We spend so much time as B2B marketers deciding what content to create. Blogs, ebooks, case studies, webinars, comparison pages, thought leadership. But I wanted to understand something from the buyer’s perspective:

What information are they actively looking for before they even speak to a vendor? And whose information do they actually trust?

So I spent some time going through Reddit discussions (with help from Jamila Piplyawala) where people were asking for software recommendations, sharing their experiences, and comparing tools. I also looked at research from TrustRadius and Forrester to see whether those conversations reflected broader buying behavior.

A few interesting patterns emerged. And some of them made me rethink how I approach bottom-of-funnel content.

Let’s get into it 👇

📌 TL;DR

• Buyers often begin with a problem rather than a list of vendors.

• AI and online communities can introduce them to tools they haven’t considered.

• Reviews matter, but buyers look for specific experiences and credible details.

• Peer recommendations remain influential, particularly when someone has used the product.

• Vendor content still has a role, especially when it helps buyers evaluate their specific use case.

• Demos, trials, and practical product evidence can be more influential than marketing collateral.

1. Before buyers evaluate your product, they need to discover it

One pattern that emerged from the Reddit discussions was how buyers described their requirements before asking for product recommendations.

In a project management discussion on Reddit, the original poster described an Excel-based workflow and asked the community for alternatives.

The responses introduced different products, but the useful information went beyond product names. People discussed how tools worked for their needs, what limitations they encountered, and which alternatives might fit.

The original poster later updated the thread, saying the recommendations had broadened their perspective and introduced them to tools they wanted to explore.

Three patterns stood out from this discussion:

• Problem-led discovery: Buyers can begin by describing an operational problem without knowing which products could solve it.

• Experience-led recommendations: Specific workflows, limitations, and firsthand experiences provide context beyond a list of product names.

• Community-driven awareness: A recommendation can introduce a buyer to a vendor they weren’t previously considering.

Now, here’s where the broader B2B research becomes interesting.

According to TrustRadius’s 2026 B2B Buying Research:

• 79% of buyers had heard of the product they eventually purchased before beginning their research.

• 83% shortlisted three or fewer products, with an average shortlist of just 2.7.

• 67% ultimately purchased the product that was their first choice when they created their shortlist.

These numbers suggest that brand familiarity and early consideration are particularly important in B2B software purchases.

And that’s where I see the value of communities and AI-assisted discovery.

TrustRadius also found that 63% of buyers used AI during their buying process. AI tools and community discussions can help buyers identify possible solutions before they reach vendor websites or start comparing specific products.

The implication for B2B content marketers is fairly significant.

If our content strategy focuses entirely on people already searching for our product category or comparing known vendors, we may miss opportunities to reach buyers who are still trying to understand their problem and explore their options.

Methodology note: An illustrative framework developed from my qualitative analysis of software-buying discussions, supported by TrustRadius’s 2026 research. Buyers may revisit stages or use multiple sources simultaneously.

2. Buyers rely on reviews, but they also question how credible those reviews are

One of the clearest findings across the research was the importance of third-party reviews in software evaluation.

According to TrustRadius, 74% of buyers consulted user reviews during their buying process, with most turning to software review platforms.

G2 Digital Markets’ State of Software Buying in 2026 adds another perspective:

• 43% of respondents in G2’s March 2026 B2B buyer survey said software review sites influenced which vendors made their shortlist.

• 39% of respondents in its broader Software Buying Trends survey selected review and comparison sites among their most influential research sources.

• Buyers used review filters such as industry, company size and role to find feedback relevant to their circumstances.

But the Reddit discussions revealed an interesting contradiction.

In this discussion about G2, Capterra and TrustRadius, contributors questioned whether software reviews were genuinely useful, how representative they were and whether positive reviews could always be trusted.

Other contributors defended review platforms and discussed their usefulness for comparing products.

So the pattern isn’t simply that buyers trust third-party reviews.

Buyers actively seek independent opinions, but they still evaluate the credibility of those opinions.

From the discussions I reviewed, three signals appeared particularly useful:

1. Specificity: Does the reviewer describe the actual problem they were solving?

2. Context: Is their company, industry or workflow comparable to mine?

3. Trade-offs: Do they explain limitations and frustrations alongside the benefits?

This helps explain why a detailed review from someone with a similar use case can be more informative than a generic five-star rating.

3. Buyers want information from people who understand their situation

Here’s something that became clearer when I compared the Reddit discussions with published buyer research.

Buyers aren’t necessarily looking for the most authoritative-sounding opinion.

They’re looking for an opinion that helps them understand whether a product will work in their circumstances.

• According to Forrester, 82% of B2B buyers trust coworkers and internal management, making them the most trusted information source in that study.

• TrustRadius found that 53% of buyers spoke with a peer who had used the product. Among those buyers, everyone found those conversations at least somewhat helpful.

• G2’s research also found that 49% of software buyers selected industry experts among their most influential research sources.

And that was the kind of distinction I noticed in the project management discussions on Reddit.

Contributors frequently explained the workflows, organizational requirements, and limitations behind their recommendations.

It also explains why industry-specific communities can be useful during research. Buyers can ask questions that generic product descriptions may not answer.

One limitation worth acknowledging: practitioner newsletters and podcasts didn’t appear prominently in the public discussions I reviewed. That doesn’t establish that buyers ignore them. It simply means I found stronger, more observable evidence of peer conversations, review platforms, and communities in this sample.

4. AI has become an important research source. But visibility doesn’t automatically translate into trust.

The role of AI in software research is becoming difficult to ignore.

G2’s March 2026 survey of 1,076 B2B decision-makers found that 55% identified AI chat as a source influencing which vendors made their shortlist. That was higher than the rates on software review sites (43%) and vendor websites (36%) in the same survey.

But there’s another number I find equally important.

According to TrustRadius, 94% of buyers who used AI fact-checked its output at least some of the time.

This suggests that AI is becoming an important means of discovering and researching software, while buyers continue to seek validation elsewhere.

The two studies measure different things, so I wouldn’t directly compare their percentages as if they came from the same survey.

What they collectively suggest is a distinction between being discovered and being believed.

For marketers, getting mentioned in an AI-generated answer is valuable.

But what happens next?

If someone discovers your software through ChatGPT, they may still want to check independent reviews, compare features, consult colleagues, or understand how the product works in practice.

And if those sources don’t support the original recommendation, visibility alone may not be enough.

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5. Buyers aren’t just evaluating features. They’re evaluating whether the software fits their reality.

This was one of the more interesting patterns in my Reddit research.

In the same discussion about project management software, one contributor described building a customized solution rather than relying entirely on existing software.

The reason wasn’t that the available products lacked features. It was that those features didn’t adequately support the complexity of their particular workflow.

The contributor described a visual effects studio with multiple concurrent projects, changing timelines, and complicated resource requirements.

They had built an initial version of a customized scheduling solution using newer development tools.

Now, one Reddit comment doesn’t establish that buyers are abandoning SaaS to build their own tools.

But it illustrates a frustration worth examining: software can offer an extensive feature set and still fail to solve a buyer’s specific problem. G2’s report helps quantify what buyers look for when evaluating software on review and comparison sites.

The most commonly sought information included:

• 58%: Security details

• 51%: Value for money

• 51%: Clear feature information

• 48%: Ease of use

• 47%: Pricing

Notice how much of this goes beyond simply asking whether a product has a feature.

Buyers want to understand whether the product is secure, practical, affordable, and suitable for their environment. And this has implications for the kind of content we create.

A generic case study about improving efficiency may demonstrate value.

But a case study explaining how a financial services company implemented the software, integrated it with existing systems, and handled its specific compliance requirements gives a prospective buyer much more to evaluate.

The same applies to comparison pages, product documentation and demonstrations.6. There’s still a gap between what vendors promote and what buyers find useful

This was another finding I didn’t want to overlook.

TrustRadius asked vendors which resources they considered most influential.

Sales representatives ranked highest in the vendor responses. But when buyers were asked about influential resources, free trials, product demos, and prior experience came out ahead.

The report found:

• 77% rated free trials or accounts as influential.

• 73% rated product demos as influential.

• 72% rated their own prior experience as influential.

These figures reinforce something I’ve been thinking about. Buyers aren’t avoiding vendor information altogether. They still need to understand the product.

But they seem to place greater value on information and experiences that help them assess it for themselves.

6. Buyers want to experience the product before they commit

There’s another disconnect worth examining: what vendors believe influences purchasing decisions versus what buyers find useful.

TrustRadius’s 2026 research found that vendors considered sales representatives their most influential resource.

But buyers placed substantial value on independent evaluation and hands-on product experiences.

G2’s research makes the importance of product experience even clearer. 50% of buyers cited a trial as a decisive factor in their purchase decision, compared with 35% influenced by a sales presentation.

And product experience can work against vendors too.

According to G2, the three most commonly reported reasons for removing a vendor from the final shortlist were:

• 55%: Security or compliance gaps

• 52%: Unfavorable contract terms

• 50%: Poor trial experiences

This is important because it shows that buyers aren’t only looking for evidence that a product works. They’re also looking for reasons it might not.

A product demonstration that only shows ideal scenarios may not answer the questions buyers actually have.

• What happens when their existing data is imported?

• How does the product handle exceptions?

• What integrations are supported?

• What limitations should they anticipate?

• And what happens after they sign the contract?

The same G2 report found that 61% of buyers experienced at least one unexpected implementation disruption in the previous 18 months.

Among buyers who experienced implementation disruptions, integration issues and data migration problems were common, as were delays.

That makes implementation information relevant before the purchase, even though the problems themselves may emerge afterward.

What does all this mean for B2B SaaS content marketers?

After reviewing the Reddit discussions and comparing them with the published research, I think there are five areas worth reconsidering.

1. Create content for buyers who don’t know your product category yet.

Problem-led articles, educational resources, and participation in relevant communities can help buyers discover possible solutions before they have a vendor shortlist.

2. Make independent customer evidence easier to find.

Detailed reviews and firsthand experiences can help buyers validate claims. The most useful evidence includes context, trade-offs and real-world outcomes.

3. Make case studies much more specific.

Instead of relying only on broad success stories, explain the customer’s industry, requirements, implementation challenges and measurable results.

4. Treat product evaluation content as a priority.

Security documentation, integration information, pricing explanations, comparison pages, demos and trials can answer questions buyers may otherwise have to bring to sales.

5. Think beyond AI visibility.

Being recommended by an AI tool may help a vendor enter the consideration set. But buyers still need credible information to validate that recommendation.

My biggest takeaway

Before doing this research, I was primarily interested in which content formats buyers trusted most. But the more useful finding was how much their information needs change depending on what they’re trying to establish.

• A buyer discovering a product needs different information from someone validating a recommendation.

• Someone comparing two vendors needs different proof from someone assessing implementation risks.

• And someone trying to justify a purchase internally may need an entirely different set of answers.

That makes me question how often we organize B2B content around formats and publishing calendars rather than actual buyer questions.

Because a buyer might not care whether the answer comes from a blog, a review, a case study, or a product demo.

They care whether it gives them enough information to make a better decision.

And perhaps that’s where our content strategy should begin.

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Onwards and upwards,

Sreyashi

Follow SaaS Splash Bulletin on LinkedIn

If you are looking at starting your newsletter on Substack this year, I’m here to help.

I migrated SaaS Splash Bulletin from Kit to Substack this year, and in six months, I reached 520+ followers.

And while 520 might not be a huge number, those six months taught me quite a bit about how Substack actually works. Because moving to Substack isn’t only about choosing a different tool to send your emails. With Substack:

• You can use it as your newsletter platform.

• You can build a publication around it.

• You can turn it into a home for your writing.

And increasingly, you can use the Substack network itself to get your work in front of people who didn’t already know you. So this isn’t going to be a tutorial on where to click to open your Substack account. You can figure that out in five minutes.

Instead, I want to cover the things I wish more “How to start a Substack” guides talked about:

→ Why I think Substack is worth considering
→ How to set it up as more than a newsletter
→ What your homepage, About page, and navigation should actually do
→ How to migrate an existing newsletter without starting over
→ How I would think about your old content
→ How to make your Substack discoverable outside Substack
→ And how to actually grow once you start publishing.

Let’s get into it. 👇

Onwards and upwards,

Sreyashi 🌻

I have been thinking about this question quite a bit lately, partly because I write my own newsletter on Substack, but also because I see more and more individuals and companies building newsletters directly on LinkedIn.

And I don’t think the answer is simply that one platform is better than the other.

It depends on who you are, what audience you already have, and more importantly, what you want the newsletter to eventually do for you.

So I researched and compared the two across discovery, thought leadership, AI visibility, subscriber relationships, and what happens as your newsletter grows.

Let’s get into it 👇

📌 TL;DR

• If you’re an individual creator or subject matter expert trying to build an audience you own, I would lean towards Substack.

• If you’re an established B2B company trying to reach more of the professional audience already engaged with your brand, I think LinkedIn makes a very strong case.

• If AI search visibility is an important goal, LinkedIn currently has much stronger public evidence to support it. One 2026 study found LinkedIn appearing in roughly 11% of the AI responses it analyzed.

• And if you’re an individual with an existing LinkedIn audience? The answer might not be LinkedIn or Substack at all. More on that later.

1. Reach and discovery

Let’s start with probably the biggest question when you’re launching a newsletter: Where will the readers actually come from?

For individuals, Substack has built a pretty serious discovery engine

One reason I think Substack works particularly well for individual creators is that you aren’t completely responsible for bringing every subscriber yourself.

According to Substack, more than half of new subscriptions now come from within the Substack network itself. It also says the platform has 5 million+ paid subscriptions, tens of millions of people reading, watching and listening every week, and more than 1 million posts being discovered by potential subscribers in its app every day.

That’s a substantial distribution system sitting around your newsletter. People can discover you through Notes, recommendations, the app, other publications, and Substack’s own network.

So if I’m an individual starting from scratch, this is pretty attractive.

I’m not only starting an email newsletter. I’m entering an ecosystem where readers are already actively looking for writers and publications to follow.

LinkedIn has a completely different advantage, especially for companies

Imagine you’re a B2B company with 30,000 LinkedIn followers.

You decide to start a newsletter. You don’t necessarily have to start building the audience from zero.

When a LinkedIn Page publishes its first newsletter edition, LinkedIn notifies its followers to subscribe. And there’s another interesting distribution loop.

When someone follows a person or Page that has a newsletter, LinkedIn can automatically invite them to subscribe to the newsletter as well.

So your company Page can keep feeding the newsletter.

Then, with each new edition, there is another distribution advantage: subscribers can receive notifications about it on LinkedIn and, depending on their settings, via email.

This is why I think LinkedIn newsletters make a lot of sense for companies that have already spent years building a following there.

Why send that audience somewhere else before you’ve even established whether they want your newsletter?

