We started working with Level AI in 2024 with the goal of ranking for high buying-intent SEO keywords like “call center customer analytics tools.” In 2025, as AI search exploded in popularity, that goal expanded to include getting LLMs to recommend Level AI when users ask for customer intelligence solutions. 

Across both traditional and AI search, the goal is always the same: drive conversions, not just traffic. That principle guided both Level AI’s SEO and AI search (AEO/GEO) strategy. 

This case study focuses on the AI search component. We’ll show how we earned Level AI brand mentions (not just citations) in 100+ product-centric prompts using our Prioritized GEO strategy. 

If you’d like help improving your AI search visibility, get in touch with us.

How We Achieved AI Search Visibility for Level AI

Prioritized GEO is based on the fact that LLMs almost always search the web to help formulate responses when users ask for product recommendations. This process is known as grounding. Their training data alone is not always up to date or sufficient for these prompts, so they use search to supplement it. 

So, if we want LLMs to recommend Level AI when someone asks for product recommendations in their space, traditional search is the most efficient way to make that happen. For example, if a user asks ChatGPT for voice of customer platforms, it will search the web — and if our article discussing Level AI appears in those results, it significantly increases the chances of being recommended in the response. 

Learn more about Prioritized GEO.

What Keywords We Rank for and Where We Stand in LLMs

Let’s look at a few examples of high buying-intent keywords that are important for Level AI, the content we published to rank for each, where we rank, and the AI prompts we appear in.

Example #1: AI call center monitoring

One of the first keywords we targeted for Level AI was “AI call center monitoring,” and we now rank on the first page for that term.

Our AI visibility tool, Traqer, shows that we consistently appear in the top 5 positions in LLMs when users ask about AI call monitoring solutions. 

Note how Traqer asks about the topic in multiple ways. This is because AI search is not as reproducible as traditional SEO — meaning even slight variations in prompt wording produce different results, and even the same prompt asked at different times can yield different results. 

Traqer accounts for this by asking about a single topic in multiple different ways. We explain this in detail in Topic-Based GEO. 

Side note: We’ve noticed across dozens of brands that visibility on Perplexity and Google AIO often increases before ChatGPT. We hypothesize this is because Perplexity and Google AIO are more heavily influenced by traditional web search results. They function more like search summarizers, so traditional SEO rankings have a greater impact on their output. ChatGPT, on the other hand, blends more of its training data into its responses, which naturally biases it toward larger, more well-known brands. 

Beyond brand mentions of Level AI, we also see LLMs using our article as a source in responses to these prompts.

Here’s an example of Google citing our article and listing Level AI as a solution in its AI Overview for “AI call monitoring solutions.”

Example #2: Call center real time reporting

Another relevant topic for Level AI is “call center real time reporting” because it’s one of the few QA tools that analyzes customer conversations in real time, not after the fact.

Ahrefs shows we rank first for “call center real time reporting” on Google. 

Meanwhile, Traqer shows that we have good AI search visibility across ChatGPT, Perplexity, and Google AIO when someone asks for software suggestions for real-time call center reporting.

We also see that LLMs cite our article more than any other source for topics related to “call center real time reporting.” In fact, across the 6 prompts above, Google AIO, Perplexity, and ChatGPT cite our article a combined 15 times, almost twice as often as any other article. 

This is how you influence LLM responses: create content they discover through web search and use to generate their answers. 

Here’s Google listing Level AI as a popular solution in this space and citing our article first in its AI Overview:

Example #3: Call center analytics software

Another broad topic area where Level AI has a strong value proposition is “call center analytics software.” It’s a popular product category, and appearing in those discussions has long-term value for Level AI.  

So, we wrote an article targeting that keyword, which at the time of writing ranks first on Google.

In Traqer, we see strong visibility for prompts related to call center analytics software on ChatGPT, Perplexity, and Google AIO.

Perplexity recommends Level AI when it’s prompted with questions asking for call center analytics software options, and cites Level AI as one of the main sources it uses to formulate responses.

Example #4: Call center quality assurance tools

The fourth keyword we’d like to cover is “call center quality assurance tools,” where we rank third on Google.

In Traqer, Level AI has strong visibility on ChatGPT, Perplexity, and Google AIO when users ask about call center QA tools.

Although we’re not the top-cited article, LLMs consistently reference us across multiple prompts related to that topic.

We can’t cover every keyword we’ve targeted for Level AI, but these 4 examples demonstrate a clear pattern: ranking well for a keyword on Google gives us AI search visibility for prompts related to that keyword. Keywords we aren’t yet ranking for have little to no visibility in LLMs.

All in all, we’ve targeted 50+ keywords for Level AI so far, resulting in visibility in LLMs for 100+ related high buying-intent prompts.

Examples of AI Using Our Content to Respond to Prompts

Another core principle of our AI search visibility strategy is that the level of detail in your public content helps dictate how LLMs talk about your products and services (full article). 

This means LLMs act like your first salesperson — summarizing your product’s differentiators, value propositions, and arguments, and using them to educate users on what makes you different from competitors and why they should choose you.

As a result, brands need to produce thorough, detailed content that articulates their solution in extreme detail: who it’s for, use cases, features and benefits, pain points it solves, and differentiators versus competing solutions. This way, you feed LLMs your exact value propositions and differentiators so they can make a compelling case for your solution. 

If you publish generic content that echoes what others are already saying, even ranking in LLMs won’t help because it will describe you the same way it describes everyone else, making readers far less likely to convert.

The differentiators and value propositions we focused on for Level AI were:

• Level AI uses semantic intelligence to understand the full context of conversations, making its results more accurate than many alternatives. Most software simply looks for keywords.
• Level AI can detect emotions in customer conversations. Most call center QA software struggles with this.
• Level AI can auto-score all conversations based on the client’s QA rubrics. Teams don’t have to listen to hours of customer conversations and score agents manually.
• Level AI’s deep analysis of conversations uncovers hidden issues and revenue opportunities without surveys, sampling bias, or manual analysis.

