The platforms aren’t suppressing content uniformly; they’re rewarding formats they want to promote. Knowing which formats those are is the more useful question than asking whether reach is “up or down” overall.
And here’s the more fundamental point: reach was never the destination. It was always a proxy — a way of estimating whether the right people might have seen something. If your content is generating the outcomes you actually care about (pipeline, profile growth, inbound enquiries, qualified conversations), then lower headline reach numbers are almost irrelevant. A post seen by 500 of exactly the right people that drives three meaningful conversations is doing more useful work than a post seen by 50,000 people who scroll straight past. The question worth asking isn’t “how do I get more reach?” — it’s “is my reach reaching the right people, and are they doing anything as a result?”
The boring truth about consistency
In the analysis of 4.8 million observations across approximately 161,000 profiles on Facebook, Instagram, and X, Buffer found that accounts that went quiet for a week consistently underperformed their own baseline growth rates. They called it the "no-post penalty."
The headline isn't that you need to post more. It's that going quiet has a penalty.
What engagement is really becoming
The shift from public to behind-the-scenes engagement isn't a temporary algorithm quirk. It reflects something real about how people use these platforms now — more intentional, more private, more likely to save something for later or send it to one specific person than to perform a public reaction to it.
For individuals and organisations, the metrics worth building dashboards around are increasingly the ones that don't show up in the public domain.
And underneath all of it, the findings from both studies point to the same foundational behaviours: show up consistently, use the formats that generate intentional engagement, and be social on social.
And when someone asks whether your reach or engagement is down — ask them what happened next. Because if the right people saw it, engaged with it in ways that mattered, and something moved as a result, those are not metrics problems. That's the whole point.
Sources: Buffer, State of Social Media Engagement 2026; Metricool, LinkedIn Study 2026
Reach is down. Engagement is shifting. And we’re probably measuring the wrong thing.
Every few months, the same topic reappears. Reach on social media is down. Algorithms are suppressing organic content. Nobody's seeing anything anymore. The feeds are broken.
And sometimes, the data agrees. Sometimes, it doesn't. What it almost never does is tell a simple story - because marketing measurement has never been simple!
Two new studies put some useful numbers behind the social media measurement challenge. Buffer analysed over 52 million posts across 10 platforms. Metricool looked into 673,658 LinkedIn posts from more than 63,000 accounts. Together, they complicate the narrative in the most useful way possible.
What ‘success looks like’ has changed
Yes, some platforms are showing declining engagement rates. Instagram's median engagement rate fell from around 7.4% in 2024 to around 5.5% in 2025 - a 26% decline.
But here's what that number doesn't tell you: Instagram has increasingly steered users toward views as its primary success metric, which means the traditional engagement rate formula may simply be measuring less of what Instagram is actually optimising for – it was designed for a feed of static images. It doesn't capture saves ("I want to come back to this") or sends ("I want someone else to see this"), neither of which shows up in a public count. The metric didn't break. The platform moved on, and the formula didn't follow.
Meanwhile, Facebook's median engagement rate rose to around 5.6% in 2025 (up from around 5.0% in 2024), Pinterest climbed 23%, and X, despite remaining at the bottom of the engagement-rate rankings, saw a 44% relative gain.
So, is social media performance dying? Not across the board. What's really happening is that each platform is evolving its own definition of what success looks like - and the core metrics we've been using for years aren't keeping pace.
"Engagement" isn't one thing
This is the part most reporting gets wrong. When we talk about engagement rates across platforms as if they're comparable, we're mixing up fundamentally different measurements.
LinkedIn, for example, includes clicks in its engagement rate, while most other platforms don't. The Metricool LinkedIn data highlights this issue; look at the year-over-year numbers for LinkedIn Company Pages: impressions down 10%, likes down 13%, comments down 17%, shares down 11%. On the surface, it looks like a platform losing steam. And if those were your only metrics, you'd probably be worried.
But clicks, the interactions that don't show up publicly on your posts, rose by 5%. Every time someone swipes through a carousel, watches a video, or follows a link, LinkedIn tracks it. When you include those invisible interactions, overall engagement rate went up, from 12.21% to 13.90%.
LinkedIn now surfaces impression metrics for comments, meaning the conversation itself is being measured for reach. And posts don't just generate engagement; they drive profile views and follower conversions that don't register as "engagement" in most reporting dashboards, but are often the actual business outcome the post was trying to achieve.
A post that gets 200 likes and converts 40 profile visitors into followers is doing more useful work than a post that gets 500 likes and sends nobody anywhere. Most analytics tools report the first number. Virtually none report the second.
People are interacting with LinkedIn content more than ever. Just not in ways anyone else can see. And that's not just a LinkedIn story.
Instagram’s most valuable engagement signals — saves and sends — are invisible to everyone except the creator and the algorithm. A save tells Instagram that this content has enough value that someone wants to return to it. A send tells Instagram that this content is worth someone's social capital to share privately.
These are higher-intent signals than a like, and they're exactly the ones the traditional engagement rate formula was never built to capture.
And then there’s the role of comments; not just as engagement, but as content in their own right. The original post is often only the starting point; the real narrative, social proof, and persuasion increasingly unfold in the comment thread.
Across nearly two million posts from 220,000+ accounts on Threads, LinkedIn, Instagram, Facebook, X, and Bluesky, posts where creators reply to comments consistently outperformed those where they don't — on every platform studied.
The estimated engagement lift: Threads +42%, LinkedIn +30%, Instagram +21%, Facebook +9%, X +8%, Bluesky +5%.
The Like button: a great 2009 answer. Just not a 2026 one.
The Like button turned 17 this year. When it launched in 2009, there were no carousels, no Reels, no Stories. The feed was a chronological stream of status updates and photos. Engagement meant just one of two things: you pressed a button, or you left a comment.
Now, swiping through a carousel is engagement. Saving a post to return to later is engagement. Sending something to a colleague with a note is engagement. Clicking through to someone's profile after reading their post is engagement. None of these behaviours fit into the original model, and yet they're now among the highest-intent signals a platform receives about whether content is actually working.
The platforms have been quietly reweighting their algorithms around these user behaviours for years. What's changed is that the gap between what the algorithms are measuring and what most social media managers are reporting. The conversation has not moved on from asking about followers and likes.
The format gap
Both studies independently arrived at the same finding: the formats that dominate in posting volume are almost never the formats that perform best.
On LinkedIn, images account for around 49% of all Company Page posts, and video another 25% — so nearly 75% of all content. Yet carousels, which make up just 7.6% of posts, earn a 49.52% engagement rate. Images earn 5.77%. Video earns 6.91%. Metricool's headline finding: carousels get 11x more interactions than images, yet images are posted 6x more than carousels.
Metricool also found that LinkedIn posts including a direct question see 77% more comments than average, and posts with a specific call to action to comment see an 80% increase. That's not a hack, it's simply designing content to invite a response rather than passive consumption, and measuring whether the conversation happened, not just whether the post was seen.
There’s a similar story on Instagram — and it’s really a reach story. Reels get 36% more reach than carousels, putting your content in front of people who’ve never heard of you. But carousels earn 109% more engagement per person reached, meaning the people who do see them are going much further in. These aren’t competing findings; they’re pointing to two different strategies for two different goals. Reels are for reach — discovery, new audiences, the top of the funnel. Carousels are for depth — getting the people who already follow you to go further in. The mistake is treating “best format” as a single answer when the platform is really asking: best for what?
And top tip, add music to your carousel and Instagram will treat it as a Reel.
And it's worth mentioning polls on LinkedIn, which reach nearly three times more people per post than any other format and are simultaneously the least-used format of all.
Part of the tension here is operational: we're often not creating content that aligns with the engagement outcomes we're actually worried about. Instead, we're defaulting to what is easiest, familiar, or historically “standard” within teams.
Reach: the right question
So is reach actually collapsing? The honest answer is: it depends what you’re posting, and why. Organic reach has undeniably become harder on some platforms — LinkedIn impressions for Company Pages fell 10% year-on-year, and algorithm-driven feeds mean fewer posts surface to non-followers by default. That’s real.