So my first distinction would be:

Of course, this changes if you’re an individual with 100,000 LinkedIn followers or a company with 200 followers. Which is why I wouldn’t choose either platform without considering where my existing distribution sits.

2. What kind of relationship do you want with your subscribers?

This is where the difference gets much bigger. On Substack, you’re building an email list. And crucially, you can export it. That matters a lot to me.

Because if I spent two years building thousands of subscribers and eventually decide that Substack isn’t right for me anymore, I haven’t necessarily lost the audience I spent three years building.

I can move.

LinkedIn works differently. You can see your subscribers. You can see new subscribers. You can even follow, connect with, or message them individually.

But LinkedIn’s documentation doesn’t describe an equivalent exportable email list.

Your subscribers remain LinkedIn subscribers.

And this is why, if I were an individual trying to build a long-term audience around my own name, I would be uncomfortable making LinkedIn the only home for my newsletter.

But there’s another side to this. Because LinkedIn knows something about your subscribers that an email newsletter often doesn’t.

Who they are professionally.

LinkedIn newsletter analytics can show subscriber demographics including:

• Job title

• Company

• Company size

• Seniority

• Location

For a B2B company, that’s incredibly useful.

Imagine having 5,000 subscribers and being able to see whether you’re attracting individual contributors at small companies or senior leaders at the enterprise companies you’re actually trying to sell to.

That’s very different from knowing that 5,000 email addresses subscribed.

So this is another trade-off I would think about:

For an individual creator, I value the first one more. For a B2B marketing team, the second one becomes extremely interesting.

3. If you want to build a community, Substack has a structural advantage

This is another reason I would lean toward Substack for individuals.

The newsletter doesn’t have to remain a one-way email forever.

• You can have comments.

• You can use Notes.

• You can create subscriber-only conversations through Chat.

• You can allow subscribers to start discussions themselves.

• You can restrict conversations to paid subscribers or founding members.

• And eventually, if you want to, you can turn part of the publication into a paid membership.

Substack says writers keep 90% of subscription revenue before credit card and payment processing fees. So there is a pretty natural progression available:

And all of that can happen inside the same ecosystem.

LinkedIn obviously gives you comments, reactions, and conversations too.

But I see the relationship differently.

People are on LinkedIn for lots of reasons.

• They’re networking.

• They’re job hunting.

• They’re selling.

• They’re reading industry content.

• They’re checking notifications.

Your newsletter is one part of that experience.

With Substack, someone has made a much more deliberate choice to enter your publication’s ecosystem. That feels particularly valuable when the person is the brand.

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4. Thought leadership is where the answer becomes less obvious

If I’m an individual expert, I like Substack because my publication can gradually become a body of work around me.

My archive lives together. People can discover other things I’ve written.

My subscribers can follow the publication rather than relying on an algorithm to show them my next LinkedIn post.

And over time, I can add community, paid content, products or services around that audience.

If I’m running content for a B2B company, I might actually want my thought leadership sitting right next to the company’s Page, employees, leadership team, customers, and industry conversations.

Someone can discover an article, click through to the company, see who works there, see what those employees are publishing, and continue exploring the brand.

And then I found something that made the LinkedIn argument even more interesting. AI search.

5. LinkedIn is becoming much more than a social distribution channel

This was probably the most surprising part of my research.

Semrush analyzed 325,000 unique prompts across ChatGPT Search, Google AI Mode and Perplexity in January and February 2026. From those responses, it identified 89,000 unique LinkedIn URLs. Across its dataset, LinkedIn appeared in roughly 11% of AI responses, making it the second-most-cited domain in the study.

The percentage was even higher in some individual platforms:

• ChatGPT Search: 14.3%

• Google AI Mode: 13.5%

• Perplexity: 5.3%

Now, this doesn’t mean publishing a LinkedIn newsletter automatically gets you cited by ChatGPT. Far from it. But it does change how I think about LinkedIn content.

Your company’s LinkedIn presence isn’t necessarily only content for people scrolling LinkedIn anymore. Some of that content can become source material for the systems people are increasingly using to research companies, products, and categories.

I would definitely use the Semrush chart showing LinkedIn’s citation rate across AI platforms.

This is probably the strongest visual/data point in the entire newsletter. And there was another finding I found important. The study didn’t only find individual creator posts.

It analyzed several types of LinkedIn URLs, including Company Pages, individual posts, long-form articles, job listings, and other LinkedIn content.

So if you’re a company thinking seriously about visibility in AI search, I don’t think LinkedIn should be treated only as the place where the social media manager distributes your blog posts anymore.

There is growing evidence that LinkedIn itself is becoming part of the information layer AI systems retrieve from. For B2B companies especially, that’s worth paying attention to.

6. What about Substack and AI search?

This is where my initial assumption proved partly wrong. I originally thought the argument would be:

If you’re publishing on Substack and care about AI/search visibility, you need to connect it to your own domain.

I couldn’t find evidence strong enough to make that claim.

In fact, Substack explicitly says it does work behind the scenes to help publications get indexed and ranked in search engines. It also offers SEO features such as sitemaps and Google Search Console verification as publications grow.

So having a normal yourpublication.substack.com A URL does not automatically mean your content is invisible to search engines. Substack’s SEO documentation literally states that it works to ensure publications are indexed and ranked in search engines.

You can also connect a custom domain to Substack.

Use the custom-domain setup page showing the $50 one-time fee and custom-domain option.

Would I personally use a custom domain if a company decided to build a serious publication on Substack? Probably yes.

I’d rather have a long-term publishing property connected to a domain the company controls. But I wouldn’t tell you that doing this will automatically improve your chances of being cited by ChatGPT.

I couldn’t find credible comparative research proving that. And that’s actually an important difference between the two platforms right now:

On LinkedIn, we have large-scale research showing substantial citation across major AI search platforms.

For Substack, we know the content can be indexed and optimized for search, but I couldn’t find a comparable large-scale study that compares how often Substack content appears in AI answers.

Until that data exists, I’d rather not pretend we know.

7. The analytics are also designed for slightly different goals

Substack gives you the kind of analytics I would expect when building a publication.

You can look at:

• Subscribers

• Paid subscribers

• Open rate

• Delivery rate

• Link clicks

• Traffic sources

• Subscriber sources

• Retention

• Sharing

• Notes

• Revenue

You can also connect Google Analytics to get deeper visibility into page views, traffic sources, and conversions. Substack has a very screenshot-friendly metrics guide that shows Posts, Stats, and Growth views.

LinkedIn gives you something much closer to professional audience analytics.

You get:

• Article views

• New subscribers

• Engagements

• Email sends

• Email open rate

• Subscriber demographics

And again, those demographics can be broken down by factors such as seniority, company, and job title.

Use either the LinkedIn Newsletter Analytics page or its Post Analytics documentation showing email sends/open rate.

If I’m a creator, I probably care a lot about:

• Where did my subscribers come from?

• Which posts converted readers?

• Are people staying?

• Could this eventually become a paid product?

If I’m a B2B marketing team, I might care more about:

• Are the right kinds of professionals actually reading this?

That’s a subtle difference. But it’s a meaningful one.

So, LinkedIn or Substack?

After going through all of this, here’s where I’ve landed.

If you’re an individual building your reputation from scratch, I’d lean toward Substack.

You get platform discovery, an exportable subscriber list, community features, and a clearer path towards eventually monetizing the audience.

If you’re an individual who already has a substantial LinkedIn audience → I wouldn’t rush to choose.

LinkedIn might be your best discovery channel.

Substack might still be the better place to build the deeper, more portable relationship.

If you’re an established B2B company → I’d seriously consider LinkedIn.

Especially if you already have a meaningful following on the Page.

• Your existing followers can be invited into the newsletter.

• New followers can continue receiving invitations.

• You get professional subscriber demographics.

• Your newsletter lives inside the same ecosystem as your employees, executives, customers, and prospects.

And now there’s increasingly strong evidence that LinkedIn content is surfacing in AI search results, too.

If your company wants to build a serious owned media property → I wouldn’t make either platform the entire strategy.

This is where I’d think about:

• Your website/domain as the owned content layer.

• LinkedIn as professional distribution.

• And potentially an email/newsletter platform as the subscriber layer.

Because maybe the mistake is assuming one platform has to do everything.

The question I would actually ask

When people ask:

“Should I start my newsletter on LinkedIn or Substack?”

I think we’re starting one step too late.

I’d first ask:

What do I eventually want to own?

• An audience?

• A community?

• Professional reach?

• Search visibility?

• A paid publication?

• A media property around my company?

Because a subscriber count can look identical across two platforms while representing two very different assets. And after researching this, that’s probably the biggest difference I see between LinkedIn and Substack.

Don’t start with where everyone else seems to be writing. Start with what you want the newsletter to become.

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Onwards and upwards,

Sreyashi

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We all have a list of people we follow religiously on LinkedIn. We subscribe to their newsletters, save their posts, and occasionally think, I wish I could create content like this. But what after that?

It’s one thing to get inspired by other creators in your field. It’s another to actually understand why their content works, what they consistently talk about, and how all those individual posts fit into a larger strategy.

So for this issue, I’m using one of my favorite content marketers, Ryan Law, as an example and breaking down how I would analyze a creator’s content strategy from scratch.

Ryan is the Director of Content Marketing at Ahrefs, and I’ve been following his work since his Animalz days. But this isn’t really about Ryan. You can apply the same process to any creator whose work you admire.

Let’s get into it 👇

📌 TL;DR

• Start with at least three months of content, not the creator’s five posts you happen to remember.

• Find their recurring content pillars, then look at the different angles they use within each.

• Separate what they talk about from how they talk about it: direct promotion, indirect promotion, expertise, opinion, proof of work, etc.

• Go beyond posts. Their headline, About section, Featured section, website, and newsletter tell you what they actually want their audience to associate them with.

Step #1: Start by collecting, don’t jump to analysis first

Pick one creator you genuinely admire. Then collect at least the last three months of their content.

You can use AI to make this part easier. Put the posts into a spreadsheet, remove reposts and reshares, and keep the content they actually created.

For my Ryan Law analysis, I created something like this:

You don’t need anything particularly sophisticated at this point. I would simply track:

• Post/topic

• Content pillar

• Angle or format

• Whether there is a direct/indirect company or product connection

• What the person is actually doing with the post

And then I would start looking for patterns. The question to focus on here is: What does this person consistently want to be associated with?

Step #2: Find their content pillars

Once you have enough posts in one place, the recurring themes become much easier to spot. With Ryan, for example, I see three broad areas.

1. Ahrefs and the work happening there

AI search, SEO, research, Ahrefs updates, events, product capabilities and the work his content team is doing.

2. Writing, content and AI

This is also naturally his area of expertise. He talks about writing, content marketing, AI-generated content, content workflows, and how the role of a content marketer is changing.

3. His own work and products

His writing and editing courses are an obvious example. He doesn’t talk about them nearly as frequently as he talks about Ahrefs or content marketing, but they are still part of how he has positioned himself.

And that last part is important. Frequency is also part of the strategy. Your content pillars don’t have to get equal airtime.

Someone might talk about their area of expertise 50% of the time, their company 30% of the time, and their own products only occasionally.

You’re trying to understand that distribution too.

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Step #3: Now go one level deeper. How are they talking about each pillar?

This is probably the most useful part of the exercise.

Knowing that Ryan talks about Ahrefs doesn’t tell me much.

I want to know how he manages to keep talking about Ahrefs without publishing the same kind of post every week.

And when you break it down, there are several approaches.

Sometimes it’s direct. He shares an Ahrefs event, a new piece of research, something his team has published, or something they’re working on.

Sometimes the research itself becomes the content.

For example, he has shared findings from Ahrefs’ AI search studies directly on LinkedIn, turning company research into useful, standalone insights.

Sometimes Ahrefs enters the conversation much more indirectly.

One post I particularly liked begins with what Ryan would do on his first day as Head of Content.

The premise has nothing to do with promoting Ahrefs. It’s a genuinely useful question for his audience.

But as he walks through what he would actually do, things like Ahrefs MCP, Brand Radar, Site Audit, and Agent A naturally become part of the workflow because those are tools he actually uses.

That’s very different from:

“Here are five reasons you should use Ahrefs.”

And this is exactly what I would look for when analyzing another creator.

For every pillar, ask:

What are the different angles they use?

• Direct update?

• Personal experience?

• Opinion?

• Research?

• Use case?

• Behind-the-scenes?

• Something they learned at work?

• A question?

• A story?

Once you start looking at content this way, one “content pillar” can suddenly give you ten different directions.

Step #4: Look for the overlap between their expertise and their work

Ryan has one advantage that I think is worth calling out. He’s a content marketer working at Ahrefs. So there’s a huge overlap between what he personally knows about, what his audience follows him for, and what his company does.

If he’s talking about content marketing, AI search, or SEO, an Ahrefs mention doesn’t necessarily feel like he’s suddenly switched into employee-advocacy mode.

It belongs there. And if you’re working full-time, I think it’s worth doing this exercise for yourself too. Draw two circles:

What do I genuinely know enough about to talk about?

and

What problems does my company/product solve?

Where do those two overlap?

If you’re happy with the company you work for and believe in what you’re building, that overlap can be an incredibly useful content source.

You’re contributing to your company’s visibility, yes. But you’re also publicly building proof that you understand the problems you’re being paid to solve.

That’s useful for your current job and your own career.

Step #5: Don’t just study their feed. Study their profile.

This is something I think we miss when we’re analyzing creators. We scroll through their posts. But their posts are only one part of their strategy.

Go back to the top of their profile.

Look at:

• Their headline: What is the first association they’re trying to create?

• Their About section: What experience and proof do they choose to emphasize?

• Their Featured section: What gets permanent real estate?

• Their current role: How do they describe what they’re doing?

Ryan is an interesting example here.

His current role at Ahrefs is immediately clear from his headline. But when you go to his Featured section, you’ll also find his writing and editing courses. He doesn’t need to publish a course-related post every week for those products to remain visible.

The profile is doing some of that work for him.

Which brings me to a distinction I’ve started finding useful:

Your feed tells people what you’re thinking about right now. Your profile tells them what you want to be known for.

Look at both.

Step #6: Go outside LinkedIn too

If the creator has a personal website, portfolio, Substack, or another publication, don’t stop at LinkedIn. Go there.

• Look at what topics deserve long-form treatment.

• Look at the case studies or projects they showcase.

• Look at the proof they use.

• Look at how they describe themselves when they aren’t constrained by LinkedIn’s profile format.

• Even pay attention to recurring phrases.

Because at this point, you’re no longer analyzing someone’s LinkedIn strategy. You’re starting to understand their positioning. And that’s far more useful.