As a result, we’d like to share screenshots of LLMs using almost the exact same text in their responses as we use in our articles.

When we ask ChatGPT for accurate AQM software, it emphasizes that Level AI doesn’t just look for keywords but can understand the full context of customer interactions. 

This is known as semantic understanding.

Semantic understanding is the main value proposition we focus on in our articles.

Here, we can see how ChatGPT says Level AI can detect, label, and filter specific emotions like anger, disappointment, worry, and happiness:

We use almost the exact same phrase in our articles:

These are just a few examples, but generally, when ChatGPT recommends Level AI for customer intelligence solutions, it mirrors the same language, value propositions, and differentiators we use in our content.

What This Means for Your Brand: 2 Key Takeaways

1. Target high buying-intent keywords and prompts your target audience is likely to enter into LLMs. LLMs search the web when users ask for product recommendations, so if you rank well for a keyword in Google search, they’re more likely to recommend your brand when users enter prompts related to that keyword.

2. Write highly specific, differentiated content. This gives LLMs the context to position your product or service clearly. Avoid publishing generic content, or your LLM pitch for your brand will sound the same as everyone else’s. Learn more about writing specific, high-quality content.

How to Take This Strategy Further in Your Own Organization

Ranking for high buying-intent keywords with highly differentiated content should give you a strong foundation for LLM search visibility and SEO rankings.

The next step in our Prioritized GEO strategy is Tier 2: citation outreach. This involves identifying the sources AI systems reference most for a given prompt or topic (which you can do in Traqer) and reaching out to those websites to request a mention of your brand. Sites like G2 or Capterra generally require payment, while competitors are often willing to trade mentions.

This works through the same underlying logic as Tier 1: owned content. When LLMs search the web to respond to user prompts, they see your brand mentioned across multiple sources (not just your own website), increasing the likelihood that they mention you more prominently.

That said, there are two mistakes to avoid when doing citation outreach:

1. ​Asking any website in your industry for mentions: We recommend being strategic and focusing on earning mentions in articles that LLMs are already citing.

2. Not controlling the narrative on other websites: We strongly advise writing the product snippet yourself and being specific about your value propositions and differentiators. This way, your messaging is consistent across sources, giving LLMs the information needed to position your solution in a clear and differentiated way.

Increase Your Brand’s LLM Visibility with Grow and Convert

If you’d like us to improve your brand’s LLM visibility and drive conversions, get in touch for a quick call.

In the marketing community, there’s a growing belief that if you rank for these fan-out queries, you’ll consistently get cited by LLMs.

But we wanted to verify and quantify this: How much overlap is there between fan-out query rankings and the sources ChatGPT actually cites? Do the sources cited in a ChatGPT response rank on Google for the fan-out queries? If so, how highly do they rank?

We focused on ChatGPT because, across dozens of clients, it drives the most referred traffic. 

We looked at 100 different buying-intent prompts, each generating 2-4 fan-out queries, and compared the sources ChatGPT cited against the Google and Bing search results for each fan-out query to see how much overlap there actually was.

In total, only 27% of cited sources ranked on Google for the fan-out queries used by ChatGPT. Bing was slightly lower at 23%. Accounting for the ~10% overlap between Google and Bing results, only about 40% of cited sources appeared somewhere in the first 10 pages of either search engine for those queries. 

That means, on average, more than half (60%) of cited sources in ChatGPT answers don’t rank in the first 10 pages of Google or Bing for the fan-out queries.

We also observed a wide range: for some prompts, there was 100% overlap between citations and fan-out query rankings. For others, none of the cited sources ranked within the first 10 pages for those queries.

This data doesn’t invalidate the connection between traditional search rankings and AI search visibility. According to our study, ChatGPT still searches the web for 80%+ of product-related prompts. And as we’ve written about before, these bottom-of-funnel prompts are where AI visibility matters most. 

But it adds to a growing truth about AI search: LLMs are fundamentally less predictable and less “hackable” than traditional SEO.

More specifically, trying to produce single pieces of content to “rank” for specific AI search prompts, in this case by targeting fan-out queries, doesn’t make sense for AI search. It’s fuzzier and less direct than that.

As a result, brands are better off doing authentic marketing — including publishing extremely detailed and thorough content about their products, use cases, features, and benefits — rather than trying to “hack” their way into ChatGPT by targeting specific fan out queries, or as we’ve talked about in previous articles, with tactics like adding FAQ pages, bullet point summaries, or llms.txt files.

You don’t know the true prompts your users are entering. And even if you did, this study shows that targeting fan-out queries gets you cited less than half of the time. So instead of a content strategy aimed at specific prompts, focusing on product-centric content that covers relevant topic areas makes more sense.

Below, we’ll explore these implications further, along with the obvious next question: if only 40% of citations come from the fan-out query SERPs, where does ChatGPT get the remaining 60%?

A Note on Methodology

If you’ve been following us, you’ll know that we believe bottom-of-funnel, product-centric prompts are the only ones worth worrying about for AI search. Top-of-funnel queries are typically answered by LLMs without mentioning or linking to brands or websites. 

All 100 prompts we tested had buying intent (e.g., “best help desk software for small teams,” “top accounts payable automation tools,” or “best standing desk for home office”). The prompts covered B2B, B2C, software, services, and physical products. 

We used the browser UI, not the API, so the data would reflect what a real user sees. 

Of the 100 prompts, 83 triggered a web search, meaning 17% of the time ChatGPT answered without searching the web at all. When a web search was triggered, each prompt generated 2–4 fan-out queries.

To pull Google’s SERPs for the fan-out queries, we used SerpAPI. For Bing, we ran the searches manually because the API returned unreliable results, so the dataset for Bing is smaller.

Finally, we compared ChatGPT’s cited sources against Google and Bing SERPs at the prompt level, by category, and by domain match (vs exact URL match). 