But reach is not evenly distributed — it’s format-dependent. LinkedIn polls reach nearly three times more people per post than any other format on the platform, yet they’re the least-used format of all. On Instagram, Reels consistently outperform carousels on raw reach. The platforms aren’t suppressing content uniformly; they’re rewarding formats they want to promote. Knowing which formats those are is the more useful question than asking whether reach is “up or down” overall.
And here’s the more fundamental point: reach was never the destination. It was always a proxy — a way of estimating whether the right people might have seen something. If your content is generating the outcomes you actually care about (pipeline, profile growth, inbound enquiries, qualified conversations), then lower headline reach numbers are almost irrelevant. A post seen by 500 of exactly the right people that drives three meaningful conversations is doing more useful work than a post seen by 50,000 people who scroll straight past. The question worth asking isn’t “how do I get more reach?” — it’s “is my reach reaching the right people, and are they doing anything as a result?”
The boring truth about consistency
In the analysis of 4.8 million observations across approximately 161,000 profiles on Facebook, Instagram, and X, Buffer found that accounts that went quiet for a week consistently underperformed their own baseline growth rates. They called it the "no-post penalty."
The headline isn't that you need to post more. It's that going quiet has a penalty.
What engagement is really becoming
The shift from public to behind-the-scenes engagement isn't a temporary algorithm quirk. It reflects something real about how people use these platforms now — more intentional, more private, more likely to save something for later or send it to one specific person than to perform a public reaction to it.
For individuals and organisations, the metrics worth building dashboards around are increasingly the ones that don't show up in the public domain.
And underneath all of it, the findings from both studies point to the same foundational behaviours: show up consistently, use the formats that generate intentional engagement, and be social on social.
And when someone asks whether your reach or engagement is down — ask them what happened next. Because if the right people saw it, engaged with it in ways that mattered, and something moved as a result, those are not metrics problems. That's the whole point.
Sources: Buffer, State of Social Media Engagement 2026; Metricool, LinkedIn Study 2026
Reach is down. Engagement is shifting. And we’re probably measuring the wrong thing.
Every few months, the same topic reappears. Reach on social media is down. Algorithms are suppressing organic content. Nobody's seeing anything anymore. The feeds are broken.
And sometimes, the data agrees. Sometimes, it doesn't. What it almost never does is tell a simple story - because marketing measurement has never been simple!
Two new studies put some useful numbers behind the social media measurement challenge. Buffer analysed over 52 million posts across 10 platforms. Metricool looked into 673,658 LinkedIn posts from more than 63,000 accounts. Together, they complicate the narrative in the most useful way possible.
What ‘success looks like’ has changed
Yes, some platforms are showing declining engagement rates. Instagram's median engagement rate fell from around 7.4% in 2024 to around 5.5% in 2025 - a 26% decline.
But here's what that number doesn't tell you: Instagram has increasingly steered users toward views as its primary success metric, which means the traditional engagement rate formula may simply be measuring less of what Instagram is actually optimising for – it was designed for a feed of static images. It doesn't capture saves ("I want to come back to this") or sends ("I want someone else to see this"), neither of which shows up in a public count. The metric didn't break. The platform moved on, and the formula didn't follow.
Meanwhile, Facebook's median engagement rate rose to around 5.6% in 2025 (up from around 5.0% in 2024), Pinterest climbed 23%, and X, despite remaining at the bottom of the engagement-rate rankings, saw a 44% relative gain.
So, is social media performance dying? Not across the board. What's really happening is that each platform is evolving its own definition of what success looks like - and the core metrics we've been using for years aren't keeping pace.
"Engagement" isn't one thing
This is the part most reporting gets wrong. When we talk about engagement rates across platforms as if they're comparable, we're mixing up fundamentally different measurements.
LinkedIn, for example, includes clicks in its engagement rate, while most other platforms don't. The Metricool LinkedIn data highlights this issue; look at the year-over-year numbers for LinkedIn Company Pages: impressions down 10%, likes down 13%, comments down 17%, shares down 11%. On the surface, it looks like a platform losing steam. And if those were your only metrics, you'd probably be worried.
But clicks, the interactions that don't show up publicly on your posts, rose by 5%. Every time someone swipes through a carousel, watches a video, or follows a link, LinkedIn tracks it. When you include those invisible interactions, overall engagement rate went up, from 12.21% to 13.90%.
LinkedIn now surfaces impression metrics for comments, meaning the conversation itself is being measured for reach. And posts don't just generate engagement; they drive profile views and follower conversions that don't register as "engagement" in most reporting dashboards, but are often the actual business outcome the post was trying to achieve.
A post that gets 200 likes and converts 40 profile visitors into followers is doing more useful work than a post that gets 500 likes and sends nobody anywhere. Most analytics tools report the first number. Virtually none report the second.
People are interacting with LinkedIn content more than ever. Just not in ways anyone else can see. And that's not just a LinkedIn story.
Instagram’s most valuable engagement signals — saves and sends — are invisible to everyone except the creator and the algorithm. A save tells Instagram that this content has enough value that someone wants to return to it. A send tells Instagram that this content is worth someone's social capital to share privately.
These are higher-intent signals than a like, and they're exactly the ones the traditional engagement rate formula was never built to capture.
And then there’s the role of comments; not just as engagement, but as content in their own right. The original post is often only the starting point; the real narrative, social proof, and persuasion increasingly unfold in the comment thread.
Across nearly two million posts from 220,000+ accounts on Threads, LinkedIn, Instagram, Facebook, X, and Bluesky, posts where creators reply to comments consistently outperformed those where they don't — on every platform studied.
The estimated engagement lift: Threads +42%, LinkedIn +30%, Instagram +21%, Facebook +9%, X +8%, Bluesky +5%.
The Like button: a great 2009 answer. Just not a 2026 one.
The Like button turned 17 this year. When it launched in 2009, there were no carousels, no Reels, no Stories. The feed was a chronological stream of status updates and photos. Engagement meant just one of two things: you pressed a button, or you left a comment.
Now, swiping through a carousel is engagement. Saving a post to return to later is engagement. Sending something to a colleague with a note is engagement. Clicking through to someone's profile after reading their post is engagement. None of these behaviours fit into the original model, and yet they're now among the highest-intent signals a platform receives about whether content is actually working.
The platforms have been quietly reweighting their algorithms around these user behaviours for years. What's changed is that the gap between what the algorithms are measuring and what most social media managers are reporting. The conversation has not moved on from asking about followers and likes.
The format gap
Both studies independently arrived at the same finding: the formats that dominate in posting volume are almost never the formats that perform best.
On LinkedIn, images account for around 49% of all Company Page posts, and video another 25% — so nearly 75% of all content. Yet carousels, which make up just 7.6% of posts, earn a 49.52% engagement rate. Images earn 5.77%. Video earns 6.91%. Metricool's headline finding: carousels get 11x more interactions than images, yet images are posted 6x more than carousels.
Metricool also found that LinkedIn posts including a direct question see 77% more comments than average, and posts with a specific call to action to comment see an 80% increase. That's not a hack, it's simply designing content to invite a response rather than passive consumption, and measuring whether the conversation happened, not just whether the post was seen.
There’s a similar story on Instagram — and it’s really a reach story. Reels get 36% more reach than carousels, putting your content in front of people who’ve never heard of you. But carousels earn 109% more engagement per person reached, meaning the people who do see them are going much further in. These aren’t competing findings; they’re pointing to two different strategies for two different goals. Reels are for reach — discovery, new audiences, the top of the funnel. Carousels are for depth — getting the people who already follow you to go further in. The mistake is treating “best format” as a single answer when the platform is really asking: best for what?
And top tip, add music to your carousel and Instagram will treat it as a Reel.
And it's worth mentioning polls on LinkedIn, which reach nearly three times more people per post than any other format and are simultaneously the least-used format of all.
Part of the tension here is operational: we're often not creating content that aligns with the engagement outcomes we're actually worried about. Instead, we're defaulting to what is easiest, familiar, or historically “standard” within teams.
Reach: the right question
So is reach actually collapsing? The honest answer is: it depends what you’re posting, and why. Organic reach has undeniably become harder on some platforms — LinkedIn impressions for Company Pages fell 10% year-on-year, and algorithm-driven feeds mean fewer posts surface to non-followers by default. That’s real.