Step #7: Finally, bring it back to yourself

This is where I think most “inspiration” exercises go wrong. If Ryan talks about Ahrefs frequently, my takeaway shouldn’t automatically be:

“I need to talk about my employer more.”

If another creator has a successful course, my takeaway isn’t:

“I should launch a course.”

And if somebody writes long, opinionated posts that perform incredibly well, that doesn’t mean I suddenly need to start writing 2000-character LinkedIn posts.

Instead, after doing the analysis, I would answer these questions:

Creator I studied:

That last question might actually be the most important one. Because the point of studying your favorite creators isn’t to reverse-engineer your way into becoming them.

It’s about understanding the decisions behind the content you already admire.

We save hundreds of posts because something about them worked on us.

I’ve started finding it much more useful to stop asking:

“How can I write something like this?”

and start asking:

“Why does this person keep creating this kind of content in the first place?”

That’s usually where the actual strategy is hiding.

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I come from a writing background, so naturally, I think about this a lot: what writing jobs will actually survive as AI gets better?

I obviously don’t know for sure. But based on the kind of work I’m seeing companies invest in, the conversations I’m having with people in my network, and where content distribution itself seems to be heading, there are three writing jobs I’d personally bet on for the near future.

Interestingly, all three of them are about knowing how to write somewhere, for someone, in a way that is difficult to manufacture at scale.

Let’s get into it 👇

📌 TL;DR

• Reddit writing is becoming valuable because Reddit has enormous influence on AI search, while the platform itself is increasingly hostile to spam and inauthentic participation.

• Newsletter writing is moving in the opposite direction of commodity content: people subscribe because they want a specific person’s thinking, experience, and point of view.

• LinkedIn ghostwriting is becoming less about writing generic founder posts and more about capturing expertise that already exists inside a company and turning it into original, credible content.

• The common thread across all three: the writing itself is only part of the job. Context, research, platform fluency, and the ability to extract original thinking matter more.

#1 Reddit writing

Reddit has become incredibly important for AI search visibility.

A Semrush study analyzed 248,000 Reddit posts cited across Google AI Mode, Perplexity, and ChatGPT Search. Reddit was among the top three cited domains across the platforms they studied, and more than half of the cited Reddit content came from Q&A threads.

Most of those cited posts had fewer than 20 upvotes and 20 comments. So you don’t necessarily need a viral Reddit thread to become part of the information AI surfaces. You need a useful one.

But there’s another side to this. Reddit is actively trying to protect the thing that makes it valuable in the first place: human conversation. Its spam policy explicitly prohibits repeated or unsolicited mass engagement and mentions generative AI tools when they’re used to facilitate spam. Reddit has also been investing heavily in detecting spam, fake engagement, and low-quality contributors. Individual subreddits have their own rules, moderators, and cultures in addition to the platform-wide rules.

That creates a pretty interesting content problem for companies. They want to participate on Reddit because the conversations there can influence search, AI visibility, and, ultimately, how people discover products.

But they can’t approach Reddit like another distribution channel where you take a blog post, shorten it, add a link and call it a day. And that’s where I think Reddit writers become a genuinely interesting specialization.

I’m already seeing roles looking specifically for people who can write helpful Reddit comments, understand moderator dynamics and subreddit cultures, identify relevant conversations early, and participate without resorting to spammy tactics.

That person isn’t really a traditional content writer. They need to understand how Reddit thinks.

Skills I think a really good Reddit writer will need

• Subreddit fluency. The ability to enter a subreddit and understand fairly quickly what people talk about, what gets downvoted, what moderators remove, what kind of self-promotion is tolerated, and how people actually speak to each other.

• Research before writing. You can’t manufacture a convincing first-hand answer from a three-line content brief. A good Reddit writer needs enough access to customers, product teams, or subject matter experts to understand what they’re talking about.

• Thread spotting. Finding the right conversation is probably as important as writing the comment itself. Which questions signal genuine intent? Which discussions are worth contributing to? Which ones should a brand stay completely away from?

• Writing without marketing language. Reddit has an unusually good radar for corporate language. The writing needs to be candid, useful, and specific, without desperately trying to sound “casual.”

• Judgment. Probably the biggest one. Knowing when to mention your product genuinely helps the conversation, and when to say nothing about it is the better move.

I wouldn’t be surprised if “Reddit writer” or “Reddit content specialist” becomes a much more normal line on content teams and agency rosters over the next couple of years.

#2 Newsletter and Substack writing

If AI can produce a perfectly decent 1,000-word article in minutes, why would somebody pay another human to write a newsletter?

I think the answer is hidden inside the question.

I read newsletters because I want to know what someone noticed this week. What they’re changing their mind about. What happened when they tried something. What they’re hearing from customers that hasn’t made its way into a polished industry report yet.

That distinction becomes more valuable when the internet is overflowing with perfectly acceptable information.

And newsletters are particularly well suited to it because the relationship is direct. Someone chooses to let a specific person’s thinking land in their inbox every week.

We’re already seeing how seriously people are treating that relationship. The Substack resource I was reading while researching this piece talks about newsletter building almost like building a standalone media business: positioning, content pillars, voice, audience development, paid tiers, retention and monetization.

That is very different from “write four articles a month.” And I think there’s a B2B version of this opportunity too.

Founders, operators, and subject-matter experts within companies have years of knowledge. Product companies themselves have interesting things happening behind the scenes: experiments, customer conversations, product decisions, mistakes, market observations.

Most of those people don’t have the time, or sometimes the writing ability, to turn all of that into something people genuinely want to read every week.

That’s where the opportunity is. Not “newsletter writer who can produce 1,200 words.” Someone who can turn access into a publication people recognize.

Skills I think a great newsletter writer will need

• Interviewing. If you’re writing for somebody else, your raw material shouldn’t primarily come from Google. It should come from the person whose name is on the newsletter.

• Point-of-view extraction. A subject matter expert might give you 45 minutes of conversation. Your job is to notice the three sentences that contain something genuinely interesting.

• Voice. Not “make it conversational.” Actually understanding how that person thinks, the words they naturally use, what they would never say, and how certain or uncertain they tend to sound.

• Editorial judgment. Knowing what deserves an entire issue, what is merely a LinkedIn post, what has already been said before, and what could become a recurring theme.

• Audience understanding. Good newsletter writing is partly knowing what your reader will forward to a colleague, reply to, save, or pay for.

• Consistency without sameness. You need a recognizable publication without writing the same issue every Friday with a different headline.

Successful newsletters aren’t being treated as collections of posts anymore. They’re being built around positioning, recognizable content pillars, audience growth, and eventually products or paid offerings. That makes newsletter writing much closer to editorial strategy than freelance article writing.

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#3 LinkedIn personal branding

Ghostwriting is obviously not new. But I think the job is becoming more interesting because LinkedIn itself is becoming more important outside LinkedIn.

One recent study analyzed 89,000 LinkedIn URLs cited by ChatGPT Search, Google AI Mode, and Perplexity. LinkedIn appeared in around 11% of the AI responses they analyzed, making it the second-most-cited domain in their dataset.

More importantly, original educational content was cited far more often than reshares. And individual creators mattered: ChatGPT Search and Google AI Mode cited individual creators more often than company pages in that study.

That changes the equation a little. Your founder’s LinkedIn isn’t necessarily just a social channel anymore.

The things your leadership team publishes can become part of the information layer through which people, and increasingly AI systems, understand your category, your expertise, and potentially your company.

Then there is employee advocacy.

Instead of one corporate account publishing everything, you can have a founder talking about where the market is going, a product leader explaining what they’re building, a sales leader talking about what they’re hearing from buyers, and employees sharing what they’re learning from actually doing the work.

Same company. Completely different perspectives.

But again, most of these people don’t have time to sit down three times a week and think, “What should I post on LinkedIn today?”

So I think there will continue to be an opportunity for writers who can do something much harder than produce LinkedIn posts: think and write convincingly as another human being.

Skills I think the best LinkedIn personal-brand writers will need

• Voice capture. Your CEO, product leader, and salesperson should not mysteriously sound like the same content marketer.

• Interviewing and listening. The best raw material is often buried in an offhand comment during a 30-minute conversation. You need to recognize it when you hear it.

• Original-source research. Sales calls. Customer conversations. Internal Slack discussions. Product decisions. Experiments. Things the person actually experienced. That’s what makes the content difficult to replicate.

• Editorial instincts. Not every interesting thought needs to become “5 lessons I learned about leadership.” Sometimes the unfinished observation is the better post.

• Business context. You need to understand why this person is building an audience in the first place. Hiring? Category creation? Pipeline? Credibility? Partnerships? Their content should eventually compound toward something.

• Writing restraint. Probably underrated. Good ghostwriting isn’t about making someone sound smarter. It’s knowing how much polishing you can do before they stop sounding like themselves.

Writing jobs aren’t disappearing. The definition is changing.

When I look at these three roles together, this is what I think:

• Reddit writers need community intelligence.

• Newsletter writers need access and editorial intelligence.

• Personal-brand writers need human intelligence, the ability to extract somebody else’s thinking and preserve their voice.

AI can absolutely be part of all three workflows. Research, transcripts, idea organization, editing, repurposing- I would use it for all of that.

But the valuable part of these jobs is knowing which conversations matter, which ideas are actually worth saying, how a particular human would say them, and what shouldn’t be published at all.

Maybe that’s the shift writers should be preparing for. Becoming harder to replace before the writing even begins.

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I have been doing a lot of research on original research lately, because we are planning to publish one at my current company.

You already know the typical formats. The State of AI. The State of SEO. The State of whatever your industry cares about this year. So while I was digging around, I started saving the survey-led pieces that did something different, and I built myself a swipe file.

I am sharing three ideas from my swipe file today. I have intentionally left out the usual downloadable reports and gated lead magnets. The three that I have picked here are different in their own way. Be it in the type of data it collects, how the data is presented, or how the report is positioned.

I will break down what makes each one work as we go. Let’s get into it 👇

📌 TL;DR

• Original research doesn’t need a new idea; it needs a different presentation. Same survey, three genuinely different plays.

• Ahrefs shows the positioning play: lead with a raging problem your audience already fears, then let your proprietary data settle the argument. Works only if the data is truly yours.

• AirOps shows the distribution play: go deep enough that the vocabulary filters your audience, then ungate the whole thing. For a niche expert reader, removing friction beats collecting emails.

• Cognism shows the data-type play: research built on your own product usage data is the most defensible kind, because nobody can replicate your users. Bonus: the findings can prove the problem you solve without pitching.

• Across all three, the data you have determines how you can position the report. Pick the play your data actually earns, not the one you wish it did.

#1 Pick a raging problem and back it with your proprietary data

If you follow the SEO tool world, you already know that Ahrefs, Semrush, and their peers publish a steady stream of AI search reports and write-ups. Worth watching, even when the results are not the point. As a content strategist, this is the category to study because these companies sit on enormous amounts of data and use it about as well as anyone else.

The report style they lean on, and the one I keep coming back to, works like this. They pick a challenge their potential users are actually facing, then use their own data to establish it, back it up, or break it down.

This one above is a good example, written by Ryan Law; the problem it opens with is that marketers are now scared to use AI in any capacity, convinced Google will punish them for it. That fear is the hook. Then they answer it with data, and the answer is that Google is not punishing AI content; it is punishing bad content.

Notice the data type too. This whole study runs on pages. For a tool like Ahrefs, pulling pages is easy, whether for a specific industry or across no particular domain at all. Read their methodology, and you will see how much that access shapes what they can publish.

One honest caveat. This positioning does not fit every brand. If you do not sit on a proprietary data set, you cannot credibly break down a problem this way. But for martech and data-rich SaaS, it is one of the strongest ways to frame a report.

What I learned from it:

• Lead with a problem your audience already feels, not a topic. “Are marketers right to fear AI content?” beats “The State of AI Content.”

• Turn that problem into the hook, then let your data settle the argument for you.

• Use data you actually own. The positioning only holds up if the numbers are yours to analyze.

• Read the methodology before you borrow the format. It is only repeatable if you have similar access.

#2 Make it a detailed report, but ungated

Next on my list is AirOps. Same category as before. Brands like AirOps and Profound also publish a steady stream of AI search research. But this one stood out to me for two reasons.

The first is depth. Put this next to the Ahrefs piece, and the difference is obvious. This is research research. The Ahrefs study answers one clean question with a few clear data points. This one maps an entire pipeline: what happens in the gap between a user’s query and an AI citation. That is a far narrower, far more technical problem, and they treat it that way. They tested retrieval rank, heading match, content length, schema, freshness, authority, one after another, with controlled comparisons for each.

The second is who it is for. This is not written for any writer or any content marketer. It is written for someone who already understands how fan-out works, how citations get chosen, how AI engines actually retrieve. The vocabulary assumes it. The structure assumes it. They made no attempt to widen the audience, and that is the point.

The data collection itself is not that different from Ahrefs in principle. AirOps has a user base, so they can track prompts, queries, and pages at scale and build trends from them. The raw capability is similar. What is different is how far they chose to take it. Same well, much deeper bucket.

Worth noting too: this is not a one-and-done report. If you look at their blog, they slice this same study into problem-specific posts, each one framed around a single finding. That is the same move Ahrefs makes, and it is a good reminder that one serious research project can feed a quarter of content if you break it apart by problem.

But here is why I included this AirOps study: they kept a report this detailed completely ungated. No form, no download, no email wall. For a study this heavy, that is a deliberate bet, and I think it works for two connected reasons.

One, the report is niche, and the small group of people who can fully appreciate it is exactly the audience AirOps wants in the room.

Two, that audience has zero patience for friction. Someone who understands AI engines that well is not going to trade their email to read your gated PDF.

So AirOps removes the gate and reaches them directly, betting on trust and reach with the right people. That is the real lesson here. Gating is not a default setting. It is a positioning choice, and sometimes the more strategic move is to give the whole thing away.

What I learned from it:

• Match the depth to the audience. A niche, technical reader wants controlled findings and real rigor, not a skim-friendly overview.

• Pick a problem specific enough that the vocabulary alone filters your audience. If everyone understands the title, it may be too broad to signal expertise.

• One serious study is a content engine. Break it into problem-specific posts, each built around a single finding, the way both AirOps and Ahrefs do.

• Treat gating as a decision, not a habit. Ask who you are trying to reach before you ask for their email.

• For a high-expertise audience, ungating can be the stronger play. Remove friction when the people you want most are the least willing to tolerate it.

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#3 Product usage patterns that present a trend

Last one, Cognism. Ahrefs and AirOps both sit on a lot of data, but it is data about the outside world. Ahrefs looks at pages and SERPs. AirOps ran queries through an AI engine and watched the results come back. In both cases, the data is something they went out and observed.