We also analyzed the sources ChatGPT cited that didn’t appear in Google or Bing SERPs to see what we could learn about where those citations come from — and your chances of being one of them.

The Overlap Between ChatGPT Sources and Google’s SERP Varies Wildly from Prompt to Prompt

20 of the prompts had zero cited sources found in the fan-out SERPs — despite the fact that ChatGPT chose to search the web. Only one prompt out of the 100 we tested had 100% overlap. 

A logical follow up question is, are we seeing this wide range of overlap between cited sources and the SERP because ChatGPT relies more heavily on search for some types of prompts vs others (where its training data is enough)? 

So we also grouped them by business type:

While there is some variability, there’s less variation across categories than you might expect. Physical products had the lowest overlap, which is what we’d expect since ChatGPT is likely tapping into features like Google Shopping that weren’t considered in this study.

B2C SaaS also has a weaker overlap between cited sources and the SERP, but, again, this is to be expected because these types of products are typically pulled from app stores rather than their website directly.

This tells us that the variability comes more from how it weighs search results vs training data rather than from what the user is looking to buy. 

We Went 10 Pages Deep, But ChatGPT Doesn’t for Most of Its Sources

Of the sources in our study that did appear in Google’s results for the fan-out queries, page 1 alone accounted for a third of all matches. The top three pages of the SERP contained ~66% of the matches, with a significant decline after that.

This shows what SEOs (including us) have observed and frankly, wished for, which is that ranking higher improves your chances of getting cited.   

But the next important question is if ChatGPT is only pulling from the live web about 40% of the time on average, where is it getting the rest of the sources?

Where Does ChatGPT Get the Other 60% of Sources It Cites?

At the end of the day, where ChatGPT is pulling its sources from is a black box. But we do have a few theories based on what we observed in the data.

ChatGPT May be Running Additional Queries Behind the Scenes

We can only see the fan-out queries that run in our browser. It’s likely, however,  that ChatGPT is running additional searches on OpenAI’s servers that we never see. 

As QueryBurst has documented, it would make sense for OpenAI to distribute searches across multiple servers simultaneously — using different search engines, running different query variations, etc. If it did all of this sequentially in the browser, responses would be painfully slow.

Response Caching

ChatGPT may also be remembering or caching results from past users’ queries to save resources. OpenAI offers developers discounts if they used cache responses in their builds, so it makes sense that they would be using this approach too. If millions of users are asking similar buying-intent questions, it would be cheaper and faster to cache frequently retrieved sources rather than running fresh web searches every time.

Training Data

There’s evidence that ChatGPT retains specific details and facts verbatim from its training data. Research from NeurIPS has demonstrated that large language models can memorize and reproduce training data verbatim. If ChatGPT can remember exact text snippets, it’s conceivable it could also remember exact URLs.

In fact, we found several cases in our data where ChatGPT cited a real, live URL but gave it an outdated title — one that no longer matches the current page. For example:

In another example, ChatGPT cited the same exact URL twice but with different titles, in the same source list. 

Here are the two titles ChatGPT showed: 

The best video conferencing software in 2026 | Zapier

The best video conferencing software in 2025 | Zapier

And, here’s the title that appeared in Google’s SERP for the fan-out query which is also the actual title of the article as of this writing: 

The best video conferencing software for teams in 2026

One explanation for both cases is that ChatGPT is pulling the URL and its old title from training data, not from a live web search. In the second example, one explanation for why it appeared twice is that  it may have pulled the URL from its training data and from a live web search. 

Caching doesn’t explain this either, since OpenAI’s own documentation shows caches typically last 5–10 minutes, and sometimes up to an hour — not months or years, which is the gap between these outdated titles and the current ones.

Hallucinations

In our dataset, we found roughly 10% of citations led to error pages. Recent academic research puts the number higher. A February 2026 study found hallucination rates above 14% for cited sources, and an earlier 2023 study published in Nature documented similar patterns. These are citations that look real — plausible URLs with plausible titles — but point to pages that don’t exist.

So where does all of this leave us? 

GEO ≠ SEO…

The core takeaway is that you can’t use the same mindset or strategies for GEO or AIO that you do in traditional SEO. In SEO, we’re used to the repeatable and predictable idea of “you write a relevant, helpful, quality article targeting keyword X, get your on page SEO right, and build backlinks, and rank for X”. But, LLM answers are way too unpredictable for this same approach to be useful. 

SparkToro’s recent research reinforces this. They found that the brand names that LLMs recommend are wildly inconsistent. Different users can enter the same exact prompt and not only get a different list of recommendations, but also get those recommendations in different orders. 

Add to that the fact that in AI tools, unlike traditional Google, users ask essentially the same question in wildly different ways and the LLMs factor in a ton of deeply personal context with every users’ prompt, and you see how GEO is less controllable and predictable than SEO.

On top of that, you get different fan-out queries for the same exact prompt. The LLM won’t always search the web even for the same prompts. If it does search the web, the amount it relies on that web search versus training data can differ wildly. Also, the model’s capabilities change based on whether the user is paying or using a free account. I could go on, but you get the point.  

And, all of that unpredictability only accounts for roughly 40% of the cited sources. 

That is why a content strategy built around trying to rank for specific queries in order to be cited is impractical and ultimately will be ineffective.

… But SEO is Necessary for GEO

While the nebulous nature of LLMs is uncomfortable for most marketers and business owners, there are patterns to how LLMs operate and actions you can take to get mentioned more often by LLMs. 

Be Available for Training Data

OpenAI admits to using web crawlers both for live web search and for training data, which means you have a chance of getting into ChatGPT’s training data just by putting content on your website. (More on what this content should be below. Hint: It’s not just “easy to read content with digestible chunks”.)

Increase Your Topical Authority

In the SparkToro piece we mentioned earlier, they mention that certain brands are mentioned repeatedly across responses.The top 3 most mentioned brands were cited 64% of the time for the same prompt on ChatGPT.