But reach is not evenly distributed — it’s format-dependent. LinkedIn polls reach nearly three times more people per post than any other format on the platform, yet they’re the least-used format of all. On Instagram, Reels consistently outperform carousels on raw reach. The platforms aren’t suppressing content uniformly; they’re rewarding formats they want to promote. Knowing which formats those are is the more useful question than asking whether reach is “up or down” overall.
And here’s the more fundamental point: reach was never the destination. It was always a proxy — a way of estimating whether the right people might have seen something. If your content is generating the outcomes you actually care about (pipeline, profile growth, inbound enquiries, qualified conversations), then lower headline reach numbers are almost irrelevant. A post seen by 500 of exactly the right people that drives three meaningful conversations is doing more useful work than a post seen by 50,000 people who scroll straight past. The question worth asking isn’t “how do I get more reach?” — it’s “is my reach reaching the right people, and are they doing anything as a result?”
The boring truth about consistency
In the analysis of 4.8 million observations across approximately 161,000 profiles on Facebook, Instagram, and X, Buffer found that accounts that went quiet for a week consistently underperformed their own baseline growth rates. They called it the "no-post penalty."
The headline isn't that you need to post more. It's that going quiet has a penalty.
What engagement is really becoming
The shift from public to behind-the-scenes engagement isn't a temporary algorithm quirk. It reflects something real about how people use these platforms now — more intentional, more private, more likely to save something for later or send it to one specific person than to perform a public reaction to it.
For individuals and organisations, the metrics worth building dashboards around are increasingly the ones that don't show up in the public domain.
And underneath all of it, the findings from both studies point to the same foundational behaviours: show up consistently, use the formats that generate intentional engagement, and be social on social.
And when someone asks whether your reach or engagement is down — ask them what happened next. Because if the right people saw it, engaged with it in ways that mattered, and something moved as a result, those are not metrics problems. That's the whole point.
Sources: Buffer, State of Social Media Engagement 2026; Metricool, LinkedIn Study 2026
London
Rich Fitzmaurice
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Reach is down. Engagement is shifting. And we’re probably measuring the wrong thing.
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“I want to leave my Head of Growth Marketing role as I dislike my boss but I am not finding new roles easy to come by. I apply to so many jobs and hear nothing back at all, even ones where I look like an absolutely perfect fit. My 20 odd years of experience is worth something. I've had calls with head-hunters and they are nice to talk to, but I hear nothing.
I am thinking of leaving my role and just rebranding as a fractional CMO which I know you do. But, ideally I want a full-time role as I want the security and benefits. Any advice?"
Charlotte, London
Rich's Reply
I'm sorry you're itching to move but finding it tough, Charlotte.
The market for permanent senior marketing roles in 2026 is brutal. The endless AI doom narrative has marketers sitting still in their current roles out of fear, and just like the housing market, if nobody moves, everything grinds to a halt, and it takes years to get going again. When an exciting role does hit LinkedIn it's a rat race. You can refresh the page and watch the application count climb by the minute. I've heard from people whose same day email alert arrived after the posting had already closed.
That's what you're up against. It sucks. But it sucks far worse for those without a role at all.
You're doing the right things. You're applying. You're talking to head-hunters. You're using your network. You're seeking out advice. That's exactly the activity the market demands right now, so keep going. As always, I'll give you my perspective on what I'd personally recommend, but please seek other opinions too, including any comments on this reply that push back on my view.
I'll leave the boss problem to one side and focus on the bigger decision you're weighing.
You're clear you want to work somewhere else, in-house. So, I would not recommend rebranding as a fractional CMO. I don't think it's a viable interim solution for you, and I wouldn't want you to risk your income and the security you crave on something you only see as a stop-gap. I certainly wouldn't want it to divert your focus from finding the role you actually want.
And I say this having just launched 'How to become a high-performing, high income, fractional CMO'.
There's a lot of confusion over the term. A lot of people are calling themselves a Fractional CMO who probably shouldn't, mostly out of a need to put food on the table, which I will never begrudge anyone. But a fractional CMO isn't a freelancer with a better title. They are not a marketing specialist with a broader remit. Nor a marketer with a fraction of a CMO's skill set (believe it or not, I see some recruiters getting that wrong too).
Being a fractional CMO is harder than it looks, and harder than being a full-time CMO.
Fractional life is not a softer version of in-house. It's a different job. The thing nobody warns you about is that the comfort blanket you're describing, the structure, the team, the rhythm, the meetings that aren't yours to run, the colleagues who say good morning, the salary that turns up regardless of whether you contributed anything that month, all of that disappears on day one.
You wake up and the only person responsible for the pipeline is you. The only person responsible for the work is you. The only person who knows whether you should take that client or not is you. For some marketers that's liberating. For others it's quietly terrifying, and they don't realise which one they are until they're already in it.
Disliking your boss is a tax, not a reason to blow up your career. Pay the tax while you find the right role. Don't make your life ten times harder because of one person.
A few things I'd focus on instead:
Reconnect with old colleagues. Not to pitch. Just to reconnect. Senior roles overwhelmingly come from people who already know you, not from job boards.
Keep working the head-hunters, but accept they will only place you if they have a live mandate that fits and it makes them money. Stay top of mind but don't expect favours.
Get to industry events in person. The rat race on LinkedIn is the worst possible way to find a role. The best roles aren't posted there at all. You need to build the connections that help you uncover those opportunities.
Ask your current employer to invest in you. Projects, leadership training, anything that broadens your network beyond marketing. It costs you nothing (hopefully) and it makes you more valuable wherever you land next. And you wouldn't be the first person to knuckle down, deliver good work, upskill and find themselves getting their boss's job.
Onwards!
Rich
Got a question for Rich? Email it to editor@b2bmarketing.com
“I want to leave my Head of Growth Marketing role as I dislike my boss but I am not finding new roles easy to come by. I apply to so many jobs and hear nothing back at all, even ones where I look like an absolutely perfect fit. My 20 odd years of experience is worth something. I've had calls with head-hunters and they are nice to talk to, but I hear nothing.
I am thinking of leaving my role and just rebranding as a fractional CMO which I know you do. But, ideally I want a full-time role as I want the security and benefits. Any advice?"
Charlotte, London
Rich's Reply
I'm sorry you're itching to move but finding it tough, Charlotte.
The market for permanent senior marketing roles in 2026 is brutal. The endless AI doom narrative has marketers sitting still in their current roles out of fear, and just like the housing market, if nobody moves, everything grinds to a halt, and it takes years to get going again. When an exciting role does hit LinkedIn it's a rat race. You can refresh the page and watch the application count climb by the minute. I've heard from people whose same day email alert arrived after the posting had already closed.
That's what you're up against. It sucks. But it sucks far worse for those without a role at all.
You're doing the right things. You're applying. You're talking to head-hunters. You're using your network. You're seeking out advice. That's exactly the activity the market demands right now, so keep going. As always, I'll give you my perspective on what I'd personally recommend, but please seek other opinions too, including any comments on this reply that push back on my view.
I'll leave the boss problem to one side and focus on the bigger decision you're weighing.
You're clear you want to work somewhere else, in-house. So, I would not recommend rebranding as a fractional CMO. I don't think it's a viable interim solution for you, and I wouldn't want you to risk your income and the security you crave on something you only see as a stop-gap. I certainly wouldn't want it to divert your focus from finding the role you actually want.
And I say this having just launched 'How to become a high-performing, high income, fractional CMO'.
There's a lot of confusion over the term. A lot of people are calling themselves a Fractional CMO who probably shouldn't, mostly out of a need to put food on the table, which I will never begrudge anyone. But a fractional CMO isn't a freelancer with a better title. They are not a marketing specialist with a broader remit. Nor a marketer with a fraction of a CMO's skill set (believe it or not, I see some recruiters getting that wrong too).
Being a fractional CMO is harder than it looks, and harder than being a full-time CMO.
Fractional life is not a softer version of in-house. It's a different job. The thing nobody warns you about is that the comfort blanket you're describing, the structure, the team, the rhythm, the meetings that aren't yours to run, the colleagues who say good morning, the salary that turns up regardless of whether you contributed anything that month, all of that disappears on day one.
You wake up and the only person responsible for the pipeline is you. The only person responsible for the work is you. The only person who knows whether you should take that client or not is you. For some marketers that's liberating. For others it's quietly terrifying, and they don't realise which one they are until they're already in it.