Cognism’s report runs on a different type of data entirely. This is product usage data. Their platform is a sales intelligence and data-as-a-service tool, so every day it captures live signals as customers use it: leadership changing roles, tech stacks shifting, records going stale, intent tightening around certain topics. The report is not built on data they collected for the study. It is built on the exhaust of the product simply doing its job. That difference changes what they can credibly say, and it is worth breaking down properly.

First, this is the strongest possible version of “only we could have published this.” Ahrefs’ page data has close substitutes; Semrush sees a similar web. AirOps’ query approach could be replicated by Profound with enough effort. But the signal data flowing through Cognism’s platform is genuinely non-replicable, because it is a direct product of their specific user base doing specific things. Nobody can reverse-engineer your product’s usage. When your data is your product’s behavior, the moat around the research is real, not stylistic.

Second, the findings double as proof of the problem the product solves. Look at the data decay section. Cognism uses its own data to show that around 30% of C-suite records become inaccurate within a year, and that revenue leadership records (CRO, CMO) decay fastest of all. That finding is interesting on its own, but it is also a precise description of the exact pain Cognism’s “always-on” data exists to fix. The research does not sit next to the pitch. It is the pitch, established as a market fact before anyone mentions the product. And notice they were willing to admit their own data decays, which most vendors would bury. Naming the problem honestly is what makes the eventual solution believable.

Third, usage data lets you claim authority over the market itself, not just your niche. AirOps can speak with authority about AI citations. Cognism, because its signals span companies, leadership, hiring, and buying behavior, can make claims about where the whole business economy is heading in 2026. That is a category-leadership move. You stop being a vendor with a good tool and become the source people quote when they describe the state of the market. Very few data types let you legitimately reach that high.

Now, the trade-off: this format has a real failure mode. Usage-data reports slide into product ads faster than any other kind. The line between “here is a genuine market shift” and “here is why you should buy us” is thin, and readers can smell it the moment you cross it. Cognism mostly stays on the right side because each finding is framed first as a market pattern and second as a product implication. But you can feel it tip toward the end, with the demo CTA and the People Moves widget. That is the tax on this format. If you write research based on your own usage data, the discipline is to let the trend stand on its own and let the product implication follow as a consequence, never as the point.

What I took from it:

• Product usage data is the most defensible data you can build research on, because it cannot be replicated. If your product generates signal as a byproduct, that exhaust is a research asset most teams ignore.

• The best usage data research proves the problem your product solves without pitching it. Establish the pain as a market fact first, then let the reader connect it to you.

• Be willing to name a problem that implicates your own data. Cognism admitting that its records are decaying is what makes the fix credible. Transparency reads as confidence.

• Usage data can earn you authority over the whole market, not just your corner of it. If your signals are broad enough, you can credibly narrate the state of an industry, which is a stronger position than owning one topic.

• Watch the tipping point. This format degrades into an ad the fastest. Keep every finding standing on its own merit and let the product implication trail it, never lead.

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Hey friend,

Suppose a content team does everything right. Clean pages, real research, keywords with actual intent behind them. And the traffic keeps sliding.

So they publish more, refresh what’s decaying, and it holds for a quarter before sliding again. Nothing was done badly.

The loop content strategy ran on for a decade, find the question, write the best answer, rank, get the traffic, is coming apart, because AI Overviews now answer the question before anyone clicks.

Which means content strategy has to answer two things instead of one: what deserves to exist, and what still creates value if nobody clicks. This is the workflow I use for both.

Let’s get into it.

The question worth keeping

Here’s the filter I run everything through now:

Will this still create value if Google sends zero clicks?

It’s uncomfortable, because a lot of what we call good content only ever worked because search rewarded it. Take the clicks away, and there’s nothing underneath.

The things that survive the question tend to be the same things: research nobody else has, a genuine point of view, content that does a job at the moment someone is deciding, and formats that live somewhere you own.

Content calendars often don’t survive that question. That’s the useful part. Run yours through it and see what’s left.

That’s the whole shift, and it’s narrower than “AI killed content.” AI search is unkind to content that could have been written by anyone. It rewards content that could only have come from you, formatted so a machine can lift it.

Onwards and upwards,

Sreyashi 🌻

I’ve looked over 20 1:1 calls in the last few months. Most of them are built the same way: a long list of article links, sometimes without any proof attached to them. And that’s it: no context, no explanation, no impact that you could rely on.

Here are three things that make a portfolio stand out in the AI era, and three you can delete/ignore.

Let’s go👇

📌 TL;DR

• A one-minute Loom you recorded yourself beats a two-page cover letter.

• One vibe-coded tool someone can actually open beats twenty article links.

• Proof has to show AI visibility now, not a blue-link screenshot.

• Certificates and your full education history are mostly filler. Cut them.

• Every short freelance stint doesn’t need its own entry. Collapse them into one.

#1 Record a Loom for every role

For every role you apply to, record a short Loom video that does what a cover letter used to do, but keep it under a minute. I know that sounds too short. It isn’t, once you’ve actually decided what to say.

Keep the script simple: what you’ve done, why this role specifically, what you’d bring, what you want to learn, why you’d fit the team.

The part that makes it work is showing you actually researched the company. A founder’s post you read, a new product they launched recently, or any real detail that shows your curiosity. That’s the difference between saying you researched the company and proving it.

Also, don’t script this with AI. Because the entire value here is that it sounds like you, a real person who spent a few minutes talking bout themselves. That’s the one thing an AI-written cover letter can’t fake, so don’t hand it away.

💡Pro Tip: Don’t send the same portfolio to everyone. Make a version per role. Notion is the fastest way to publish, as a link or a page. The Loom should sit on top of whichever version you send.

#2 Build one small tool and let people use it

“Good with AI” on a resume means nothing now.

The way you actually prove it is by building something small and letting people open it. For example: a competitor-analysis agent, a content-refresh agent, a topic finder, a brief creator. It doesn’t have to be something exceptional. It has to be real that’s your brainchild.

Also, don’t just link it. Walk through it: what problem you were solving, how you built it, what it saves you in a normal week. That last part matters most, because it shows you build tools to get actual work done.

And this is a real hiring signal now. “Vibe marketer” has gone from a joke to an actual job title in about a year. If you can show you build your own tools, you’re answering a question a lot of teams have started asking out loud.

#3 Show proof that fits how search works now

This is the one where many content marketers accidentally hurt themselves.

They include a #1-ranking screenshot or an AI Overview grab as proof, thinking it’s the strongest part of the portfolio. The problem is what it signals. To anyone paying attention, a blue-link ranking screenshot reads as someone who hasn’t updated how they think about search. You’re trying to show strength and showing the opposite.

Here’s what I’d show instead, in the order I’d reach for it.

If you have access to an AI visibility tool (Ahrefs Brand Radar, Otterly, Semrush, Profound), take a screenshot showing where your content is actually cited or mentioned in AI answers. That’s the version of “ranking” that means something now.

No tool? Take a free trial and capture one clean pass. One good screenshot is enough.

Neither? Open an incognito window, run the real bottom-of-funnel prompts a buyer would type, and show how your content surfaces in the answer itself.

And if you can show a before-and-after, do that above all else. AI visibility before your work, AI visibility after. That comparison says more than any single number.

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What I’d take off from my portfolio right away…

If I were you:

• Certificates and courses. They’re not what gets you hired. If you keep one, keep a current one (e.g., AirOps certification), not the same old HubSpot content marketing course from years ago.

• Your full education history. Latest degree is enough IMO

• A separate job entry for every short freelance stint. Roll them into one: “Freelance content marketer, [year] to present,” with the companies, a linked portfolio, and the kind of content underneath.

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In a small marketing team, you’re usually the go-to person for SEO, content, socials, and the list goes on. And what helped me balance my time better was setting up Claude in a way that saves MY time and helps ME manage things more effectively.

I NEVER gave in to all the noise around Claude, and took my time to figure out how I can do my best without selling my soul to AI. And finally, I have something worth sharing that might help you.

Let’s go👇

📌 TL;DR

• Skills are the repeatable-process layer: I’ve built four (voice, decks, BOFU research, sales LinkedIn).

• Agents are the monitoring layer: three of them run on their own: a competitor roundup, an industry news pull, and a daily Reddit read.

• Projects are the accumulated context layer: I don’t make new ones anymore, but the old ones are gold because of everything I've fed them.

• Connectors are the real-data layer: Ahrefs does the heavy lifting, plus Google Drive, Notion, Whimsical, and Wispr Flow.

• The privacy line I don’t cross, and the setting you should check before you paste anything.

Skills: the layer that stops me repeating myself

A skill is just a saved set of instructions that Claude can reach for on its own when the task fits. I don’t paste my rules in every time. The process lives in one place, and it shows up when it’s needed.

I’ve built four so far, and each one maps to a job I was tired of doing from scratch.

The voice and tone skill aligns any piece of content to our brand voice, not only the stuff I write. That matters on a small team, because content comes from more than one person and it all has to sound like us.

The deck skill spins up quick leadership decks that already follow our voice and brand guidelines. Which means my time goes into the research and the story, not into fixing fonts and fighting layouts.

The BOFU and listicle skills do the part I actually dread: the grind across G2, Gartner, and competitor sites. They come back with a detailed outline, so by the time I sit down to write, the hard thinking is mostly done.

And the sales LinkedIn skill lets sales pull post captions from the latest blog themselves, without waiting on me.

Agents: the layer that works while I don’t

Skills wait for me to ask. Agents don’t. That’s the difference, and it’s a big one.

I run three, and they’ve changed what my Mondays feel like.

The first is a competitor roundup. Every Friday, it summarizes what our competitors published that week: articles, reports, ebooks, and gated resources. I open one summary instead of fifteen tabs.

The second pulls industry news. Friday mornings, it collects what’s happening across the US and European markets from a fixed set of reliable sources I actually trust.

The third is a Reddit read that runs every single day. At the end of the day, it tells me what people are saying about our topics, our competitors, the other tools in the space, and the integration questions that keep coming up.

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Projects: the layer holding everything I ever told it

Projects are where I started, back before I understood agents at all. I don’t create many new ones now.

But the old ones? I still go back to them, because of everything I’ve fed them over months. That context doesn’t exist anywhere else.

I use them to edit something quickly, or to have Claude interview me with real questions when I’m stuck on what I actually think. FAQs and TL; DRs come out of them within seconds.

Where they still earn their place is brainstorming. When there’s a creative event or a messy idea I’m circling, I load the context and use it as a thinking partner, one that pushes a new angle instead of agreeing with me.

Connectors: the layer touching real data

This is where Claude stops working off what I typed and starts working off what’s true.

Ahrefs is my most-used by a distance. It’s how I look at organic health, spot which pages are declining and why, and build leadership-ready reports without exporting CSVs by hand. It also answers the small stuff fast, like how a specific blog performed in a specific window.

Google Drive handles the flow of work in and out. I pull things in, drop drafts back out, and sometimes create publish-ready drafts with the custom embed code already in place.

Notion and Whimsical are for thinking on the page, brainstorming and noting things down as they come.

And Canva. Honestly, it is hit-or-miss for me. With a tight brief, it’ll produce a quick static, but I won’t oversell it. Someone with real design instincts would get far more out of it than I do.

Quick note on the privacy aspects

• Before I paste anything, I check the data and training settings in the tool I’m using. Not once, and then forget, actually check, because defaults change and it’s your name on the risk.

• I never feed in customer PII, unreleased numbers, or anything under an NDA. No exceptions, no “just this once.” If I’m not sure whether something counts, that’s my answer: it counts, so it stays out.

• I keep a human review step on anything customer-facing or factual. The draft can come from Claude. The sign-off comes from me.

• And I treat connectors like access. They touch live data. That’s the point of them, and it’s also the reason to be deliberate about what you connect and why.

The tools are only ever as safe as the habits around them. I’d rather you leave this issue with the habits than the stack.

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I have been noticing a pattern in my 1:1 calls: some of the questions, though in different formats, keep repeating again and again. So I thought of answering them on my newsletter once and for all.

Let’s get into it 👇

📌 TL;DR

• Confused about GEO? Stop reading about it and start watching one tool report on prompts you picked yourself

• Not getting cited? It’s rarely your formatting. It’s that nobody else on the internet mentions you

• Not seen as strategic? You’re being qualitative in a room that runs on numbers

• Losing ground to SEO folks? It’s not a turf problem, it’s an evidence problem

• Not sure where AI fits? Hand over the workflow. Keep the point of view

Q1. “I have no clarity on GEO, and I can’t tell what’s noise”

First question back at you: what’s your lens?

Because a writer asking “what kind of content actually gets pulled into AI answers” and a GEO owner asking “how do we move share of voice this quarter” need completely different reading lists. A lot of the confusion I hear is just people reading the wrong material for their job and feeling confused about it.

Read, definitely. But treat all of it as directional. Even the credible sources are publishing a new study every week right now, and one study a week does not mean one truth a week. Pick two or three sources that match your lens and ignore the rest without guilt.

Here’s the honest thing though. I didn’t understand GEO from reading. I understood it when I started using Otterly and watching it report on prompts I’d chosen myself.

Three things I learned there that no article told me cleanly:

It’s usually not your writing

Writers keep getting told their structure and formatting are why AI isn’t citing them. Sometimes true, rarely the main reason. You have to be mentioned by other people. Your page can be perfect and still invisible if the rest of the internet has never heard of you.

Your prompt choice decides what you see

Track only bottom-of-funnel prompts, and a low citation count will feel like failure when it’s completely normal. Track only top-of-funnel, and you’ll see plenty of citations that convert nothing. Read your numbers against the funnel stage you chose, or you’ll panic over the wrong thing.

Citations aren’t the last metric

The tools show you sentiment and intent too. Someone arriving from an AI answer ready to buy and someone arriving to compare five options are not the same visit, and treating them as one number hides the whole story.

Can’t get a tool approved? Do it manually.

Fixed list of prompts, fixed day of the week, one sheet, log what you see. It’s slower, and it’s more effort. It teaches you the same thing.

GEO stops feeling like noise the moment you’re looking at your own data instead of everyone else’s conclusions.

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Q2. “I understand content strategy, but I can’t show up as a strategist in the room”

If this is you, I’d bet the problem isn’t your strategy. It’s that you’re being qualitative in a room that runs on quantitative.

I’m not being clever here; this is my own gap. I have the instinct for what’s good, what will hold up in twelve months, which piece is doing the work. And for a long time that’s all I had when someone senior asked me why.

Gut feel is usually right. Gut feel also loses to a slide with a number on it. Every time.

So start where it’s easy. Google Search Console, GA, Ahrefs/Semrush. These are not hard tools, and I say that as someone who avoided them for longer than I’d like to admit.

Get comfortable, then go one layer out: your CRM, then attribution and customer journeys, then something like Factors.ai or RB2B.

The shift that actually changes how you’re seen isn’t using more tools. It’s making the data the base of your strategy instead of the defence you scramble for after someone challenges you.