And our own data suggests ChatGPT may be recognizing that certain domains have more topical authority than others.

If we count all sources that had some page on their domain rank for a fan-out query — instead of requiring the exact URL to match — the percentage of cited sources appearing in Google’s SERPs jumps from 27% to about 50%.

This suggests that there’s some correlation between what ChatGPT and Google consider to be an authoritative site. And, ChatGPT may be favoring domains it recognizes as topically authoritative, even when it doesn’t cite the exact page that ranks. Your domain’s broader presence across a topic may matter more than whether one specific page ranks for one specific query.

One of the most effective ways to build that kind of topical authority is through SEO. Not the “optimize your meta tags and add FAQ schema” kind of SEO, but the kind where you’re consistently publishing detailed, substantive content that addresses the real pain points your customers have. The more comprehensively your domain covers a topic area, the more likely ChatGPT is to recognize you as a relevant source when someone asks about that topic, regardless of which specific fan-out query it runs.

78% of Cited Sources Were Typical SEO-Style Content, Whether or Not They Ranked

One of the big questions floating around marketing circles right now in various forms is whether or not content type needs to change. Do LLM’s even cite traditional marketing or SEO style blog posts such as list posts? And, important for this study, if you produce a traditional style SEO post like a list post but don’t rank yet for your target keyword (or related keywords), do you still have a shot at being cited by ChatGPT for typically similar prompts? Our data says, yes.

For example, here’s a post by Calendly that was ranking for the fanout query “tools for scheduling meetings with clients without back and forth emails scheduling tool”.

It looks very much like a typical SEO-style post that we would write for that keyword. And they put themselves first on the list, which we always recommend. 

In fact, 78% of all cited sources for these high buying intent keywords were listicles, product pages, homepages, pricing pages, etc — essentially webpages selling your products/services, i.e., typical marketing or SEO content.

While this makes sense for the 40% of sources that were in the fan-out query SERPs, what may surprise some marketers is that it was also true of the 60% of sources not found in Google or Bing’s SERPs. The number only went down slightly to ~74%.

Said another way, even the majority of content cited by ChatGPT that were not ranking for fan-out queries looked and felt like content you would publish to rank for those queries: list posts, landing pages, etc.

This is aligned with the theories discussed above of where else ChatGPT could get these citations from, if not from the fan-out queries: caching past searches, or finding similar content by doing web searches when it was last trained. 

This reinforces what we’ve been saying: the best way to influence LLM responses is to produce bottom-of-funnel, product centric content aimed to rank for similar queries on traditional google search. 

What we can add to that understanding with this data is that you don’t need to target and rank for fan-out queries that you can’t even truly find. Instead, creating typical SEO content is the best way to be available for training data and build topical authority, which, again, are your best bets for showing up in LLM answers. 

So, focus on building domain-level topical authority over time. What we recommend and do for clients is to identify your strengths and find Google keywords that map to those strengths. Then, write helpful, relevant, detailed content for those keywords. 

That’s an actual content strategy.

How this Looks for a Real Client, Toro TMS

Toro TMS is a good example of how publishing content builds AI search visibility. They’re a relatively young brand  that hadn’t done much content marketing before we started working with them. Once we began producing product-centric content targeting relevant Google keywords, their ChatGPT visibility followed.

They published thorough, detailed content about their product and the problems it solves, targeted at real Google keywords where their product is a genuine answer. The AI visibility came as a byproduct of high-quality content marketing.

Traditional SEO is predictable. You pick a keyword, create content around it, build links, and over time, you rank. You know which keywords have volume. You know which pages rank. You can track exactly where you stand. It’s measurable and repeatable.

AI search is different. There’s uncertainty at every step. 

1. Prompts: You don’t know exactly what prompts users are entering, because users don’t type short, repeatable keywords. They have long conversations unique to them. 
   
2. Context: Even if you do know the prompts users are entering, you or your AI visibility tool isn’t going to get the same answer as a real user because LLMs factor in a massive amount of user history, memory, and context to generate a response that’s personalized to that specific person. (Read more: Invisible Prompts).
   
3. Repeatability: Even the same user, asking the same prompt repeatedly, will get different responses and citations each time.

So what do you do with all this uncertainty? How do you get those LLMs to recommend your brand and your product when users ask about solutions to their problems in your space? 

The answer relies on this key fact: LLMs still run on content. 

How Search Changes from SEO to AI Search

Here’s the key difference between traditional SEO and AI search.

In SEO, everyone searches with short keywords and largely sees the same results. Yes, some personalization is done based on your browsing history and location, but it’s mostly around the edges.

For example, say we both search for “project management software.” We’ll likely get similar results: 

Seems normal, right? 

But what if you’re at an enterprise company switching from Jira and you want something very specific, and I’m a small business owner currently using pen and paper looking for my first real project management tool? Those details absolutely affect what project management software makes sense for you versus me, but Google doesn’t have any idea. So it serves us the same ten blue links. 

The way we used to deal with this in SEO is going after a few longer tail variants when they exist, like “enterprise project management software” or “project management software for small business.” But even that is insufficient. That doesn’t capture the nuance I mentioned above: that you’re already on Jira and want something specific that Jira doesn’t offer, and that I’m looking for my very first project management software. 

Those are just two random examples. There are really an infinite number of possible specific requests and pain points for a customer looking for any solution. Traditional Google search simply didn’t have a good way of handling all this personal context. 

But AI search does. 

In AI search, each user has a unique, personal conversation with the LLM where they share a massive amount of context. The LLM understands their company size, existing tools, and highly specific pain points. This context comes from both the current thread and a large body of prior chat history, enabling the model to recommend products tailored to their exact situation.

Here is Benji asking Claude about the best project management software “for us.” Look at how Claude knows what our company is, how big, and more. Benji didn’t type any of that into that prompt, but it knows. We call this phenomenon Invisible Prompts. 