Disliking your boss is a tax, not a reason to blow up your career. Pay the tax while you find the right role. Don't make your life ten times harder because of one person.
A few things I'd focus on instead:
Reconnect with old colleagues. Not to pitch. Just to reconnect. Senior roles overwhelmingly come from people who already know you, not from job boards.
Keep working the head-hunters, but accept they will only place you if they have a live mandate that fits and it makes them money. Stay top of mind but don't expect favours.
Get to industry events in person. The rat race on LinkedIn is the worst possible way to find a role. The best roles aren't posted there at all. You need to build the connections that help you uncover those opportunities.
Ask your current employer to invest in you. Projects, leadership training, anything that broadens your network beyond marketing. It costs you nothing (hopefully) and it makes you more valuable wherever you land next. And you wouldn't be the first person to knuckle down, deliver good work, upskill and find themselves getting their boss's job.
Onwards!
Rich
Got a question for Rich? Email it to editor@b2bmarketing.com
“I want to leave my Head of Growth Marketing role as I dislike my boss but I am not finding new roles easy to come by. I apply to so many jobs and hear nothing back at all, even ones where I look like an absolutely perfect fit. My 20 odd years of experience is worth something. I've had calls with head-hunters and they are nice to talk to, but I hear nothing.
I am thinking of leaving my role and just rebranding as a fractional CMO which I know you do. But, ideally I want a full-time role as I want the security and benefits. Any advice?"
Charlotte, London
Rich's Reply
I'm sorry you're itching to move but finding it tough, Charlotte.
The market for permanent senior marketing roles in 2026 is brutal. The endless AI doom narrative has marketers sitting still in their current roles out of fear, and just like the housing market, if nobody moves, everything grinds to a halt, and it takes years to get going again. When an exciting role does hit LinkedIn it's a rat race. You can refresh the page and watch the application count climb by the minute. I've heard from people whose same day email alert arrived after the posting had already closed.
That's what you're up against. It sucks. But it sucks far worse for those without a role at all.
You're doing the right things. You're applying. You're talking to head-hunters. You're using your network. You're seeking out advice. That's exactly the activity the market demands right now, so keep going. As always, I'll give you my perspective on what I'd personally recommend, but please seek other opinions too, including any comments on this reply that push back on my view.
I'll leave the boss problem to one side and focus on the bigger decision you're weighing.
You're clear you want to work somewhere else, in-house. So, I would not recommend rebranding as a fractional CMO. I don't think it's a viable interim solution for you, and I wouldn't want you to risk your income and the security you crave on something you only see as a stop-gap. I certainly wouldn't want it to divert your focus from finding the role you actually want.
And I say this having just launched 'How to become a high-performing, high income, fractional CMO'.
There's a lot of confusion over the term. A lot of people are calling themselves a Fractional CMO who probably shouldn't, mostly out of a need to put food on the table, which I will never begrudge anyone. But a fractional CMO isn't a freelancer with a better title. They are not a marketing specialist with a broader remit. Nor a marketer with a fraction of a CMO's skill set (believe it or not, I see some recruiters getting that wrong too).
Being a fractional CMO is harder than it looks, and harder than being a full-time CMO.
Fractional life is not a softer version of in-house. It's a different job. The thing nobody warns you about is that the comfort blanket you're describing, the structure, the team, the rhythm, the meetings that aren't yours to run, the colleagues who say good morning, the salary that turns up regardless of whether you contributed anything that month, all of that disappears on day one.
You wake up and the only person responsible for the pipeline is you. The only person responsible for the work is you. The only person who knows whether you should take that client or not is you. For some marketers that's liberating. For others it's quietly terrifying, and they don't realise which one they are until they're already in it.
Disliking your boss is a tax, not a reason to blow up your career. Pay the tax while you find the right role. Don't make your life ten times harder because of one person.
A few things I'd focus on instead:
Reconnect with old colleagues. Not to pitch. Just to reconnect. Senior roles overwhelmingly come from people who already know you, not from job boards.
Keep working the head-hunters, but accept they will only place you if they have a live mandate that fits and it makes them money. Stay top of mind but don't expect favours.
Get to industry events in person. The rat race on LinkedIn is the worst possible way to find a role. The best roles aren't posted there at all. You need to build the connections that help you uncover those opportunities.
Ask your current employer to invest in you. Projects, leadership training, anything that broadens your network beyond marketing. It costs you nothing (hopefully) and it makes you more valuable wherever you land next. And you wouldn't be the first person to knuckle down, deliver good work, upskill and find themselves getting their boss's job.
Onwards!
Rich
Got a question for Rich? Email it to editor@b2bmarketing.com
Content
Content
How to's
Here is the number that should be on every B2B CMO's desk this quarter. In G2's August 2025 buyer survey, 87% of B2B software buyers said AI chatbots like ChatGPT, Perplexity, Gemini and Claude are changing how they research purchases. In the same study, only 12% of B2B SaaS brands appeared when buyers ran category-level searches inside those tools. The other 88% were simply absent from the moment their buyers were forming opinions.
That gap, between where buyers are researching and where brands are visible, is the single biggest unpriced risk in B2B marketing right now. It is also not a distant problem. A multi-source analysis published in March 2026, covering 680 million AI citations and almost two million browsing sessions, found that 73% of B2B buyers are already using AI tools inside their purchase research process. Forrester's 2025 buyer study put a number on the downstream consequence. 61% of the B2B buying journey now completes before a buyer ever contacts a vendor, and that figure climbs every time an AI tool synthesises a shortlist on their behalf.
Meanwhile, McKinsey estimates that around 50% of Google searches already include AI summaries, rising above 75% by 2028, and projects that $750 billion in US revenue will funnel through AI-powered search by 2028. This is a rewiring of how B2B buyers discover, compare and choose, not a marginal channel shift.
The discipline that governs whether you are visible inside those AI-generated answers has a name. It is Answer Engine Optimisation (AEO), and it is a different discipline to SEO.
Why SEO tactics don't translate (and why that matters to your budget)
For most of the last two decades, B2B marketers have optimised for a specific user behaviour. Type a query into Google, scan a list of ten blue links, click one. The entire SEO playbook (keyword targeting, backlinks, domain authority, ranking positions) is a set of levers pulled against that behaviour.
Answer engines don't work that way. When a buyer asks ChatGPT "what are the best account-based marketing platforms for mid-market B2B?", the model doesn't show them ten blue links. It synthesises an answer, names two or three vendors, and sometimes cites a handful of sources. The buyer leaves with a shortlist instead of a search results page.
That one behavioural change breaks several of the core assumptions B2B marketers have been budgeting against.
Rankings don't map to citations. A page that ranks #1 on Google may never be cited by ChatGPT, and a page that doesn't rank in Google's top 20 can be cited repeatedly by Perplexity. The 2025 AI Visibility Report from The Digital Bloom found that only 11% of domains cited by ChatGPT were also cited by Perplexity for the same queries. These are different retrieval systems with different signals.
Backlinks are no longer the dominant signal. In the same study, brand search volume was the strongest predictor of AI citations, with a correlation of 0.334. That is materially stronger than any backlink-based metric. Models are increasingly using brand familiarity as a proxy for trust.
Content freshness suddenly matters in a way SEO never rewarded. Roughly 65% of log hits to cited content were for pages published in the last year, and 79% were from the last two years. AI systems lean toward recent, actively maintained sources.
Traditional measurement is silent on the question that matters. GA4 will not tell you how often ChatGPT recommends you. Your rank tracker will not tell you whether Perplexity is citing your competitor. The measurement stack most B2B marketing teams rely on was built for a world where discovery happened on a SERP.
These differences add up. The inputs, the signals, the measurement and the skills required are distinct enough that treating AEO as "the SEO team's next project" is already producing a second wave of wasted budget across the industry.
The four pillars of AEO for B2B marketers
The methodology breaks down into four pillars. They work as concurrent workstreams rather than sequential steps, and each one pulls a different lever.
1. Entity positioning: make the model understand who you are
Before an answer engine can recommend you, it has to understand what you are. Large language models reason in terms of entities: companies, products, categories, people. If the model's internal representation of your brand is vague, incomplete or confused with a competitor, no amount of content will fix it.