One more thing here, because it comes up on the same calls. Think horizontally. My instinct is organic, organic, organic (yours might be too), and that instinct isn’t always right.

LinkedIn works differently. Events work differently. Newsletters work differently. Search is full of AI-generated content now, and the smart people already know it, so original thinking shared in places people actually trust is what holds up.

Not pausing organic. Just not treating it as the entire plan.

Q3. “SEO folks keep sidelining me, and I’m losing creative ownership”

Same root cause as the last one, different symptom.

Before anything else: I don’t encourage friction here. SEO and content live together and die together. If you’re building a case against the SEO team, you’ve already lost the interesting part of your job.

What’s actually happening is that they’re fluent in a language you haven’t learned yet. They can say “this page lost 40% of its clicks in six weeks and here’s the query set” and you’re saying “this angle is better.” Both can be true. Only one of them survives the meeting.

So learn how they build an argument, and learn it from them directly. Ask what they’re looking at. Ask why they weighted one thing over another.

Then run your own experiments, small ones you fully own. Publish the piece you believe in, write down what you expect to happen, check it at 30, 60, 90 days. Do that four or five times and you stop being the person with opinions and start being the person with a track record. Creative ownership follows evidence. Nobody hands it over because you feel strongly.

Q4. “How do I actually move from writer to content marketer?”

This one’s slightly funny to answer because it’s basically everything I’ve written for the last year. So the quick answer is: go read the archive, it’s all there.

The real answer is shorter than people want it to be. Think like a marketer at every step, not just at the drafting step.

That means the shift happens in research, not in the writing. Before you ask how long the piece should be, ask what it’s supposed to do. Who’s meant to read it, at what stage, and what happens next if it works.

If you’re freelancing, this is the highest-leverage change you can make this month. Stop waiting for briefs. Pitch topics. And when you pitch, explain why that topic, for that funnel stage, for that business right now. Companies are not short of people who can write. They are very short of people who can tell them what to write and why.

Q5. “How do I use AI tools as a content marketer?”

Two ways people get this wrong, and I see both.

One is resisting AI entirely, which is a position you can hold for maybe another year before it costs you. The other is handing over the exact part of the work that made you worth hiring.

What I’d actually do: learn one tool properly instead of sampling nine. For me that’s Claude. Learn how skills work, how Cowork works, how to point it at a workflow rather than at a sentence. That’s the difference between using prompts like “write me an intro” and “pull every competitor’s pricing page, tell me where our positioning is weak, and draft the outline that fixes it.”

Research, competitor pulls, reporting, repurposing, first-pass structure. All of that can be handed over. The point of view can’t. That’s the whole asset.

And this is where question five loops right back to question one. Original thinking is what gets other people to mention you. Mentions are what get you cited. So the thing that makes you hard to replace is also the thing that makes you visible.

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I rated seven new and old content formats out of 10. Purely through the lens of AI search and how it’s shifting.

Let’s get into it 👇

📌 TL;DR

• Informational TOFU scored a 3. Ahrefs found 99.9% of AI Overview triggers are informational intent, so there’s nowhere to hide.

• MOFU listicles scored 6.5, and I had the reason the wrong way around. It isn’t domain authority holding you back. 35% of the “best” lists ChatGPT cites sit on low-authority domains. It’s freshness.

• BOFU scored 7.5, and the score has an expiry date. Its protection is a Google product decision, not a content moat.

• LinkedIn is the surprise of this list. Founder posts and newsletters both scored 8 because ChatGPT and AI Mode cite individual creators 59% of the time rather than company pages.

• Thought leadership scored 6, which is an average, hiding two different answers. With proprietary data, it’s an 8. Without it, a 4.

• Original research scored 9, the only 9 here. Primary research pages are 3.3x more citation-dense than everything else.

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1. Informational TOFU articles: 3/10

You know the article. “What is sales outreach?” “How does X work?” The kind you built your whole content engine on because it brought in volume, and it fed the funnel, and everyone agreed that was the job.

Ahrefs analyzed 146 million SERPs and found that 99.9% of keywords that trigger an AI Overview have informational intent. And when that AI Overview appears, the page sitting at position 1 loses 34.5% of its click-through rate.

So the traffic argument for TOFU is gone. That’s the 3. But here’s what stops it from being a 0, and this is the part I’d have missed if I hadn’t gone digging.

Kevin Indig looked at nearly 4,000 domains across four AI engines and found something uncomfortable: 74.9% of domains get cited, but only 38.3% get mentioned by name. He calls them ghost citations. Your page is in the answer. Your brand isn’t.

Which means your TOFU library might be feeding AI answers right now while your analytics show you nothing at all.

If you kill your TOFU today because the clicks died, you’ll disappear from AI answers in six months and never connect the two. It’s a 3 for traffic. It’s maybe a 6 for being in the room.

2. MOFU articles and listicles: 5.5/10

I had this at 5.5, and my reasoning was completely backward. Let me show you the thing I got wrong, because you might be carrying the same assumption.

My assumption: listicles are middling because G2 and Capterra have domain authority I’ll never touch, so they take the citations, and I don’t.

Glen Allsopp ran a study for Ahrefs on 750 top-of-funnel prompts across software, products, and agencies, manually categorizing 26,283 source URLs. “Best X” blog lists were the most prominent page type at 43.8%.

Here’s the part that killed my assumption. In that study, G2 and Clutch-style pages are counted separately as “non-blog lists.” They’re not the ones taking the 43.8%. Ordinary blog lists are.

And then this: 35% of the cited best lists were on low-authority domains. Some of them, in Glen’s own words, questionable.

So the gate isn’t a domain authority. I was wrong about that, and if you’ve been telling yourself you can’t compete with G2, you might be wrong about it too.

It is freshness. Of 1,100 cited lists he checked, 79.1% had been updated in 2025, and 26% had been touched in the previous two months alone.

So why 6.5 and not higher?

Because being on other people’s lists still beats writing your own, and the same study found a clear trend towards brands positioned near the top of a list getting recommended. That’s an outreach problem, not a writing problem. Your content team can’t fix it.

3. BOFU articles: 7.5/10

I was ready to give this an 8, and I still nearly did.

The case for BOFU is real.

• Ahrefs found that shopping is the lowest AI Overview category at 3.2%, and navigational queries trigger just 0.09% of the time.

• Seer puts transactional at 5%.

• And the intent quality holds up: Search Engine Land’s 13-month analysis across their client base found that LLM referrals convert at roughly 18%, higher than paid search, SEO, and PPC.

So BOFU is protected, and the people who read it are the people who buy. Good.

But I had the reason wrong, and I think a lot of us do.

I’d been telling myself BOFU is safe because it’s hard to summarise. Product-focused, specific, too much nuance for an AI Overview to flatten. That’s a nice story, and it isn’t true. An AI Overview could summarise your BOFU page tomorrow. It doesn’t, because Google has decided which intents get an AI Overview.

That’s not a content moat. That’s a policy moat. And policy moves.

It’s already moving. Semrush tracked 600,000 keywords between November 2025 and April 2026 and found the share of commercial SERPs carrying an AI Overview grew 71%. Their longer study of 10 million keywords shows that transactional AI Overview triggers climb from 1.98% to 13.94% over 12 months.

7.5 today. Ask me in March.

4. Founder LinkedIn posts: 8/10

I had this one at 7.5 in my notes. The data pushed it up.

Profound analyzed 1.4 million citations between November 2025 and February 2026. LinkedIn moved from around #11 to around #5 in ChatGPT’s domain rank, more than doubling its citation frequency. It was the biggest domain authority shift they tracked all year. For professional queries specifically, LinkedIn is now the most-cited domain across ChatGPT, Gemini, AI Overviews, AI Mode, Copilot, and Perplexity.

But the number that matters for founders is this one: ChatGPT Search and Google AI Mode cite individual creators 59% of the time over company pages.

Your founder’s post outranks your company page. In the answer engine. On your own category question.

There’s even a length spec buried in there. Feed posts perform best between 50 and 299 words, which is roughly the post you’d write anyway if you weren’t trying to game anything.

Why not a 9?

Because it’s rented land, and I don’t want to pretend otherwise. Profound’s analysis leans partly on a synthetic prompt basket, so read it as direction rather than gospel. And a platform that reprices this fast in your favor can reprice just as fast the other way.

5. LinkedIn newsletters: 8/10

I wanted to give this a 10. I’ve been telling people it’s a 10.

Here’s why it isn’t.

The supporting data is genuinely strong. Profound found that posts, long-form articles, and newsletters together rose from roughly 27% to about 35% of LinkedIn’s citations inside ChatGPT, while profile pages collapsed from nearly 34% to under 15%. Answer engines are moving away from static identity and toward published expertise. Articles in the 500 to 2,000 word range draw the most citations.

Your podcast episode, your webinar, your customer interview, your case study, nobody reads. All of it has a second life as something an AI engine can cite weeks later.

But newsletters specifically are still a rising tier-3 source. They’re not top-five on any platform yet. The citation lift is coming from LinkedIn, the domain, not from newsletters, the format. Giving it a 10 would mean claiming credit for the platform’s win.

And one thing I’ve said before that I need to walk back: I’ve told people that company pages don’t get visibility. In AI search, that’s inverted on at least one engine. Perplexity cites company pages 59% of the time. Founder-first is right for ChatGPT and AI Mode. It isn’t a universal law.

6. Thought-leadership articles: 6/10

This score is an average, and the average is hiding the whole point.

I’d been saying that thought leadership is protected because it’s hard to AI-overview. Except that most thought leadership is informational in intent, and informational intent is the single most AI-overlooked category on the internet. So that protection was never real.

What actually protects it is whether there’s something inside that a model can’t reconstruct from ten other pages. Ahrefs, studying 1.4 million ChatGPT prompts, found that pages whose titles and URLs matched ChatGPT’s narrower sub-questions got cited more than pages that only matched the broad prompt.

Read that again if you write thought leadership. You don’t lose on quality. You lose on framing. A piece that never declares which question it answers is invisible to a model that’s busy breaking a prompt into six smaller ones.

So the real scores are: Thought leadership carrying proprietary data or your own specific numbers: about an 8. Thought leadership that’s a well-argued opinion with nothing in it only you could know: about a 4.

Averages out to 6. But you’re not writing the average. You’re writing one or the other.

7. Original research reports: 9/10

The only 9 on this list, and the data isn’t close.

Growth Memo found that primary research pages averaged 11.3 citations each. Everything else averaged 3.4. That’s 3.3x the citation density. And the scarcity is the whole story: only 8 pages out of 301 qualified as genuine primary research, and those 8 pages took 8.4% of all citations in the set.

Rare, then over-indexed. That’s the asset.

Original data gets you eligible. Structure gets you cited. Those are two different jobs, and most research reports only do the first one.

So what do you actually do with this

If I’m reading my own scores back honestly, the pattern isn’t “AI search killed content.” It’s narrower than that.

AI search punishes content that could have been written by anyone. It rewards content that could only have been written by you, and only if you formatted it so a machine can lift it.

That’s it. That’s the whole shift.

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I’ve been quieter on LinkedIn the last few weeks. Not because I ran out of things to say, but because I’ve been heads down figuring something out: I’m moving from a content marketing role into a growth role.

I don’t have this figured out yet. What I have is a clearer picture of what I’m preparing for, and I thought it was worth sharing while it’s still messy, not after I’ve cleaned it up into a neat framework.

Let’s get into it 👇

📌 TL;DR

• Growth or GTM isn’t “content marketing but more.” It sits at the intersection of product marketing, content, demand gen, and brand, and that’s a genuinely different way of thinking

• The real shift isn’t adding channels, it’s dropping channel-first thinking altogether

• Knowing your customer now means knowing their pressure points and trade-offs, not just their job title and company size

• AI stopped being something I chat with and started being infrastructure I build on, agents and MCPs doing the repetitive work, so I get the research time back

Why this role, why now

For most of my career, my thinking has been channel-specific. Give me a content gap, and I’ll tell you the blog post, the LinkedIn angle, the newsletter hook. That instinct served me well. Some of last year’s wins came directly from being that specific.

But when I actually sat down and researched what a growth or GTM role involves, I realized channel-specific thinking is not a strength, once you’re operating at this level. This role isn’t about picking better channels. It’s about thinking from the brand outward, horizontal expansion instead of doubling down on one or two channels that already work.

That’s a different muscle. I’m building it now.

Getting inside the customer’s head, not just their persona

Here’s what I would tell anyone making the same move: your product understanding and your sales team’s input have gotten you this far. They won’t get you the rest of the way alone.

• I’m now sitting in on every call transcript I can get my hands on. Not summaries of calls, the actual calls.

• I’m talking to sales more than I ever have. I’m asking about the customer’s age, the inside jokes that land with them, what makes them anxious, what actually relieves that anxiety, what they’re trading off when they choose us over someone else, or nothing at all.

Those are uncomfortable questions to sit with, honestly. A job title tells you what someone does. It tells you nothing about what keeps them up at night. And a brand that’s supposed to feel emotionally relatable can’t be built on job titles alone.

AI as the GTM engineer, not the chat window

I come from a very human, very manual content writing background, and it took me real time to figure out how to use AI as anything more than a chat box. For the longest time, that’s exactly what it was for me. Ask a question, get an answer, move on.

This role changed that. I now think of AI as a lever for the actual mechanics of the job, what a lot of people are calling GTM engineering. So I’m experimenting daily, mostly with Claude.

• I’ve set up agents that track what’s happening across the industry and inside competitors’ worlds.

• I’ve pulled in MCPs for tools like Webflow and Ahrefs, so parts of my job run on their own, which means the time I get back goes straight into research and thinking, not busywork.

Maybe that sounds like I’ve cracked something. I haven’t, not fully. But most of the time, having the repetitive 30% automated is enough to make the other 70% feel possible again.

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Where I actually am

I’m telling you all this because this exact question has come up in almost every 1:1 call I’ve had recently. People aren’t sure how to move from content marketing into the next stage, and growth or GTM is a real next stage worth considering.

This is how I started. I don’t fully know what comes after this either. But I’ll figure it out as I go, and I’ll keep sharing it here as it happens, not after it’s finished and tidy.

P.S. This probably won’t be the last time I write about this. Consider this issue one of the first entries in a running series as the transition actually unfolds, good weeks and messy ones both 😊

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Marketing folks, especially content and SEO people, have a bad habit in general: they focus on one tool, a maximum of five data points, and that’s all.

However, as you transition to full-stack marketing, you will know that you need to dive deeper. There are five places I go regularly to understand why something's working, and Search Console and GA are only two of them.