LLMs aren’t just surfacing articles from high-authority domains.

They understand what a user needs, search the web for relevant solutions, read those results, combine that with what they already know, and recommend the product or service that best fits the user’s specific pain points. 

That’s a fundamentally different dynamic. And it changes the kind of content you need to produce.

If you want LLMs to recommend your product, you need to give them the details they need to recommend you in the right scenarios. Not generic “what is project management” content. 

You need to publish specific content that covers:

• What types of customers your product is best for
• What specific use cases and scenarios it handles
• What pain points it solves and for whom
• The features that matter and when they matter
• The benefits and outcomes customers experience
• How you compare to competitors and what makes you different
• Case studies that prove you’ve delivered results

Introducing Your GEO Topic Map

Your GEO topic map is a collection of content that teaches LLMs everything they need to know about your product so they can recommend you in the right conversations.

Here’s how we build it for clients:

1. We map every product angle that matters. Your categories. Your competitors. Your use cases. Your target personas. The jobs your product does. The pain points it solves. This becomes your topic map: the full universe of conversations where an LLM could potentially recommend you. Most companies aren’t thinking this way, much less producing this content. 

2. We interview the people who know your customers best. We sit down with your sales team, your customer support team, your product team, your founders, all of the customer facing roles inside your organization. We ask them exactly what users are telling them are their pain points and use cases. Then we extract the specific positioning, features, and differentiators that make your product the right choice in each scenario. These details are not something you can get from Google research. AI generated copy will not by default produce this. The details required to produce this kind of content have to come from the people inside your company who talk to customers every day.

3. We produce content for each angle at the depth of a sales conversation, not an intro guide. There’s this long standing culture in content marketing where everything is written at an introductory level.  “Intro guide to this.” “Beginners guide to that.” That came from everyone chasing high volume SEO keywords that were by nature beginner-leaning. 

But beginner level content won’t help you when your customer asks advanced and specific questions to an LLM. It wants to recommend brands that talk at that customer’s level. So your content needs to meet the customer there. Think about the level of depth and detail that happens in a sales conversation: detailed discussion of features, screenshots, specific scenarios where your product applies, use cases, case studies. That’s how we write this content.

4. We target Google keywords if and when it makes sense. If some of the user pain points we uncover in our interviews with your team match a known Google search term, we’ll target those search terms with our content so you get two inbound channels (traditional search and AI search) in one go.

For most businesses, there are plenty of these product recommendation keywords (what we call bottom of funnel queries) on Google. Keywords like: “Best [category] software,” “[competitor] alternatives,” “how to [solve specific problem].” 

5. We produce case studies. LLMs are smart enough to connect the dots between a story of your product helping someone and their user who is asking them about a similar issue. So it’s our experience and belief that case studies help. They naturally have the elements of specificity and pain point/solution pair elaboration that we’re talking about here. 

6. We track your AI visibility at the topic level. To make this process work, you need to measure success the right way. That means tracking visibility at the topic level, not the individual prompt level. Unfortunately most AI visibility tools (like Peec, Profound, Scrunch) are default to measuring success on an individual prompt basis. But we built our tool, Traqer.ai to be consistent with this topic-based property of GEO. That’s how we track improvement in visibility for clients and ourselves: which topics are we visible in and how is that growing.

Case Study: Constitution Lending

Constitution Lending is a private lender competing against companies with 80+ domain authority, thousands of backlinks, and decades of industry presence.

We built their topic map by interviewing their lending experts, the people who talk to borrowers every day, and producing content covering every specific lending scenario their ideal customers face: LLC mortgages, DSCR loans for specific property types, bridge loans for particular situations.

The result: Constitution Lending now appears in AI search results for 50+ bottom-of-funnel prompts across ChatGPT, Perplexity, and Google AI Overviews — competing with and often outranking lenders that have been in the market for decades.

This didn’t happen because of an llms.txt file or restructured headings (these on-site hacks don’t help LLMs connect your brand to users’ pain points, read more why here). It happened because LLMs found detailed, specific content about Constitution Lending’s products and could match it to user queries.

[Read the full case study →]

Case Study: InnovationCast

InnovationCast makes innovation management software for enterprise companies. 

Innovation software has multiple use cases, from crowdsourcing ideas from a large employee base, to validating ideas, to scouting new technologies.

When we started working with them, they had a limited content and SEO presence. But by producing content in their various topic areas, we’ve built not only an SEO presence that generates qualified leads for them, but also AI visibility

For example, here is their topic based coverage for “innovation portfolio management software”:

And here it is for another, slightly different way customers talk about software like theirs: “technology scouting software”:

Next Steps

If you’d like to talk to us about building your brand’s topic-based GEO strategy, you can reach out here.

If you’d like to read more about our GEO experience, observations and research here is further reading that underpins this Topic-Based GEO approach: 

• Ultra-Specific Content: Why top-of-funnel content doesn’t matter for AI search and why ultra-specific content is necessary
• Prioritized GEO: Why on-page hacks like llms.txt don’t work and why content and web search is the foundation of GEO
• Invisible Prompts: Why you can’t see the full context behind users prompts 

If you’d like more articles like this as we write them, you can join our newsletter. 

Here’s an example: company leadership asks marketing, “How are we doing on GEO/AEO/AIO?” Marketing turns to a tool like Peec or Profound, which spits out a single brand-visibility percentage, usually shown in a graph. Marketing reports back to leadership, “We have 26% visibility!” 

If that number is higher than last week, everyone’s happy and thinks marketing is “doing their job.” If it’s lower than before, leadership assumes something is wrong and wants marketing to change what they’re doing.

Below, we argue that this approach doesn’t make sense because it’s fundamentally the wrong way to think about GEO performance. 

A brand’s “AI search visibility” isn’t a brand-level metric. It’s a function of the specific topics or prompts being measured.

Reporting visibility at the brand level—especially as a single percentage—is, at best, unhelpful and, at worst, outright misleading. 