Entity positioning is the work of making sure every AI system has an unambiguous understanding of who you are, what category you compete in, what you do better than alternatives, and who your ideal customer is. The practical work includes Wikidata entries, Knowledge Panel optimisation, structured data (Organization, Product, Software Application schemas), and consistent entity descriptions across high-authority third-party sources.
Diagnostic question for your team: open ChatGPT and ask "What does [your brand] do?" Then ask "Who are [your brand]'s main competitors?" If the answers are vague, wrong, or list your brand alongside companies you don't actually compete with, you have an entity problem. Entity sits upstream of every other AEO lever, so this is usually where the work starts.
2. Answer architecture: structure content so models can extract and cite it
Most B2B content is written for humans scrolling on a laptop. Answer engines don't scroll. They extract. Content that wins citations is content that answers specific questions directly, in a structure the model can parse.
The brands that get cited repeatedly across platforms usually aren't the ones producing the most content. They are the ones whose content is architected for extraction: clear definitions, direct answers at the top of sections, FAQ blocks with schema, concise paragraphs, well-structured headers, and explicit comparisons. Seer Interactive's data on AI Overviews found that brands cited in Google's AI answers earn 35% more organic clicks and 91% more paid clicks. The same Seer analysis showed that when AI Overviews are present and your brand is not cited, organic CTR drops 61% and paid CTR drops 68%. Answer architecture is what determines which side of that line you sit on.
3. Source signal: earn mentions in the places LLMs actually weight
Not all sources are weighted equally. Each AI platform draws from a different constellation of trusted inputs, and the differences are significant. The same 2025 cross-platform analysis found that Reddit accounted for 46.7% of top Perplexity citations but under 10% on ChatGPT after a September 2025 rebalancing. ChatGPT leans heavily on Wikipedia and long-standing editorial sources. Google AI Overviews favour a diversified cross-platform presence.
Source signal is the discipline of earning mentions, reviews and references in the specific places each platform treats as authoritative for your category. For a B2B SaaS brand, that usually means G2 and Gartner Peer Insights (G2's own internal study confirms they are heavily cited across LLMs), plus category-specific industry publications, relevant subreddits, and any analyst coverage that ends up in the training corpus. This is PR, earned media and community work done with a very specific retrieval target in mind.
4. Measurement: track what your rank tracker can't see
If you can't measure AI visibility, you can't manage it, and you certainly can't defend the budget in front of a board. The four metrics we track for clients map directly to the commercial question every B2B CMO cares about: are we showing up when our buyers are choosing?
AI Mention Rate. For a defined set of 20 to 50 buyer-intent queries, what percentage of the time does your brand appear in the answer across ChatGPT, Perplexity, Claude, Gemini and Google AI Overviews?
Citation Authority. When those platforms cite sources, how often is your domain among them, and how does that compare to your top three competitors?
Entity Clarity Score. When you ask the model directly "What does [brand] do?", is the answer accurate, complete and differentiated, or generic and interchangeable?
Answer Ownership. For the queries that matter most to your pipeline, are you the primary recommendation or a supporting mention?
This is the layer most B2B marketing teams are missing entirely. Only 22% of marketers currently track AI visibility and traffic, and only 25.7% plan to develop content specifically for AI citations. That gap is, for the moment, the single biggest competitive opportunity in B2B marketing.
You can read the full methodology behind each of these pillars on the [growthvibe AI search optimisation methodology page](https://www.growthvibe.com/ai-search-optimization-services/).
A worked example: what this looks like in martech
Consider a mid-market martech category, say, account-based marketing platforms. Ask ChatGPT, Perplexity and Google's AI Overview the same question: "What are the best account-based marketing platforms for B2B companies with 200 to 2,000 employees?"
Run it once and you'll notice a pattern. Two or three vendors show up consistently across all three engines. A handful appear on one platform but not the others. And a long tail of category players, some of them with significantly better products, bigger marketing budgets and stronger Google rankings, don't appear at all.
When we dig into the brands that win across all three platforms, the same four things are usually true. Their entity is clear (the model knows what they do without hesitation). Their owned content is structured for extraction, with direct answers, comparison pages and FAQ schema. They have strong, recent source signal across G2, Gartner coverage, analyst mentions, and in Perplexity's case, organic Reddit discussion. And they are measuring AI visibility as a discipline rather than guessing at it.
The brands that lose? Usually strong on traditional SEO, weak on entity clarity, inconsistent on answer architecture, and invisible in the places the models trust. Their dashboards look healthy. Their pipeline is quietly leaking.
The mistakes I see most often
After running diagnostics across dozens of B2B brands, the same patterns recur.
Treating AEO as a content problem. Most teams respond to the AI search shift by publishing more blog posts about AI. That is content marketing with the word "AI" in the title. AEO requires work across entity, architecture, source signal and measurement. Content is one of four levers.
Over-indexing on a single platform. Teams obsess over ChatGPT and ignore Perplexity, or vice versa. The retrieval systems are different enough that you need a cross-platform view. The 11% citation overlap figure is the most important number most B2B marketers haven't heard.
Letting the incumbent SEO agency redefine AEO as "SEO plus schema". This is the most expensive mistake in the market right now. Schema helps, but it is a single lever inside the second pillar. If your AEO strategy can be executed by adding FAQ markup, it is not an AEO strategy.
Waiting for measurement to get easier. It will not. The teams winning in 2026 are the ones who built a measurement layer manually, accepted the rough edges, and used the data to make decisions. Waiting for a perfect dashboard is a way of conceding ground to the brands who didn't wait.
A 30-minute AEO self-audit for B2B marketers
If you want to stress-test your own position before the next board meeting, here is a condensed version of the audit we run for clients. It takes about half an hour.
1. The entity test. Open ChatGPT, Perplexity and Google's AI Overview. Ask each one "What does [your brand] do?" and "Who are [your brand]'s main competitors?" Note the answers verbatim. If any of them are wrong, vague or list competitors you don't actually compete with, flag an entity issue.
2. The category test. Run your three most important buyer-intent queries across all three platforms. Example: "What are the best [your category] platforms for [your ICP]?" Note whether your brand appears, and if so, where in the answer.
3. The competitor test. Run the same three queries again, but replace your brand name with each of your top three competitors. Compare who gets cited, how often, and in what position.
4. The source test. For every citation that appears across those queries, note the source. Which domains are being pulled? Is your domain among them? If not, which domains are, and do you have a presence on those surfaces?
5. The freshness test. Check the publication dates on your most important category pages. If your pillar content hasn't been updated in the last 12 to 18 months, you are likely losing ground to competitors whose content is fresher.
If you do nothing else this quarter, run this audit. It will tell you, in thirty minutes, whether you have an AEO problem, and roughly how big it is.
The window is open, and it won't stay open
AEO is in the phase every new marketing discipline goes through before it becomes table stakes. The market data is clear enough that ignoring it is a choice. The measurement tooling is rough but workable. The playbooks are being written in real time by the brands who decided not to wait.
B2B marketing teams that treat AEO as a discipline now, with its own strategy, its own measurement, and its own owned workstream inside the marketing function, will compound an advantage every month their competitors spend arguing about whether it matters. The ones that treat it as a side project, or hand it to an agency still thinking in terms of rankings and links, will spend 2027 trying to catch up on ground they didn't realise they were losing.
The brands being cited by AI today are the brands being chosen tomorrow. Everything else is a dashboard.
Request a free AI Visibility Report
---
*Sources: [G2: Does G2 Get Ranked in AI LLM Search?](https://learn.g2.com/tech-signals-does-g2-get-ranked-in-ai-llm-search); [PR Newswire: 73% of B2B Buyers Use AI Tools in Purchase Research](https://www.prnewswire.com/news-releases/73-of-b2b-buyers-use-ai-tools-in-purchase-research-multi-source-analysis-finds-302733319.html); [The Digital Bloom: 2025 AI Visibility Report](https://thedigitalbloom.com/learn/2025-ai-citation-llm-visibility-report/); [McKinsey: The new front door to the internet](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/new-front-door-to-the-internet-winning-in-the-age-of-ai-search); [Seer Interactive: AIO impact on Google CTR](https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update); [SparkToro: 2024 Zero-Click Search Study](https://sparktoro.com/blog/2024-zero-click-search-study-for-every-1000-us-google-searches-only-374-clicks-go-to-the-open-web-in-the-eu-its-360/); [Adobe: The explosive rise of generative AI referral traffic](https://business.adobe.com/blog/the-explosive-rise-of-generative-ai-referral-traffic); Forrester 2025 B2B Buyer Journey Study.*
Here is the number that should be on every B2B CMO's desk this quarter. In G2's August 2025 buyer survey, 87% of B2B software buyers said AI chatbots like ChatGPT, Perplexity, Gemini and Claude are changing how they research purchases. In the same study, only 12% of B2B SaaS brands appeared when buyers ran category-level searches inside those tools. The other 88% were simply absent from the moment their buyers were forming opinions.