Let’s get into it 👇

📌 TL;DR

• Each data source shows you a fragment, not the full picture, treat it that way

• Search Console and GA answer “what happened,” not “why”

• Your CRM only sees someone after they’ve already decided you’re worth their time

• LinkedIn comments carry objections weeks before they’d ever show up in a CRM

• Call transcripts have the actual words your buyers use, most teams never open them

• Heatmaps show you the exact sentence where you lost the reader

#1 Search Console + GA: the “what,” not the “why”

Does a traffic spike ever actually tell you why it happened? No. It tells you it happened. You still have to go find out why.

Two things I’ve learned the hard way here:

• A wall of “direct” traffic in GA4 is rarely people typing your URL from memory. It’s usually missing UTM tags. So even your “what” can be broken before you’ve gotten anywhere near “why.”

• Rank and impressions going up mean nothing if engagement on the page doesn’t keep pace. I’ve had pages jump five positions and lose scroll depth in the same month. GSC would’ve told you that’s a win. It wasn’t.

What I actually do: stop reading rank in isolation. Pull it against scroll depth and time on page for the same URL, same date range. If rank’s up and engagement’s flat or down, the query brought the wrong intent, or the page didn’t deliver on what the query promised.

#2 CRM + customer journey: sees people only after they’ve already decided

By the time someone’s in your CRM, the actual decision-making has already happened somewhere your CRM has no visibility into.

There’s a pattern that shows up constantly in attribution work: a channel looks weak on last-click, but strong on assists. Dig in, and you’ll often find someone saw the touchpoint, didn’t act, then searched for the brand directly three weeks later. Your CRM logs that as “organic search.” It wasn’t. It was the channel you almost wrote off.

What I actually do: pull closed-won deals and trace back which content actually touched them before the demo, not what the attribution model assumes touched them. Those two lists rarely match, and the gap is usually where the real story is.

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#3 LinkedIn: the objection shows up here first, weeks before it hits a form

This is the one source on the list that’s giving you the pushback before anyone’s even considering buying.

Someone comments, “Does this work for teams under 10?” Someone says, “We tried something like this, and it didn’t stick.” None of that reaches your CRM. Most of it never reaches your CRM, even after they buy, because by then it’s been reduced to a form field with none of the original language left.

What I actually do: keep a running note of the exact phrases people use, not my summarized version of what I think they meant. Their words, not my translation.

#4 Call transcripts: the most accurate source, and the one nobody opens

Guess which of these five gets used the least relative to how useful it actually is. This one. Nobody has forty minutes to sit through a sales call.

But this is where the real vocabulary gap lives. Marketing says, “Optimize your conversion funnel.” The prospect says, “We’re not getting enough leads.” Marketing says “omnichannel engagement strategy.” The prospect asks, “Should we even be on TikTok?” That gap is why a headline can be technically accurate and still not land; it’s written in the wrong language.

What I actually do: pull the last fifteen or twenty discovery calls, tag the objections and phrases that keep repeating, and use those exact words in the next headline instead of “professionalizing” them into something that sounds more polished and says less.

#5 Heatmaps: where the sentence actually loses the reader

Suppose a content team found that almost 40% of readers dropped off right after the first CTA in their blog posts. Not at the end. Not halfway through. Right after the first ask.

That changed how they wrote intros. Shorter, a table of contents so people could skip to what they needed, and everything before that first CTA treated as the only real estate that mattered.

One caveat, though, low scroll depth isn’t always a bad-writing problem. Could be a slow-loading page, a mismatch between what someone searched for and what they landed on, or a layout that quietly discourages scrolling. Rule those out before you blame the paragraph.

What I actually do: pull the heatmaps for the best- and worst-performing posts side by side. Find the exact line where attention drops, and check whether that line earned the scroll or assumed it.

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I read an article this week that did everything right and still decayed.

Clean structure. Clearly written by a person who knew the topic. The kind of page you skim and think, yeah, that’s solid.

Then I pulled up its traffic and watched it fall from over 2,000 visits to zero in about six months. Over 400 keywords ranked at launch. Almost none now.

This is the first edition of #ContentIcks, where I take a real published page, look at what it actually did in search, and name the thing quietly killing it.

The brand is ClickUp, and the page is their “best ChatGPT alternatives” listicle.

Let’s get into it 👇

📌 TL;DR

• A BOFU page can be well written and still rank for nothing. The writing was never what saved it.

• This one slid from 2,000+ visits and 400+ ranking keywords to near zero in about six months

• The sitewide blog decline is real, but it explains the timing, not the rot

• The actual ick: ClickUp isn’t a ChatGPT alternative, and the page never resolves that claim

• Twenty tools of completely different types in one list isn’t depth. It’s noise wearing a thoroughness costume.

• The home team gets 1,000+ words while real rivals get a line, and readers feel the imbalance even when they can’t name it

You must be aware that ClickUp has lost over 97% of organic traffic…

Here is the obvious explanation, and it deserves a fair hearing. ClickUp’s blog has been losing traffic across the board for a while now (Kamila Olexa wrote a good breakdown, worth a read). So you could file this page under collateral damage.

And you would be partly right. A sitewide decline is a real thing that happened.

But it does not explain why this particular page had nothing to hold onto when the traffic dropped. This BOFU article had some problems when it was first published. The decline is the proof.

So what actually happened to this article?

A page decays when you position it into a consideration set it does not belong in.

That is the whole thing. The writing can be genuinely good. The decision that killed it happened earlier, in the brief, when someone decided what this page was competing with. And that decision was wrong before a single sentence got typed.

Let me show you the three places it shows up.

#1 Intent is a claim. And the claim has to be true.

Is ClickUp a ChatGPT alternative? Not really.

Someone typing “ChatGPT alternatives” already has a picture in their head. They are thinking Gemini, Claude, Copilot, Perplexity. A work management platform is not in that picture, and the article never stops to draw the line between what ChatGPT is actually for and what ClickUp is.

#2 A list is a promise that these things belong together

Look at the tools part of the “ChatGPT alternatives” list apart from ClickUp:

• Generative chat tools (Gemini, Claude).

• Copywriting tools (Jasper, Writesonic).

• An SEO tool (Surfer).

• Coding assistants (CodeWhisperer, GitHub Copilot).

All on one list, as if the person choosing among them is the same person. They are not.

These tools solve different problems for different people, and the article never sets a use case up top to hold them together. So the comparison has no basis.

This is the part that gets BOFU backward. BOFU is not a coverage game. Its entire value is narrow, honest, like-for-like depth, the stuff someone needs when they are one decision away from buying. Twenty mismatched tools are not comprehensive.

#3 How much room you give something tells the reader what you think

ClickUp’s own section runs past a thousand words. The real alternatives get fewer than 400 words.

For example, Claude, A real (and probably better alternative to ChatGPT) gets shrunk to simply a “creative, conversational text generation platform.” The article was written or updated in February 2026. Ideally, by then, Claude will have introduced so many new features, and this article simply positions it as a text generation platform. It is a line written to keep the competitor small, so ClickUp can look bigger.

Someone looking for ChatGPT alternatives already knows how powerful Claude is; reducing it down to this extent is simply inappropriate.

And then there is GEO. When an AI engine is deciding what to pull into an answer, that imbalance is not just a vibe a human picks up. It is legible. What reads as dishonest to a person reads as low-quality to the system, which chooses what to surface.

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What I would have done instead

The first move might not be to write it. At least that’s what I would have done because ClickUp and ChatGPT are in no way something to be placed together

But say you want the page anyway. Here is how I would have gone about it.

• I would pick one real intent and commit. Which kind of ChatGPT alternative is this for: writing, research, coding, or general chat? Name it in the H1 and the first two lines, so the reader knows in five seconds whether they are in the right place.

• I would cut the list to three to five tools that actually compete on that intent. The depth is the entire point.

• I would give every tool its honest, current version, including the ones that compete with me. Especially those. If a rival is strong at something, I would just say so, because the reader already knows, and pretending otherwise only costs me their trust.

The discipline here is subtraction. Cut tools, cut claims you cannot defend, cut the parts where you are unnecessarily flattering yourself.

Writing was not always the hard part

Human writing used to be a moat. It is not anymore. A model can produce a tidy, readable, grammatically perfect listicle in a minute, and increasingly it does.

What survives is positioning honesty. Pages that earned their spot in the search instead of muscling into one. Pages where the intent is true, the comparison is fair, and the reader leaves trusting you a little more than when they arrived.

P.S. Which article do you want me to pick up next? Reply to the mail and let me know!

P.P.S. I have nothing against the brand, the writer, or the team that published it. This is a simple analysis by a fellow marketer.

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For a while now, I’ve had this habit of checking what AI actually says about brands. Running prompts, watching who gets named, who gets cited, who gets ignored. You’ve seen me do it here before with the HubSpot teardown.

I had some observations in the process: pages that were ranking perfectly fine on Google. Solid positions, steady impressions, nothing wrong on paper. And yet when I asked ChatGPT or Perplexity the exact question those pages answered, they weren’t there. A different page was getting pulled instead.

That’s when I understood this: content doesn’t decay on one front anymore. It decays on two.

(Remember the blog lifecycle issue, where I tracked a single post from indexing all the way to the keep, refresh, or delete decision? This is the same decision. Just scaled to your whole library. And with a second scoreboard you probably aren’t watching yet.)

That second scoreboard is what this issue is about.

Let’s get into it 👇

📌 TL;DR

• Your content now decays on two curves: Google rankings and AI citations. They move independently. A page can hold its Google spot and quietly disappear from AI answers.

• A content audit in 2026 has one real job: moving your pages from Invisible to Mentioned to Cited.

• The phases aren’t complicated. Most of the work is in the part everyone skips: actually making the call on each page.

• AI engines lean toward fresher content. One study of about 17 million citations found that AI-cited pages are roughly 26% “fresher” than pages ranking in Google’s top 10 (Ahrefs).

• You can hand this whole process to Claude as a reusable skill, so you run it once a quarter instead of rebuilding it from memory every time.

• Refresh beats writing new. A page that already has authority comes back in weeks. A fresh one takes months.

Why bother auditing at all (the honest version)

For years, a content audit was more like a regular chore. Find the dead pages, prune them, refresh a few, move on.

Now you’ve got two engines deciding whether your content gets seen, and they don’t agree with each other.

• Google decides what ranks

• AI engines decide what gets quoted

A page can sit comfortably on page one of Google and still be completely absent when someone asks ChatGPT the exact question that page answers. So the audit is how you figure out, page by page, where you actually stand on both scoreboards.

I keep coming back to the same three-tier way of looking at it:

• Invisible. AI doesn’t bring you up at all, even when the question is yours to own.

• Mentioned. You get named, but no link, no source credit.

• Cited. You get named and pulled as the source. The whole point.

A content audit in the AI era is the exercise of finding every page that sits in Invisible or Mentioned and figuring out what it would take to move it up. That’s the frame. Everything below serves it.

AI engines recommend brands and entities, not just URLs. Even the big benchmark sites are feeling this.

The phases (the actually-do-this version)

Phase 1: Get everything into one list

Pull every URL you have. Export your sitemap or your CMS list, then run a crawl to catch the orphan pages nobody links to anymore. Screaming Frog is free up to 500 URLs, which covers most of us.

For each page, grab the basics: title, publish date, last updated date, word count, and status code. Don’t try to audit the whole site in one sitting. Start with the blog. Finish a section before you touch the next one.

Phase 2: Stack your data layers

Three free sources do most of the heavy lifting:

• Search Console for clicks, impressions, and position, broken down by page and by query. Look at the 12-month trend, not last week’s numbers.

• GA4 for sessions, conversions, and engagement. What’s actually doing business, not just getting visits.

• A backlink check (Ahrefs or Moz free tier), so you know which pages have links worth protecting before you go deleting anything.

Then the layer most audits skip entirely. The AI visibility layer.

Take your money prompts. The “best tool for,” “alternatives to,” and comparison-style questions, not the informational fluff. Run them manually in ChatGPT, Claude, and Perplexity.

Note three things:

• Where do you show up

• Where a competitor shows up instead of you

• Where you get pulled as a source but never named.

That last one is real, by the way. I’ve watched ChatGPT lift content straight off a site to build its answer without ever naming the brand. Cited, but not mentioned. You only catch it if you’re looking.

Phase 3: Perform the actual audit

Four calls, one per page:

• Keep. Ranking well, stable, getting cited. Leave it alone. (Editing a page that’s already working can re-trigger indexing for no reason at all.)

• Refresh. Still has a real keyword, still holds some backlinks, structure is fine, but the information is stale, or it’s started slipping. Most of your pages will land here.

• Consolidate. Two or three pages quietly fighting over the same intent. Merge them into the strongest one, and redirect the rest.

• Prune. No links, no rankings, no traffic, and the topic is covered better elsewhere. Delete and redirect.

Strip your bias out before you decide.

There are three that wreck audits: traffic nostalgia (”but this one used to do so well”), brand attachment (”I wrote this, I like it”), and keyword thinking (”but it technically targets a keyword”). Judge what the page does now. Not what it once meant to you.

If a few of your “keep” calls start feeling uncomfortable, good. That discomfort usually means you’re being honest.

Phase 4: Prioritize, then actually do it

Don’t work in publish-date order. Start where the upside is biggest for the least effort:

• Pages sitting at positions 8 to 15. They’re one good refresh away from page one.

• Pages that lost the most ground in the last few months.

• BOFU pages before top-of-funnel ones. They’re closer to revenue, and they’re the backbone of your AI search visibility.

And when you refresh, refresh for real. Update the stats and dates, yes. But also front-load a direct answer under each heading, tighten the page into clean, scannable sections, add a table where one belongs, and fix the internal links. Changing the date and nothing else is a shortcut both Google and AI see straight through.

🎁 Bonus: Now turn the whole thing into a Claude skill

Here’s what saves you from running this from memory every quarter.

You can hand the process to Claude as a skill. A skill is just a saved instruction file that Claude reads automatically whenever you start an audit, so you stop re-explaining yourself every single time.

One honest caveat first, because it matters more than the build. You can’t make an AI that audits your content for you. You can make one that assists.

What goes inside it:

• Context first. Your product, your ICP, your category, your competitors, and your money prompt. Every audit starts from this, so Claude isn’t guessing who you are.

• The phases are written as steps. The four above, in order.

• The inputs you’ll feed it. Your crawl export, your Search Console export, your GA4 numbers, your manual AI-visibility notes.

• The output you want. A table, one row per URL: the call (keep, refresh, consolidate, prune), the reason, the priority. Plus, a separate list of every prompt where a competitor is cited, and you aren’t.

• A couple of worked examples, and a few hard “never do this” rules.

No code required. The simplest version is a Claude Project. Drop your instructions in, upload your exports, and ask it to run the audit. Save it. Next quarter, swap in fresh exports and run the same thing again.

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You build it once. You reuse it forever. That’s the whole appeal.

The quick wins to expect

I’m tagging each one so you know which scoreboard it moves.