As we explain below, this mindset can lead to several negative outcomes, including: 

• Manipulation of metrics to make GEO performance appear stronger than it actually is
• A focus on “ranking” for prompts that have little to no business value
• Marketing teams being disincentivized to push harder or pursue more ambitious GEO strategies
• Resources wasted on activities that have no real impact on lead generation or revenue

Visibility Happens at the Topic Level, Not the Brand Level

If you remove the AI visibility tools and their dashboards from the picture, the idea of a single “AI visibility score” falls apart. In the real world, no brand has one. 

AI search visibility isn’t like domain authority in SEO, which represents a real, meaningful concept: how much weight Google’s algorithm assigns to a domain when deciding what to rank. In SEO, a site with high domain authority will, on average, outrank a site with low domain authority, all else being equal. 

There’s no real-world equivalent to a single AI search visibility percentage. Brands have visibility—or don’t—within specific topics. And if a brand has strong visibility in one topic, that visibility isn’t correlated, based on any data we have seen to date, with its visibility in another. 

By its very nature, AI search visibility is a topic-specific concept. 

Topic Level Visibility Is Actionable

More practically, we believe viewing visibility at the topic-by-topic level is how marketing teams learn which actions actually improve AI search visibility. When teams rely on a single, overall visibility percentage instead, those details get buried and the signal becomes misleading.

Here’s a real example from one of our clients, Mirascope, which has strong visibility on the topic of “prompt engineering tools”:

By contrast, on the topic of “context engineering platform,” they have far less visibility:

What does averaging these two together tell the Mirascope team? Nothing useful.

Leadership should think about visibility in these two topic areas completely separately. The actions required to improve visibility for “context engineering platform”—publishing more content on the topic, clearly explaining how Mirascope supports context engineering, and conducting citation outreach on relevant articles—will have no impact on visibility for “prompt engineering tools.”

The SEO analogy applies here. If Mirascope ranked #1 for the keyword “prompt engineering tools” and #60 for “context engineering platform,” what use would it be to average the two and report on a blended rank? No one does this in SEO because it makes no sense. 

Yet this is exactly what happens in GEO, largely because popular AI search visibility tools default to showing a single, overall brand visibility percentage in their dashboards. 

Single Visibility Percentages Create the Wrong Incentives

A single AI visibility percentage often becomes misleading (at best) or downright manipulated (at worst).

Consider what happens when a marketing team starts tracking additional prompts—aspirational topics they aren’t yet visible for but want to be. Adding those prompts, by definition, drives the overall visibility percentage down. 

On the surface, it looks like AI search visibility declined. Leadership may even comment on it or assume marketing isn’t doing their job well. But nothing bad actually happened. Visibility across ChatGPT and other LLMs is exactly the same as it was the day before. The only change is that the team is tracking more topics and being more ambitious about its GEO goals.

Here’s a concrete example. On January 13, the visibility score in Peec for this brand was 26.7%: 

On January 14, we added two new prompts that were relevant to the brand but had no visibility. As a result, the visibility score dropped to 20%: 

In reality, the brand lost no visibility. The metric only looks worse because new prompts were added.

This creates terrible incentives. It encourages marketing teams to be less ambitious. Teams are incentivized to avoid tracking new prompts because doing so makes their visibility percentage look worse. 

That’s the exact opposite of good marketing. Teams should be encouraged to identify new, high-value topics they want to rank for—not discouraged from tracking them.

How Our Visibility Tool Traqer Solves This

This is exactly why we built Traqer to be topic-based.

Topic-level visibility sits front and center in our tool, and that’s how we report on GEO visibility to our clients.

This page is what marketing teams can share directly with executives or clients. At a glance, everyone sees visibility by topic and can quickly understand where their brand is performing well and where it isn’t.

In the Mirascope example above, it’s immediately clear that they perform well for “prompt engineering tools” but have work to do for “context engineering platform.” No one has to dig through reports or click elsewhere to find this information. 

Yes, Traqer also includes brand-level metrics at the top. But instead of collapsing everything into a single percentage, those metrics show the total number of topics where visibility is low, medium, or high. This distinction matters. Adding new topics doesn’t artificially reduce performance elsewhere. 

This approach does two important things:

• It aligns incentives correctly. You can add 10, 50, or 100 new topics or prompts without hurting your overall score.
• It keeps teams focused on what’s actionable: which topics already have strong visibility and which ones need work.

The Right Way to Think About GEO Visibility

Regardless of whether you use Traqer, another tool, or track visibility manually in a spreadsheet, our recommendation is to think about GEO visibility at the topic level. That’s the proper way to approach GEO strategy.

Overall brand visibility percentages are, in our opinion, a lazy way to assess GEO performance. They hide the details that should (1) guide your strategy and (2) show your team whether GEO efforts are actually working.

When you track topic-level visibility, you can:

• Identify which topics need immediate attention
• Allocate content creation resources to the right areas
• Measure progress on specific topics and initiatives
• Avoid false signals from percentage fluctuations
• Make data-driven decisions about where to focus your GEO efforts

It’s the difference between knowing you have “32% visibility” and knowing you dominate prompt engineering conversations but need work on context engineering topics.

One tells you nothing. The other tells you exactly where to focus next.

Further Reading

• You can read the full Prioritized GEO strategy we execute for clients.
• You can read more GEO strategy articles.
• You can check out Traqer for proper AI visibility tracking.
• You can reach out about working with us to improve your GEO.

One pattern we’ve seen consistently is that enterprise companies come to us after hiring an agency that technically did content marketing, published regularly, grew traffic, and hit their deliverable counts, but never generated pipeline. The content looks fine on the surface, ranks for keywords, and drives traffic, but rarely converts enterprise buyers into demos or sales opportunities.

In our experience, that failure comes down to a few recurring problems.