That gap, between where buyers are researching and where brands are visible, is the single biggest unpriced risk in B2B marketing right now. It is also not a distant problem. A multi-source analysis published in March 2026, covering 680 million AI citations and almost two million browsing sessions, found that 73% of B2B buyers are already using AI tools inside their purchase research process. Forrester's 2025 buyer study put a number on the downstream consequence. 61% of the B2B buying journey now completes before a buyer ever contacts a vendor, and that figure climbs every time an AI tool synthesises a shortlist on their behalf.
Meanwhile, McKinsey estimates that around 50% of Google searches already include AI summaries, rising above 75% by 2028, and projects that $750 billion in US revenue will funnel through AI-powered search by 2028. This is a rewiring of how B2B buyers discover, compare and choose, not a marginal channel shift.
The discipline that governs whether you are visible inside those AI-generated answers has a name. It is Answer Engine Optimisation (AEO), and it is a different discipline to SEO.
Why SEO tactics don't translate (and why that matters to your budget)
For most of the last two decades, B2B marketers have optimised for a specific user behaviour. Type a query into Google, scan a list of ten blue links, click one. The entire SEO playbook (keyword targeting, backlinks, domain authority, ranking positions) is a set of levers pulled against that behaviour.
Answer engines don't work that way. When a buyer asks ChatGPT "what are the best account-based marketing platforms for mid-market B2B?", the model doesn't show them ten blue links. It synthesises an answer, names two or three vendors, and sometimes cites a handful of sources. The buyer leaves with a shortlist instead of a search results page.
That one behavioural change breaks several of the core assumptions B2B marketers have been budgeting against.
Rankings don't map to citations. A page that ranks #1 on Google may never be cited by ChatGPT, and a page that doesn't rank in Google's top 20 can be cited repeatedly by Perplexity. The 2025 AI Visibility Report from The Digital Bloom found that only 11% of domains cited by ChatGPT were also cited by Perplexity for the same queries. These are different retrieval systems with different signals.
Backlinks are no longer the dominant signal. In the same study, brand search volume was the strongest predictor of AI citations, with a correlation of 0.334. That is materially stronger than any backlink-based metric. Models are increasingly using brand familiarity as a proxy for trust.
Content freshness suddenly matters in a way SEO never rewarded. Roughly 65% of log hits to cited content were for pages published in the last year, and 79% were from the last two years. AI systems lean toward recent, actively maintained sources.
Traditional measurement is silent on the question that matters. GA4 will not tell you how often ChatGPT recommends you. Your rank tracker will not tell you whether Perplexity is citing your competitor. The measurement stack most B2B marketing teams rely on was built for a world where discovery happened on a SERP.
These differences add up. The inputs, the signals, the measurement and the skills required are distinct enough that treating AEO as "the SEO team's next project" is already producing a second wave of wasted budget across the industry.
The four pillars of AEO for B2B marketers
The methodology breaks down into four pillars. They work as concurrent workstreams rather than sequential steps, and each one pulls a different lever.
1. Entity positioning: make the model understand who you are
Before an answer engine can recommend you, it has to understand what you are. Large language models reason in terms of entities: companies, products, categories, people. If the model's internal representation of your brand is vague, incomplete or confused with a competitor, no amount of content will fix it.
Entity positioning is the work of making sure every AI system has an unambiguous understanding of who you are, what category you compete in, what you do better than alternatives, and who your ideal customer is. The practical work includes Wikidata entries, Knowledge Panel optimisation, structured data (Organization, Product, Software Application schemas), and consistent entity descriptions across high-authority third-party sources.
Diagnostic question for your team: open ChatGPT and ask "What does [your brand] do?" Then ask "Who are [your brand]'s main competitors?" If the answers are vague, wrong, or list your brand alongside companies you don't actually compete with, you have an entity problem. Entity sits upstream of every other AEO lever, so this is usually where the work starts.
2. Answer architecture: structure content so models can extract and cite it
Most B2B content is written for humans scrolling on a laptop. Answer engines don't scroll. They extract. Content that wins citations is content that answers specific questions directly, in a structure the model can parse.
The brands that get cited repeatedly across platforms usually aren't the ones producing the most content. They are the ones whose content is architected for extraction: clear definitions, direct answers at the top of sections, FAQ blocks with schema, concise paragraphs, well-structured headers, and explicit comparisons. Seer Interactive's data on AI Overviews found that brands cited in Google's AI answers earn 35% more organic clicks and 91% more paid clicks. The same Seer analysis showed that when AI Overviews are present and your brand is not cited, organic CTR drops 61% and paid CTR drops 68%. Answer architecture is what determines which side of that line you sit on.
3. Source signal: earn mentions in the places LLMs actually weight
Not all sources are weighted equally. Each AI platform draws from a different constellation of trusted inputs, and the differences are significant. The same 2025 cross-platform analysis found that Reddit accounted for 46.7% of top Perplexity citations but under 10% on ChatGPT after a September 2025 rebalancing. ChatGPT leans heavily on Wikipedia and long-standing editorial sources. Google AI Overviews favour a diversified cross-platform presence.
Source signal is the discipline of earning mentions, reviews and references in the specific places each platform treats as authoritative for your category. For a B2B SaaS brand, that usually means G2 and Gartner Peer Insights (G2's own internal study confirms they are heavily cited across LLMs), plus category-specific industry publications, relevant subreddits, and any analyst coverage that ends up in the training corpus. This is PR, earned media and community work done with a very specific retrieval target in mind.
4. Measurement: track what your rank tracker can't see
If you can't measure AI visibility, you can't manage it, and you certainly can't defend the budget in front of a board. The four metrics we track for clients map directly to the commercial question every B2B CMO cares about: are we showing up when our buyers are choosing?
AI Mention Rate. For a defined set of 20 to 50 buyer-intent queries, what percentage of the time does your brand appear in the answer across ChatGPT, Perplexity, Claude, Gemini and Google AI Overviews?
Citation Authority. When those platforms cite sources, how often is your domain among them, and how does that compare to your top three competitors?
Entity Clarity Score. When you ask the model directly "What does [brand] do?", is the answer accurate, complete and differentiated, or generic and interchangeable?
Answer Ownership. For the queries that matter most to your pipeline, are you the primary recommendation or a supporting mention?
This is the layer most B2B marketing teams are missing entirely. Only 22% of marketers currently track AI visibility and traffic, and only 25.7% plan to develop content specifically for AI citations. That gap is, for the moment, the single biggest competitive opportunity in B2B marketing.
You can read the full methodology behind each of these pillars on the [growthvibe AI search optimisation methodology page](https://www.growthvibe.com/ai-search-optimization-services/).
A worked example: what this looks like in martech
Consider a mid-market martech category, say, account-based marketing platforms. Ask ChatGPT, Perplexity and Google's AI Overview the same question: "What are the best account-based marketing platforms for B2B companies with 200 to 2,000 employees?"
Run it once and you'll notice a pattern. Two or three vendors show up consistently across all three engines. A handful appear on one platform but not the others. And a long tail of category players, some of them with significantly better products, bigger marketing budgets and stronger Google rankings, don't appear at all.
When we dig into the brands that win across all three platforms, the same four things are usually true. Their entity is clear (the model knows what they do without hesitation). Their owned content is structured for extraction, with direct answers, comparison pages and FAQ schema. They have strong, recent source signal across G2, Gartner coverage, analyst mentions, and in Perplexity's case, organic Reddit discussion. And they are measuring AI visibility as a discipline rather than guessing at it.