• [AI] for AI search

• [Google] for classic search

• [Both] for the ones that count twice

1. Stale pages start getting cited again. [AI]

Refresh a page with current data and a clean answer up top, and AI engines often re-pull it fast. Freshness is one of the few levers that move quickly here.

(Worth knowing: this hits hardest in Perplexity and ChatGPT. Claude leans more on what it already knows, so for Claude, being mentioned across other sites matters more than your publish date.)

2. Lost traffic comes back in weeks, not months. [Google]

A refreshed page keeps its existing authority and backlinks, which is exactly why it climbs again quickly. A brand-new page on the same topic would take you half a year to reach the same spot.

3. Your cannibalizing pages stop fighting each other. [Both]

When three thin posts split the authority for one keyword, merging them into one strong page concentrates everything. Google and AI both prefer one clear, authoritative source over three half-answers.

4. You catch decay while it’s still cheap to fix. [Both]

A page that’s lost a chunk of its clicks over a couple of months needs a light refresh. The same page, ignored for a year, needs a full rewrite. The audit is how you catch it at the cheap stage.

5. Your best pages get easier to quote. [AI]

Clear headings, a direct answer under each one, a table, and the right schema. None of it is glamorous. All of it makes your page easier for an AI to lift a clean sentence from. And getting quoted is the game now.

6. You finally see where you’re invisible. [Both]

The AI visibility layer shows you every prompt in which a competitor is named, but you aren’t. That gap list is basically your content roadmap for next quarter, already written.

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How’s it going? 🌻

The job market is brutal, especially for content marketers! The last thing you want to do now is apply for the wrong jobs or go to an interview unprepared.

I’ve been on both sides of this conversation.

• Applied for roles where the JD looked exciting, and reality hit by week two.

• Watched genuinely good content marketers take jobs that dimmed them.

Not bad decisions. Just no real filter for what they were walking into. The AI era has made this harder. More job titles. More noise. More “AI-first” companies.

So here’s how I would approach a job search in 2026 as a content marketer.

Let’s get into it 👇

📌 TL;DR

• Figure out which type of content marketer you are before you read any JD

• Three paths exist right now: specialist writer / AI-savvy workflow builder/brand and campaign thinker

• “AI-native” in a title means nothing without the right follow-up questions in the interview

• The JD is only half the picture. How leadership thinks about content matters just as much

• Green flags and red flags in a JD are learnable. You just have to know what you’re looking for

Before you read any JD, figure out which bucket you’re in

AI has split this field into three paths. Applying for the wrong one wastes your time and puts you in a role that won’t let you grow the way you actually want to.

Bucket #1: You’re primarily a writer, and you’re very good at it

You are someone with a portfolio that shows range, performance, and an understanding of formats beyond SEO articles.

Think: survey reports, thought leadership, founder LinkedIn content, case studies that actually tell a story. These are the things that still have real value in a world where Claude can produce a decent 1,500-word blog in minutes.

There are fewer of these roles now. Not zero, but fewer. And the ones that exist are asking for more than writing skills.

Companies genuinely hiring for strong writers want portfolio and performance data.

• Did it rank?

• Did it convert?

• Did it get cited in AI search engines?

They’re evaluating your judgment about what to write and why, not just how well you write.

This is already playing out at hiring tables everywhere. Companies are briefing experienced writers properly and getting back technically fine work. Clean sentences, correct structure. Nothing wrong with it. But nothing that sounds like them either. So they close the doc, open an AI tool, and spend weeks shaping the output with someone who actually knows the product.

✅ What to look for in JD:

• Portfolio plus performance requested (not just samples)

• Format variety beyond blogs

• A senior editor you’d report to rather than a growth PM who treats content as a checkbox

🚨 When to Avoid:

JDs where the primary KPI is volume. “Publish X articles per week at scale” is a different job, and a shrinking one.

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Bucket #2: You think in workflows, not just pieces

Being AI-savvy is not knowing how to use ChatGPT or Claude to produce a decent draft. Everyone does that. That is not a differentiator.

Genuinely AI-savvy in a content role means you can build content systems. Look at a content operation and figure out where automation adds real value. Brief, build, and QA an AI workflow. Understand that the AI output is the first draft, not the deliverable, and know how to close that gap consistently.

The numbers back this up: Content Marketing Manager listings dropped 73% since 2023.

Real B2B SaaS JDs are already shifting. One I came across recently asked for “a genuine understanding of how AI-driven search (AEO/GEO) is changing content discoverability” as a baseline requirement, not a nice-to-have. These aren’t outliers anymore.

✅ What to look for in JD:

• Workflow-building and content ops mentioned

• AEO/GEO alongside content knowledge

• Cross-functional scope

• Outcome-based success criteria rather than a publish cadence

🚨 When to Avoid

“AI-native” titles with nothing behind them.

Ask the interviewer/recruiter questions like:

• What does your current content workflow actually look like?

• What have you tried to automate?

• What didn’t work?

If they can’t answer this specifically, you know what you’d actually be walking into.

Bucket #3: Your strength is ideas, campaigns, and brand

You come from content, but where you shine is the bigger picture.

• Brand storytelling

• Executive thought leadership

• Campaigns

• Events

• LinkedIn strategy

Work that requires a real point of view, strong taste, and the ability to hold a narrative together across formats and channels.

This is genuinely the most evergreen path. Not because AI can’t help here, it can, and it does. But because the ideas still have to come from somewhere. Brand campaigns are still at least 50% human-dependent. The creative instinct to know when something lands versus when it’s clever but pointless? That’s still yours.

✅ What to look for in JD:

• Cross-functional ownership (not just a content calendar)

• Thought leadership and campaign work

• Senior access to where strategy actually happens

• Mentions of positioning and narrative work

🚨 What to be careful about

• Companies that say “brand” but mean social media management

• Roles that promise strategy, but the day-to-day is execution without any input.

You’ll usually only find this out in the interview by asking about the last campaign they ran and who made the creative decisions.

What to look for in the company, not just the JD

You can have the right role description and still be in the wrong place.

The way founders and leadership perceive content determines almost everything about your experience.

• If they understand that content takes time, compounds, needs distribution, and works differently from a paid ad, you’ll have space to build something.

• If they think you'll publish a blog and magically get traffic by next week, you’ll spend most of your energy explaining your job rather than doing it.

Also, during an interview, ask the interviewer about AI specifically. Ask what they’re using it for and what hasn’t worked.

You want the second type.

Green flags vs. red flags in the JD itself

💚 Green flags:

• Specific content formats mentioned, not just “content”

• Portfolio plus performance data requested

• AEO or GEO mentioned alongside SEO

• Cross-functional scope explicitly stated

• Outcome-based success criteria, not just output targets

• Language that suggests you’d have strategic input: “shape our narrative,” “own the content roadmap”

🚩Red flags:

• High volume is the primary KPI

• No mention of distribution, measurement, or iteration

• AI trainer or content reviewer roles dressed up as marketing jobs. These exist, and they’re growing; they’re just a different career track entirely

• JDs so vague you can’t picture what a good Tuesday in this role actually looks like

The question nobody asks, but probably should

What’s the culture of content like inside this company?

Not externally. Internally. With the teams you’d work with every day.

Content marketing is cross-functional work, even when you have a seat at the table. You’ll always be asking product for input, sales for intel, and CS for customer stories. If those teams don’t understand why content matters, your job becomes twice as hard and twice as lonely.

The most underrated skill for a content marketer, especially in a startup, is the ability to say no. Not rudely. With a reason.

“This will take X time and add Y value relative to what we’re trying to do right now.”

If the environment punishes that, you’ll spend your time on work that doesn’t connect to anything, doesn’t move any metric, and doesn’t show up on your resume in a way that matters.

Look for places where prioritization is a conversation, not a battle. Those environments exist. They’re worth holding out for.

The honest part

The AI era hasn’t made content marketing jobs harder to find. It’s made them harder to evaluate.

The JD is no longer enough. The title is no longer enough. “AI-native” means nothing without context. And “great growth opportunity” can mean genuinely interesting work, or it can mean three jobs for the price of one.

Know which bucket you belong in. Know what company culture you need. Learn to read the signals before you say yes.

That’s how you stop taking roles that look right on paper and start building something that actually compounds.

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How’s it going? 🌻

We’re in Part 3 of the #BeyondBlogs series, and this one is the one I’ve been most nervous to write. Because, very honestly, I have made some of these mistakes myself.

Anyway, let’s get started, but before that, quickly linking the first 2 editions of this series:

Part 3 is about what goes wrong.

Not the obvious stuff. Not “don’t send generic emails” (we covered that). I mean the subtler things. The mistakes that don’t show up immediately. The ones where the campaign looks fine on paper, the dashboards look decent, and somehow nothing moves. Those are the ones worth talking about.

Let’s get into it 👇

📌 TL;DR

• Writing for one person means the whole buying committee gets nothing useful. ABM is one-to-account, not one-to-person.

• Inbound metrics will lie to you in ABM. Traffic and downloads are not the signals you need.

• The best intel for ABM content isn’t a persona doc. It’s a 30-minute conversation with a sales rep.

• Doing account research once, before you write, is not the same as knowing what that account actually cares about right now.

• Going dark after one great piece of content is how you lose the momentum you just built.

Mistake #1: Writing for the decision-maker but ignoring the buying committee

This is the one that gets content marketers the most, because it feels like the right call.

You know who the budget holder is. You write for them. You make the case, you speak their language, you address the business outcome they care about. That part is solid.

But here’s the thing about ABM accounts: the decision-maker rarely decides alone.

B2B deals involve somewhere between five and eight stakeholders. The senior buyer might approve the budget, but the IT lead is the one raising the security flag in the internal Slack channel. The finance person is the one asking about the ROI math. The person who will actually use the product every day is the one quietly making the case for or against it in the post-demo debrief.

If only one person in that group has what they need to feel confident, the deal stalls. And it won’t be obvious why, because the decision-maker loved what they saw.

So when content is written only for the top of the hierarchy, it creates a weird situation where the most important person says yes, and everyone else sits on the fence.

How to avoid it:

Before you write anything for an ABM account, map the buying committee.

Ask sales:

• Who else is in the room?

• Who has raised concerns before in similar deals?

Then audit your content against that map. Do you have something that speaks to each of them?

Not a full piece for every person, but something.

• A short insight doc that addresses the IT angle.

• A one-pager framed around what it means for the team doing the daily work.

• A case study that mentions the finance team’s concerns by name.

The goal is coverage. If one person in the buying committee has no good reason to say yes, they become the quiet reason the deal doesn’t close.

Mistake #2: Using inbound metrics to judge ABM content

This one is a measurement problem, but it starts much earlier, in how content is briefed and what success looks like when you walk in.

In inbound content, the metrics that are typically tracked include:

• Traffic

• Downloads

• Time on page

• Scroll depth

• Organic impressions

They tell you something is working. And so when content marketers are pulled into ABM work, they bring those metrics with them. It’s what they know how to report on. But those numbers will actively mislead you in an ABM context.

ABM content is not designed to reach a crowd. It’s designed to move five, fifteen, or thirty specific accounts forward. The entire point is that the audience is small and intentional. Which means traffic will be low, downloads will be low, and if you measure ABM content the way you measure a blog post, you will always conclude that it isn’t working, even when it is.

This often means: teams start optimizing for the wrong signals. A piece of ABM content gets quietly edited to be “more searchable.” A landing page gets broadened so the messaging appeals to more people. The focus drifts. And the whole point of ABM, that specificity, starts to erode.

How to avoid it:

Before an ABM content project starts, have an explicit conversation about what success looks like for this piece, specifically. Ask:

• Which accounts is this for?

• What stage are they at?

What does it mean if this content does its job?

The answers might be: this account had a follow-up call. This prospect replied to the email the sales rep sent after. This deal moved from consideration to negotiation.

These are not easy things to track. But they are the right things. Define them up front so you’re not defaulting to the easy-to-pull metrics.

Mistake #3: Creating content for ABM without talking to sales first

Content marketers are good researchers. Give them a vertical, an ICP, a set of company profiles, and they will do their homework. They’ll read industry reports, check LinkedIn, and look at what the company has published. That work matters.

But there is a kind of knowledge you cannot get from research alone: what actually happens in the sales conversation.

Sales reps know which objection comes up every single time. They know the exact phrase the champion uses when they’re trying to get internal buy-in. They know which concern, if left unaddressed, makes the deal go quiet. They’ve been in the room, or on the call, in a way that no amount of reading can replicate.

When content is built without that input, it tends to be technically correct but strategically off. It addresses the questions an outsider would assume the buyer has, not the ones the buyer is actually sitting with. Informed ABM audiences can feel the difference within the first paragraph.

How to avoid it:

Make a 30-minute conversation with a sales rep the first step of every ABM content project. A real conversation where you ask:

• Where does this account type usually get stuck?

• What have you been explaining over and over that you wish just... existed as a piece of content?

• What would make your next conversation with this account easier?

Then build that. It will almost always be more useful than what the brief said to make.

Mistake #4: Treating the content brief as the research

Persona docs are genuinely useful. They save time, they create alignment, and they give content marketers a starting point that isn’t a blank page. Nobody is arguing against them.

But in ABM, a persona is a starting point, not a destination.

The difference between inbound and ABM isn’t just the size of the audience. It’s the specificity.

A persona tells you what that type of person generally cares about. It doesn’t tell you that this company just went through a leadership change, or that they’ve been public about a compliance challenge, or that their current vendor just had a very public outage. The brief won’t have that. The persona definitely won’t.

When content is built solely on the persona, it reads as if it were made for a crowd, even when the targeting is precise. That’s the paradox. You can have a perfectly targeted ad running to exactly the right account, driving them to a landing page that was written for someone vaguely like them. The targeting was specific. The content wasn’t.

How to avoid it:

Before you write anything for a named ABM account, spend 20 minutes on actual account-level research.

• What has this company published recently?

• What has their leadership been saying on LinkedIn?

• Are there news stories, funding announcements, product launches, or industry pressures that are live right now for them?

• Is there anything on their careers page that tells you where they’re growing?

That context is what makes ABM content feel like it was made for them. It’s a 20-minute investment that completely changes how specific you can get.

Mistake #5: Going dark after the first touchpoint

This one is the most fixable, and also the most common.

A good piece of content goes out. Maybe it’s a well-researched insight doc. Maybe it’s a sharp email with a genuinely useful angle. The account opens it. Maybe someone clicks. And then... nothing. No follow-up content, no next touchpoint, no clear idea of what happens after this moment.

ABM requires a cadence. Content that moves with the relationship. The first piece is not supposed to close the deal; it’s supposed to earn the right to the next conversation. But if there’s nothing planned for after it lands, the momentum you just built disappears.