• Most agencies treat enterprise content marketing as a volume problem rather than a strategy problem rooted in how enterprise buyers actually make purchasing decisions.
• Most enterprise content marketing services target high-volume, top of the funnel keywords that attract early-stage researchers, not the active buyers who are already evaluating vendors. This has always been a flawed approach for pipeline generation, and it’s getting worse as AI tools absorb informational queries that used to drive organic search traffic.
• Most agencies don’t have a process for identifying genuine product expertise, so the content they produce is too generic and doesn’t really tell enterprise buyers how it could solve their pain points.
• Most enterprise content teams have no visibility into whether their brand appears in AI-generated responses when buyers are researching vendors on tools like ChatGPT or Perplexity, and most agencies they’ve hired don’t have a strategy for that either.

What’s behind all of this is a distinction we think matters a lot when evaluating agencies at the enterprise level. There are two fundamentally different things an enterprise business might need from a content marketing agency:

• Managing a content program at enterprise scale, with complex approval workflows, brand governance, compliance requirements, and global operations.
• Creating content that actually converts enterprise buyers who are making six or seven-figure purchasing decisions with long sales cycles and multiple decision-makers involved.

Most enterprise content marketing agencies are built for the first, but most enterprise marketing teams actually need the second, or some combination of both.

In this article, we’ll walk through the key factors enterprise marketing teams should use to evaluate content agencies, explain how we address each at Grow and Convert, and share profiles of the agencies whose names come up most often in this space.

Grow and Convert is an SEO and GEO-focused content marketing agency with experience working with several enterprise companies. If you want to hire an agency to handle your enterprise content marketing, you can learn more about working with us here. If you’d like to learn how to do this yourself, check out our content marketing course and community here.

Factors to consider when evaluating enterprise content marketing agencies

We’ve worked with enough enterprise companies to have a clear sense of what separates agencies that generate real pipeline from those that produce content without much to show for it. 

Below are the four factors we’d focus on when evaluating enterprise content marketing agencies.

Factor 1: Is their strategy built to generate pipeline, not just traffic?

The number one factor to evaluate in any content marketing agency is whether their strategy is designed to drive their company’s bottom line, i.e., generate leads and pipeline, or to just grow traffic. Most agencies, including those that market themselves to enterprise companies, usually focus on traffic growth. They target keywords with high search volume, report on sessions and rankings, and treat pipeline contribution as something that’s hard to measure as a vendor in an enterprise setting, and therefore not their responsibility.

Enterprise content marketing has a specific version of this problem. Because enterprise deals are large and sales cycles are long, there’s a temptation to justify content investment through brand awareness and thought leadership metrics rather than direct pipeline influence. Most agencies are happy to operate that way because it removes measurement pressure from them.

The agencies that actually drive enterprise pipeline start with the keywords and topics that enterprise buyers search (in both Google and with LLMs) when they’re already evaluating solutions, not when they’re trying to understand a problem category for the first time. 

In our research across dozens of B2B clients, bottom of the funnel content converts at 4.78% compared to 0.19% for top-of-funnel content. For enterprise companies where a single converted account can represent hundreds of thousands of dollars in revenue, that difference matters enormously.

When evaluating agencies on this factor, ask them to show you a client’s content strategy and explain how each piece connects to the pipeline. Ask what metrics they report on each month. If the answer centers on traffic, rankings, or domain authority rather than demos or sales opportunities sourced from content, that’s a meaningful signal about how they measure success.

Factor 2: Do they have a process for capturing genuine product expertise?

Enterprise buyers are sophisticated practitioners. A VP of Finance evaluating spend management software, or a CISO evaluating a security platform, can immediately tell whether a piece of content was written by someone who understands their problem at a practitioner level or by a writer who spent a few hours researching the topic online or used AI to produce generic copy.

Most agencies produce what we call “Google research paper” content, either on their own or with AI help. A writer is handed a keyword and assembles an article by just Googling what already ranks for that term. The output is generic, tells the reader nothing they couldn’t find on other websites, and gives enterprise buyers no reason to trust the vendor behind it. AI writing has only exacerbated this problem, with writers asking ChatGPT to just produce a piece on this topic, which, by definition, is AI just repeating what others have already said on that topic. 

In contrast, at Grow and Convert, we have an interview-based writing process, where writers conduct detailed conversations with product managers, engineers, sales leaders, and customer success teams before writing anything. Those interviews help the writers surface the specific insights, differentiators, and customer pain points that competitors cannot replicate because they require insider knowledge. For enterprise companies with complex products and sophisticated buyers, this is the content writing process that has consistently produced results for our clients.

When evaluating agencies on this factor, ask them to walk you through exactly how their writers develop domain expertise on a client’s product. If the answer involves content briefs and keyword research but no direct interviews with subject matter experts, the content they produce will reflect that gap.

Factor 3: Do they have an AI search strategy for enterprise visibility?

Enterprise buyers increasingly use AI tools like ChatGPT, Perplexity, and Claude to research vendors and build shortlists before ever talking to a sales team. If your brand isn’t appearing in AI-generated responses when buyers search for solutions in your category, you’re being filtered out of consideration before the conversation starts.

Most content marketing agencies have not built a structured methodology for AI search visibility. They may list GEO or LLM optimization as a service, but what they’re actually doing is applying tactics, like optimizing a few pages, adding FAQ schema, or pursuing mentions on third-party sites like Reddit, without a coherent model for how AI systems retrieve and cite sources or how to systematically improve visibility across an entire content program.

Our research found a 72% correlation between ranking on the first page of Google and being cited by AI tools like ChatGPT and Perplexity, which increases to 77% for the top three positions. Owned content ranking in traditional search is still the foundation of AI visibility, but there are additional layers around off-site brand mentions, third-party citations, and on-site content structure that influence whether AI tools recommend your brand when enterprise buyers ask relevant questions.