The brands that lose? Usually strong on traditional SEO, weak on entity clarity, inconsistent on answer architecture, and invisible in the places the models trust. Their dashboards look healthy. Their pipeline is quietly leaking.
The mistakes I see most often
After running diagnostics across dozens of B2B brands, the same patterns recur.
Treating AEO as a content problem. Most teams respond to the AI search shift by publishing more blog posts about AI. That is content marketing with the word "AI" in the title. AEO requires work across entity, architecture, source signal and measurement. Content is one of four levers.
Over-indexing on a single platform. Teams obsess over ChatGPT and ignore Perplexity, or vice versa. The retrieval systems are different enough that you need a cross-platform view. The 11% citation overlap figure is the most important number most B2B marketers haven't heard.
Letting the incumbent SEO agency redefine AEO as "SEO plus schema". This is the most expensive mistake in the market right now. Schema helps, but it is a single lever inside the second pillar. If your AEO strategy can be executed by adding FAQ markup, it is not an AEO strategy.
Waiting for measurement to get easier. It will not. The teams winning in 2026 are the ones who built a measurement layer manually, accepted the rough edges, and used the data to make decisions. Waiting for a perfect dashboard is a way of conceding ground to the brands who didn't wait.
A 30-minute AEO self-audit for B2B marketers
If you want to stress-test your own position before the next board meeting, here is a condensed version of the audit we run for clients. It takes about half an hour.
1. The entity test. Open ChatGPT, Perplexity and Google's AI Overview. Ask each one "What does [your brand] do?" and "Who are [your brand]'s main competitors?" Note the answers verbatim. If any of them are wrong, vague or list competitors you don't actually compete with, flag an entity issue.
2. The category test. Run your three most important buyer-intent queries across all three platforms. Example: "What are the best [your category] platforms for [your ICP]?" Note whether your brand appears, and if so, where in the answer.
3. The competitor test. Run the same three queries again, but replace your brand name with each of your top three competitors. Compare who gets cited, how often, and in what position.
4. The source test. For every citation that appears across those queries, note the source. Which domains are being pulled? Is your domain among them? If not, which domains are, and do you have a presence on those surfaces?
5. The freshness test. Check the publication dates on your most important category pages. If your pillar content hasn't been updated in the last 12 to 18 months, you are likely losing ground to competitors whose content is fresher.
If you do nothing else this quarter, run this audit. It will tell you, in thirty minutes, whether you have an AEO problem, and roughly how big it is.
The window is open, and it won't stay open
AEO is in the phase every new marketing discipline goes through before it becomes table stakes. The market data is clear enough that ignoring it is a choice. The measurement tooling is rough but workable. The playbooks are being written in real time by the brands who decided not to wait.
B2B marketing teams that treat AEO as a discipline now, with its own strategy, its own measurement, and its own owned workstream inside the marketing function, will compound an advantage every month their competitors spend arguing about whether it matters. The ones that treat it as a side project, or hand it to an agency still thinking in terms of rankings and links, will spend 2027 trying to catch up on ground they didn't realise they were losing.
The brands being cited by AI today are the brands being chosen tomorrow. Everything else is a dashboard.
Request a free AI Visibility Report
---
*Sources: [G2: Does G2 Get Ranked in AI LLM Search?](https://learn.g2.com/tech-signals-does-g2-get-ranked-in-ai-llm-search); [PR Newswire: 73% of B2B Buyers Use AI Tools in Purchase Research](https://www.prnewswire.com/news-releases/73-of-b2b-buyers-use-ai-tools-in-purchase-research-multi-source-analysis-finds-302733319.html); [The Digital Bloom: 2025 AI Visibility Report](https://thedigitalbloom.com/learn/2025-ai-citation-llm-visibility-report/); [McKinsey: The new front door to the internet](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/new-front-door-to-the-internet-winning-in-the-age-of-ai-search); [Seer Interactive: AIO impact on Google CTR](https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update); [SparkToro: 2024 Zero-Click Search Study](https://sparktoro.com/blog/2024-zero-click-search-study-for-every-1000-us-google-searches-only-374-clicks-go-to-the-open-web-in-the-eu-its-360/); [Adobe: The explosive rise of generative AI referral traffic](https://business.adobe.com/blog/the-explosive-rise-of-generative-ai-referral-traffic); Forrester 2025 B2B Buyer Journey Study.*
Here is the number that should be on every B2B CMO's desk this quarter. In G2's August 2025 buyer survey, 87% of B2B software buyers said AI chatbots like ChatGPT, Perplexity, Gemini and Claude are changing how they research purchases. In the same study, only 12% of B2B SaaS brands appeared when buyers ran category-level searches inside those tools. The other 88% were simply absent from the moment their buyers were forming opinions.
That gap, between where buyers are researching and where brands are visible, is the single biggest unpriced risk in B2B marketing right now. It is also not a distant problem. A multi-source analysis published in March 2026, covering 680 million AI citations and almost two million browsing sessions, found that 73% of B2B buyers are already using AI tools inside their purchase research process. Forrester's 2025 buyer study put a number on the downstream consequence. 61% of the B2B buying journey now completes before a buyer ever contacts a vendor, and that figure climbs every time an AI tool synthesises a shortlist on their behalf.
Meanwhile, McKinsey estimates that around 50% of Google searches already include AI summaries, rising above 75% by 2028, and projects that $750 billion in US revenue will funnel through AI-powered search by 2028. This is a rewiring of how B2B buyers discover, compare and choose, not a marginal channel shift.
The discipline that governs whether you are visible inside those AI-generated answers has a name. It is Answer Engine Optimisation (AEO), and it is a different discipline to SEO.
Why SEO tactics don't translate (and why that matters to your budget)
For most of the last two decades, B2B marketers have optimised for a specific user behaviour. Type a query into Google, scan a list of ten blue links, click one. The entire SEO playbook (keyword targeting, backlinks, domain authority, ranking positions) is a set of levers pulled against that behaviour.
Answer engines don't work that way. When a buyer asks ChatGPT "what are the best account-based marketing platforms for mid-market B2B?", the model doesn't show them ten blue links. It synthesises an answer, names two or three vendors, and sometimes cites a handful of sources. The buyer leaves with a shortlist instead of a search results page.
That one behavioural change breaks several of the core assumptions B2B marketers have been budgeting against.
Rankings don't map to citations. A page that ranks #1 on Google may never be cited by ChatGPT, and a page that doesn't rank in Google's top 20 can be cited repeatedly by Perplexity. The 2025 AI Visibility Report from The Digital Bloom found that only 11% of domains cited by ChatGPT were also cited by Perplexity for the same queries. These are different retrieval systems with different signals.
Backlinks are no longer the dominant signal. In the same study, brand search volume was the strongest predictor of AI citations, with a correlation of 0.334. That is materially stronger than any backlink-based metric. Models are increasingly using brand familiarity as a proxy for trust.
Content freshness suddenly matters in a way SEO never rewarded. Roughly 65% of log hits to cited content were for pages published in the last year, and 79% were from the last two years. AI systems lean toward recent, actively maintained sources.
Traditional measurement is silent on the question that matters. GA4 will not tell you how often ChatGPT recommends you. Your rank tracker will not tell you whether Perplexity is citing your competitor. The measurement stack most B2B marketing teams rely on was built for a world where discovery happened on a SERP.
These differences add up. The inputs, the signals, the measurement and the skills required are distinct enough that treating AEO as "the SEO team's next project" is already producing a second wave of wasted budget across the industry.
The four pillars of AEO for B2B marketers
The methodology breaks down into four pillars. They work as concurrent workstreams rather than sequential steps, and each one pulls a different lever.
1. Entity positioning: make the model understand who you are
Before an answer engine can recommend you, it has to understand what you are. Large language models reason in terms of entities: companies, products, categories, people. If the model's internal representation of your brand is vague, incomplete or confused with a competitor, no amount of content will fix it.
Entity positioning is the work of making sure every AI system has an unambiguous understanding of who you are, what category you compete in, what you do better than alternatives, and who your ideal customer is. The practical work includes Wikidata entries, Knowledge Panel optimisation, structured data (Organization, Product, Software Application schemas), and consistent entity descriptions across high-authority third-party sources.