Here’s how this usually happens: the content team was briefed on an asset, they made the asset, and the project was considered done. Nobody mapped out what comes next. Sales sends the content, waits for a response, and when the account doesn’t immediately reply, the outreach cools off. The account wasn’t uninterested. There just wasn’t a reason to re-engage.

Meanwhile, in inbound, this problem doesn’t exist in the same way. Someone finds a blog post and can discover 12 more. The next touchpoint is already there. In ABM, you have to deliberately build that path.

How to avoid it:

When you’re briefed on a piece of ABM content, ask one question before you start writing:

• What comes next?

• What does the follow-up look like after this lands?

If the answer is “we’ll figure that out,” that’s a flag. Push for at least a rough sequence. This goes out first, then this, then the sales rep reaches back out with this.

You don’t need to build everything at once. But knowing where your piece fits in the sequence changes how you write it. The CTA you choose, the question you leave open at the end, and the thing you deliberately don’t explain all of these create a natural pull toward the next conversation.

Content that knows its role in a sequence is always sharper than content that’s trying to do everything in one go.

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Onwards and upwards,

Sreyashi

P.S. Part 4 is coming, how to track ABM content performance, and which channels are actually worth your time.

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How’s it going? 🌻

Google published something last week that the content marketing and SEO world has been waiting for.

Their first official guide on how to optimize for AI search features, AI Overviews, and AI Mode, all of it. If you’ve been watching the AEO vs GEO vs SEO debate play out on LinkedIn (and honestly, who hasn’t), this is the closest thing to an official answer you’re going to get.

Let me break it down for you. What the guide actually says, where things get a little murky, and what it means for the work you’re doing.

📌 TL;DR

• Google officially said GEO and AEO are just SEO, no separate strategy needed for AI search

• What still matters: non-commodity content with a real POV, clean technical structure, quality visuals

• What to ignore: llms.txt, content chunking, AI rewrites, inauthentic mentions

• But the same week, Google’s own Chrome team shipped a Lighthouse audit that checks for llms.txt, the two teams are talking about different things (search rankings vs. agent readiness), and that distinction matters

• Bottom line: use Google’s guide directionally, not as a rulebook; the gap between their principles and your Monday morning to-do list is still yours to fill

What Google's guide actually says

The guide was published on May 15 by Google Search Central.

The central message is pretty straightforward: there is no separate strategy for AI search. GEO and AEO? Google put both terms in quotes and said, paraphrasing only slightly, that it’s all just SEO.

Here’s what they want you to focus on:

• Write non-commodity content with a real, first-hand point of view. Not a summary of what everyone else has already said. Not something AI could generate in thirty seconds.

• Keep your technical structure clean. Crawlable, indexable, fast. The basics.

• Add quality images and video where it makes sense. AI features pull visuals too, not just text.

• Use structured data as part of your overall SEO, not as a special AI hack.

• Focus on what your actual readers want, not on creating pages for every possible keyword variation.

And here’s what they said you can ignore:

• llms.txt files or any special AI markup

• Rewriting your content specifically for AI systems

• Breaking content into tiny “chunks” for AI to parse

• Chasing inauthentic brand mentions across the web

• Over-indexing on structured data for AI purposes

Google basically said: do good SEO, write content humans actually want, and you’re optimizing for AI search too.

Where the picture gets a little murkier

Now here’s the part worth paying attention to.

The same week Google’s Search Central team told you to ignore llms.txt, Google’s Chrome team shipped Lighthouse 13.3 with an “Agentic Browsing” audit that checks for exactly that file. The Lighthouse documentation says without it, agents “may spend more time crawling the site to understand its high-level structure and primary content.”

That is not the language of something irrelevant. The Search Central guide, published May 15, called it an “unnecessary AI text file” you can ignore. That’s a direct quote from the mythbusting section.

Here is what I think is actually happening.

Google Search Central is talking about one thing: whether llms.txt affects your rankings or visibility in AI Overviews. The answer there is no, at least for now. But the Chrome and Lighthouse teams are talking about something different: whether AI agents can navigate and act on your site efficiently.

Those are two separate problems. One is about being found. The other is about being usable once an agent lands on you. The confusion comes from reading both as the same guidance. They are not.

And this is not new behavior from Google. Their positions on structured data, content length, and E-E-A-T have shifted depending on which team was speaking and the year. That does not make the guidance useless. It makes it directional. There is a difference.

What this means for you (SEOs and Content Marketers)

So after all of that, what do you actually do?

Your SEO foundation is still the right bet

Crawlability, content authority, technical structure, and a genuine point of view. Nothing in this guide changes that. If you have been doing that work already, you are set up well for AI search.

Non-commodity content is now the baseline, not the differentiator

If AI can generate a decent summary of your article, you have not written anything worth citing. The bar has moved. First-hand experience, original research, real opinions. That is what gets pulled into AI responses.

The AEO and GEO labels do not matter as much as the skills behind them

Structuring content for easy extraction, building entity authority, and writing with actual expertise. That work is still valid and still worth doing. Google just does not want you calling it anything other than SEO.

For agents specifically, llms.txt is worth keeping on your radar

Not urgent, not a panic, but not something to dismiss either. Agent readiness and search rankings are different surfaces. The Lighthouse audit exists for a reason.

Here is my honest take after reading the whole thing:

Google’s guide is useful. It gives you clarity on what not to chase, which is genuinely valuable when the internet is full of people selling you a separate AI search strategy for a separate retainer. But the guide is three pages long, and AI search is moving fast. It tells you the principles. It does not tell you what to do next Monday.

That gap is yours to fill. With your own analysis, your own testing, your own read of what is working for your specific content and audience. That has always been the actual job. This guide does not change that.

Read the signals. Make your own calls.

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Onwards and upwards,

Sreyashi

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How’s it going? 🌻

Something I’ve been putting off for longer than I’d like to admit: picking an AI visibility tool for Compport.

Not because I didn’t think we needed one. We did. When AI search is increasingly where buyers are discovering tools, you can’t just assume you’re showing up. You need to actually check.

But here’s the thing.

I’ve been running prompts manually. I’ve asked our US team to do the same: incognito windows, no saved history, no logged-in accounts. And even with all that, the results were all over the place. Same country, different users, different outputs. So before I committed to paying for a dashboard, I had a question I couldn’t shake: what exactly am I trusting here?

That skepticism didn’t go away. But I decided it wasn’t a reason to do nothing. It was a reason to go in carefully.

So I tested everything I could get my hands on. Free trials, demos, the works. Here’s how it went.

📌 TL;DR

• Airops is expensive and built for content automation at scale, not visibility tracking.

• Scrunch is prompt-dependent and mostly manually replicable at zero cost.

• Profound has the best features but is priced for teams with a real budget.

• Peec.ai has a free trial too limited to form any real opinion on.

• Otterly matched our budget, 15 prompts across major engines, actually usable from day one.

The tools I tested (and why most of them didn't make it)

AirOps

A content automation platform that added AI visibility tracking on top. Starts at $200/month, jumps to $2,000/month for multi-engine visibility.

I could see what Airops was going for. It’s not just a tracking tool. It’s a full content operations platform with workflow automation, CMS integrations, and AI visibility layered in. For a large content team running high volumes, it probably makes sense.

For me, it didn’t. I’m a content person who likes to be in control of what goes out. The outputs needed too much editing to be useful, and paying for automation only to have to babysit wasn’t a trade-off I was willing to make. The pricing doesn’t help either. There’s a steep cliff between the free plan and anything that actually tracks multi-engine AI visibility.

Scrunch

Prompt-based AI visibility tracking. A usable plan starts at $250/month.

Scrunch is prompt-dependent. Whatever you track is only as useful as the prompts you put in. The auto-generated ones are lean branded, so you’ll need to add non-branded prompts yourself anyway. Most insights are things you could pull manually in 20 minutes.

It’s not a bad tool. It’s just hard to justify the pricing for a small in-house team.

Profound

Topic-level AI visibility platform. Realistically starts at $399/month.

The topic-level visibility breakdown is genuinely useful. It tells you not just whether you’re visible, but where across themes and categories. Better than Scrunch in that sense.

But at $399/month and above for a plan that covers your actual needs, it’s a significant investment for a small marketing team. The features are good. The budget ask is hard to defend when you’re still figuring out how much to trust the data.

Peec.ai

AI search analytics tool. Starter plan at around $95/month.

Heard good things about Peec before I tried it. Clean dashboards, solid competitor benchmarking, nice UI. But the free trial was limited enough that I couldn’t get a real sense of whether it delivered on what people were saying. I spent more time figuring out the platform than actually getting useful data out of it.

Might be worth revisiting once we have a clearer sense of what we’re tracking. For now, it went on the shortlist but not the final call.

And then there was Otterly…

I almost didn’t test it. The name kept getting confused with Otter.ai in search results, and I nearly skipped it entirely. I’m glad I didn’t.

Otterly’s Lite plan is $29/month. It includes 15 search prompts tracked across the major AI engines: ChatGPT, Perplexity, Google AI Overviews, and Copilot.
For a small marketing team just building a baseline, that’s enough to start.

What I actually liked:

• No content generation, no automated publishing. Just monitoring. For a content person who wants control, that’s a feature, not a gap

• Setup is fast. You’re not configuring a platform for two weeks before seeing anything useful

• The Recommendations feature tells you what to do with the data, not just what it says

• Simple overview reporting, easy to read, not a data dump

• Integrates with Google Analytics, so you can connect AI visibility to traffic you already track

What I’m still watching:

• Dashboard refreshes weekly, not in real time. You’re working from a snapshot, not a live signal

• 15 prompts feels right for now. I’ll probably hit that limit faster than expected once we get more structured

One thing I haven’t stopped believing

No tool fixes the variance problem.

AI search results still shift by user, by session, by geography, by whatever personalization bias is baked into someone’s account. What a tool gives you is a consistent reference point: a way to track direction over time, not an absolute truth.

That’s useful. It’s just not the same as certainty. If you’re thinking about investing in one of these, go in with that expectation set.

The real work is still in the content. The tool just tells you whether it’s showing up.

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Onwards and upwards,

Sreyashi

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We’re back with Part 2 of the #BeyondBlogs series.

If you missed Part 1 (published April 18), here’s the short version: ABM or account-based marketing is not the sales team’s job alone. Content marketers are in the room, whether they know it or not.

Every email, one-pager, landing page, and ad running through an ABM campaign is a piece of content. If that content is generic, the targeting is a waste of money.

This edition is about what to actually do. Specifically, the strategies that make ABM content work. Not theory, not a framework someone drew in a boardroom. These are the real things that make a target account pause, feel seen, and actually want to respond.

Let’s get into it 👇

📌 TL;DR

• Write for the vertical, not just the persona. Same product, completely different content for different industries.

• Match your CTA to where the buyer is. Cold accounts want a reason to trust you, not a demo link.

• Build around a real trigger. A deadline, a funding round, a leadership change. Connect it to what you solve.

• Give sales content they can actually use. Short, objection-specific assets that fit the conversation they’re already having.

• Use interactive content mid-funnel. Let buyers do their own math and self-qualify.

• Co-create with someone the account already trusts. The credibility transfer does what vendor content alone can’t.

Strategy #1: Write for the vertical, not just the persona

Here’s something that sounds obvious but isn’t practiced enough.

The same product, pitched to two different industries, needs two completely different pieces of content, not a light edit, a full rethink. The pain is different, the vocabulary is different, and the KPIs your reader is accountable to are different.

For example, a Head of IT at a mid-market SaaS company thinks in terms of ticket resolution time and shadow IT risk. A VP of HR at an enterprise thinks in terms of compliance exposure and retention data. Same product category, completely different entry points. If your content tries to speak to both with the same language, it ends up speaking to neither.

ABM gives you the opportunity to go narrow. Use it. Write the version that sounds like you’ve spent time in their world, because in the research phase, you should have.

Strategy #2: Match the CTA to where the buyer is

This one comes up constantly in ABM campaigns and almost always gets it wrong.

A cold target account that has received its first touchpoint is not ready to book a demo. The jump is too big. The trust isn’t there yet. A CTA that asks for too much, too early, tells the account that you’re not really paying attention to them, you’re just running a campaign at them.

Better ABM CTAs move with the relationship.

• Early stage: read something useful, download something relevant, attend something low-commitment.

• Mid-stage: have a conversation and explore a use case together.

• Late stage: see the product, talk to an expert.

The content’s job is to make the next step feel logical, not like a sales trap.

Strategy #3: Use a real hook, not a manufactured one

ABM content works best when there’s a genuine reason to reach out at that specific moment, for example, a regulatory change, a funding announcement, a leadership hire, an industry event, or a compliance deadline your target account is suddenly under pressure about.

This is called a trigger, and it’s what separates ABM outreach that feels timely from ABM outreach that feels random. The content you build around it: an email, an insight doc, a short blog, needs to connect that trigger to a specific problem your product solves. Not loosely, specifically. The linkage has to be real, or it falls apart immediately.

The hook only works if the product can actually deliver on what the content promises. Attracting the right audience with a hook and then disappointing them with the product is worse than not reaching out at all.

Strategy #4: Give sales something they can send

A lot of ABM content fails because it lives on the marketing side and never makes it into the actual sales conversation.

Think about what a sales rep does when they’re working a target account. They’re sending follow-up emails referencing points that came up in a discovery call, trying to keep the account warm between meetings. They need content that slots into that workflow: not a 2,000-word blog, but a short insight doc, a checklist, a one-pager that addresses a specific objection.

Writing sales enablement content is a content marketer’s job, even if nobody has explicitly said so. The closer your content is to the actual conversation happening in the account, the more it moves things forward.

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Strategy #5: Let accounts self-qualify with interactive content

This one is underused and very effective at the mid-funnel stage.

A calculator, a readiness assessment, a diagnostic quiz: these work in ABM because they let the buyer do their own math. They’re not reading about why your product helps companies like them. They’re putting in their own numbers and seeing what the output looks like for their situation. That’s a completely different level of buy-in.

It also helps your sales team walk into the next conversation with actual data: this account filled out the calculator, here’s what they said their current cost is, and here’s where they see the gap. That’s a warmer conversation than cold outreach.

Strategy #6: Co-create with someone the account already trusts

Target accounts are skeptical of vendor content. It’s not personal — they’ve seen a lot of it and learned to filter fast.

One way around that: create content with someone whose word already carries weight with that audience. A mutual customer, a relevant analyst, and an industry voice they follow. A webinar, a report, an interview, something where the credibility transfer is natural because the other party genuinely has skin in the topic.

This takes more effort to set up, but the trust dividend is significant. It’s not your company telling the account they should care about this problem. It’s someone they respect saying it to.

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If any of this connects with something you’re working on right now, hit reply and tell me what you’re dealing with. The messy real-world version is always more interesting than the theory.

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Onwards and upwards,

Sreyashi