Diagnostic question for your team: open ChatGPT and ask "What does [your brand] do?" Then ask "Who are [your brand]'s main competitors?" If the answers are vague, wrong, or list your brand alongside companies you don't actually compete with, you have an entity problem. Entity sits upstream of every other AEO lever, so this is usually where the work starts.
2. Answer architecture: structure content so models can extract and cite it
Most B2B content is written for humans scrolling on a laptop. Answer engines don't scroll. They extract. Content that wins citations is content that answers specific questions directly, in a structure the model can parse.
The brands that get cited repeatedly across platforms usually aren't the ones producing the most content. They are the ones whose content is architected for extraction: clear definitions, direct answers at the top of sections, FAQ blocks with schema, concise paragraphs, well-structured headers, and explicit comparisons. Seer Interactive's data on AI Overviews found that brands cited in Google's AI answers earn 35% more organic clicks and 91% more paid clicks. The same Seer analysis showed that when AI Overviews are present and your brand is not cited, organic CTR drops 61% and paid CTR drops 68%. Answer architecture is what determines which side of that line you sit on.
3. Source signal: earn mentions in the places LLMs actually weight
Not all sources are weighted equally. Each AI platform draws from a different constellation of trusted inputs, and the differences are significant. The same 2025 cross-platform analysis found that Reddit accounted for 46.7% of top Perplexity citations but under 10% on ChatGPT after a September 2025 rebalancing. ChatGPT leans heavily on Wikipedia and long-standing editorial sources. Google AI Overviews favour a diversified cross-platform presence.
Source signal is the discipline of earning mentions, reviews and references in the specific places each platform treats as authoritative for your category. For a B2B SaaS brand, that usually means G2 and Gartner Peer Insights (G2's own internal study confirms they are heavily cited across LLMs), plus category-specific industry publications, relevant subreddits, and any analyst coverage that ends up in the training corpus. This is PR, earned media and community work done with a very specific retrieval target in mind.
4. Measurement: track what your rank tracker can't see
If you can't measure AI visibility, you can't manage it, and you certainly can't defend the budget in front of a board. The four metrics we track for clients map directly to the commercial question every B2B CMO cares about: are we showing up when our buyers are choosing?
AI Mention Rate. For a defined set of 20 to 50 buyer-intent queries, what percentage of the time does your brand appear in the answer across ChatGPT, Perplexity, Claude, Gemini and Google AI Overviews?
Citation Authority. When those platforms cite sources, how often is your domain among them, and how does that compare to your top three competitors?
Entity Clarity Score. When you ask the model directly "What does [brand] do?", is the answer accurate, complete and differentiated, or generic and interchangeable?
Answer Ownership. For the queries that matter most to your pipeline, are you the primary recommendation or a supporting mention?
This is the layer most B2B marketing teams are missing entirely. Only 22% of marketers currently track AI visibility and traffic, and only 25.7% plan to develop content specifically for AI citations. That gap is, for the moment, the single biggest competitive opportunity in B2B marketing.
You can read the full methodology behind each of these pillars on the [growthvibe AI search optimisation methodology page](https://www.growthvibe.com/ai-search-optimization-services/).
A worked example: what this looks like in martech
Consider a mid-market martech category, say, account-based marketing platforms. Ask ChatGPT, Perplexity and Google's AI Overview the same question: "What are the best account-based marketing platforms for B2B companies with 200 to 2,000 employees?"
Run it once and you'll notice a pattern. Two or three vendors show up consistently across all three engines. A handful appear on one platform but not the others. And a long tail of category players, some of them with significantly better products, bigger marketing budgets and stronger Google rankings, don't appear at all.
When we dig into the brands that win across all three platforms, the same four things are usually true. Their entity is clear (the model knows what they do without hesitation). Their owned content is structured for extraction, with direct answers, comparison pages and FAQ schema. They have strong, recent source signal across G2, Gartner coverage, analyst mentions, and in Perplexity's case, organic Reddit discussion. And they are measuring AI visibility as a discipline rather than guessing at it.
The brands that lose? Usually strong on traditional SEO, weak on entity clarity, inconsistent on answer architecture, and invisible in the places the models trust. Their dashboards look healthy. Their pipeline is quietly leaking.
The mistakes I see most often
After running diagnostics across dozens of B2B brands, the same patterns recur.
Treating AEO as a content problem. Most teams respond to the AI search shift by publishing more blog posts about AI. That is content marketing with the word "AI" in the title. AEO requires work across entity, architecture, source signal and measurement. Content is one of four levers.
Over-indexing on a single platform. Teams obsess over ChatGPT and ignore Perplexity, or vice versa. The retrieval systems are different enough that you need a cross-platform view. The 11% citation overlap figure is the most important number most B2B marketers haven't heard.
Letting the incumbent SEO agency redefine AEO as "SEO plus schema". This is the most expensive mistake in the market right now. Schema helps, but it is a single lever inside the second pillar. If your AEO strategy can be executed by adding FAQ markup, it is not an AEO strategy.
Waiting for measurement to get easier. It will not. The teams winning in 2026 are the ones who built a measurement layer manually, accepted the rough edges, and used the data to make decisions. Waiting for a perfect dashboard is a way of conceding ground to the brands who didn't wait.
A 30-minute AEO self-audit for B2B marketers
If you want to stress-test your own position before the next board meeting, here is a condensed version of the audit we run for clients. It takes about half an hour.
1. The entity test. Open ChatGPT, Perplexity and Google's AI Overview. Ask each one "What does [your brand] do?" and "Who are [your brand]'s main competitors?" Note the answers verbatim. If any of them are wrong, vague or list competitors you don't actually compete with, flag an entity issue.
2. The category test. Run your three most important buyer-intent queries across all three platforms. Example: "What are the best [your category] platforms for [your ICP]?" Note whether your brand appears, and if so, where in the answer.
3. The competitor test. Run the same three queries again, but replace your brand name with each of your top three competitors. Compare who gets cited, how often, and in what position.
4. The source test. For every citation that appears across those queries, note the source. Which domains are being pulled? Is your domain among them? If not, which domains are, and do you have a presence on those surfaces?
5. The freshness test. Check the publication dates on your most important category pages. If your pillar content hasn't been updated in the last 12 to 18 months, you are likely losing ground to competitors whose content is fresher.
If you do nothing else this quarter, run this audit. It will tell you, in thirty minutes, whether you have an AEO problem, and roughly how big it is.
The window is open, and it won't stay open
AEO is in the phase every new marketing discipline goes through before it becomes table stakes. The market data is clear enough that ignoring it is a choice. The measurement tooling is rough but workable. The playbooks are being written in real time by the brands who decided not to wait.
B2B marketing teams that treat AEO as a discipline now, with its own strategy, its own measurement, and its own owned workstream inside the marketing function, will compound an advantage every month their competitors spend arguing about whether it matters. The ones that treat it as a side project, or hand it to an agency still thinking in terms of rankings and links, will spend 2027 trying to catch up on ground they didn't realise they were losing.
The brands being cited by AI today are the brands being chosen tomorrow. Everything else is a dashboard.
Request a free AI Visibility Report
---
*Sources: [G2: Does G2 Get Ranked in AI LLM Search?](https://learn.g2.com/tech-signals-does-g2-get-ranked-in-ai-llm-search); [PR Newswire: 73% of B2B Buyers Use AI Tools in Purchase Research](https://www.prnewswire.com/news-releases/73-of-b2b-buyers-use-ai-tools-in-purchase-research-multi-source-analysis-finds-302733319.html); [The Digital Bloom: 2025 AI Visibility Report](https://thedigitalbloom.com/learn/2025-ai-citation-llm-visibility-report/); [McKinsey: The new front door to the internet](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/new-front-door-to-the-internet-winning-in-the-age-of-ai-search); [Seer Interactive: AIO impact on Google CTR](https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update); [SparkToro: 2024 Zero-Click Search Study](https://sparktoro.com/blog/2024-zero-click-search-study-for-every-1000-us-google-searches-only-374-clicks-go-to-the-open-web-in-the-eu-its-360/); [Adobe: The explosive rise of generative AI referral traffic](https://business.adobe.com/blog/the-explosive-rise-of-generative-ai-referral-traffic); Forrester 2025 B2B Buyer Journey Study.*
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