What makes AI search meaningfully different from traditional SEO is that enterprise buyers aren’t typing short, repeatable keywords into Google. They’re having detailed, personalized conversations with AI tools that factor in their company size, existing tools, and specific pain points, and getting recommendations tailored to their exact situation. That means generic, high-volume informational content doesn’t give AI systems enough to work with when matching your product to a specific buyer’s situation.

When evaluating agencies on this factor, ask them to describe their specific framework for improving AI search visibility, not just whether they offer it as a service. Ask how they measure it, which platforms they track, and what their process looks like for improving a brand’s citation rate over time.

Factor 4: Do they understand enterprise buying dynamics, not just enterprise operations?

This is the distinction we raised in the introduction, and it’s worth going deeper on because it’s where many agency evaluations go wrong.

There’s a meaningful difference between an agency that knows how to operate inside a large organization and an agency that knows how to create content for the buyers that large organizations are trying to reach. The first type has experience managing complex approval workflows, navigating brand governance processes, coordinating content across multiple business units, and producing high volumes of content at scale. Those are real capabilities that some enterprise companies need.

The second type understands how enterprise deals actually happen. They know that a six-figure software purchase involves multiple stakeholders with different objections, that the buying timeline is measured in months, that the content a VP sees during the evaluation phase needs to speak to business outcomes rather than product features, and that category-level keywords with enterprise modifiers like “project management software for large teams” or “SOC 2 compliant data integration” convert at far higher rates than broad informational queries.

Most agencies that market to enterprise companies are strong at the first and weak at the second. They understand procurement and compliance. They don’t necessarily understand how to create content that generates inbound interest from the specific decision-makers their clients are trying to reach.

When evaluating agencies on this factor, ask them to show you examples of content they’ve produced specifically for enterprise buyers. Ask how their keyword strategy accounts for longer sales cycles and multi-stakeholder buying committees. The answers will quickly tell you which type of agency you’re looking at.

Best Enterprise Content Marketing Agencies

Below are content marketing agencies we’ve run into or names we’ve heard associated with enterprise content marketing. Before listing those, we start with a detailed overview of our own and how we built Grow and Convert to address each of the four factors above, and the results we’ve achieved for clients.  For each, we’ll explain their approach, what they specialize in, and what types of companies they work best with. 

1. Grow and Convert

Grow and Convert is our SEO and GEO-focused content marketing agency. We’ve spent over 10 years executing content marketing strategies that drive demo requests and pipeline for enterprise B2B companies, not just traffic. 

Unlike agencies that optimize for top of the funnel metrics like traffic or rankings, we focus on bottom of the funnel content that generates qualified leads and measurable ROI through our Pain Point SEO and Prioritized GEO frameworks.

We work with enterprise B2B companies that sell complex products to sophisticated buyers, including large organizations with long sales cycles, multiple decision-makers involved in each deal, and significant revenue attached to each closed account.

Here’s how we address each of the factors we mentioned above:

Factor 1: We build content strategies around pipeline, not traffic

The core differentiator of our agency is that we measure performance on leads generated, not just traffic or rankings.

For every client, we create an ROI graph that tracks leads generated from our content each month against the number of leads they need to break even on their monthly investment with us. This gives enterprise marketing teams something concrete to show leadership: not sessions and impressions, but actual pipeline contribution from content.

For enterprise clients specifically, this means our SEO keyword strategy is built around the queries enterprise buyers search during an active evaluation, not during early-stage research. There are a few distinct keyword types we target for enterprise companies:

• Category keywords that reflect an active buying decision, such as “best [software category] for enterprise” or “enterprise [software category] software”
• Comparison keywords where buyers are already evaluating specific vendors, such as “[your product] vs. [competitor]”
• Jobs-to-be-done keywords that map to the specific outcomes enterprise buyers are trying to achieve, such as “how to manage compliance across multiple business units” or “how to consolidate HR software for large teams”
• Keywords with enterprise-specific modifiers that signal buying intent, such as “SOC 2 compliant,” “for teams of 500 or more,” or “with Salesforce integration”

Note, as we explain below, these keywords map really well to a company’s bottom of funnel topic map for AI search: the collection of topics users may ask AI tools about and where you want LLMs to mention your brand as a possible solution. 

In our research across enterprise B2B clients, bottom of the funnel content converts at 4.78% compared to 0.19% for top of the funnel content. For enterprise companies where a single converted account can represent hundreds of thousands of dollars in revenue, that difference is the entire argument for doing content this way.

Factor 2: We have a structured process for capturing genuine product expertise

Enterprise buyers are practitioners and senior decision-makers. A CISO evaluating a security platform or a CFO evaluating financial software will immediately recognize whether a piece of content was written by someone with genuine domain expertise or assembled from generic research. 

Most content produced by agencies falls into the latter category because writers are handed a keyword and asked to research the topic online, producing what we call Google research paper content that tells enterprise buyers nothing they couldn’t find elsewhere.

Our process is different. Before writing anything, we conduct in-depth interviews with product managers, engineers, sales leaders, and customer success teams at each client company. For enterprise clients, this often means interviewing the people closest to large-deal sales cycles, the account executives who understand enterprise objections, the customer success managers who know how large organizations actually implement and use the product, and the subject matter experts whose perspectives carry credibility with senior buyers. 

The resulting content contains what we call originality nuggets, specific insights and perspectives that competitors cannot replicate because they require direct access to your team and customers.

Factor 3: We have a structured AI search strategy

Enterprise buyers increasingly use AI tools like ChatGPT, Perplexity, and Claude to build vendor shortlists before engaging sales teams. Procurement teams at large organizations use these tools to identify solutions, compare vendors, and understand the landscape before a formal RFP process begins. If an enterprise company’s brand isn’t appearing in those AI-generated responses, it’s being filtered out of consideration before a sales conversation ever starts.

Most content marketing agencies approach AI search visibility through isolated tactics rather than a coherent strategy. We built the Prioritized GEO framework specifically to address this problem.

The framework treats AI visibility as a layered strategy: owned content ranking in traditional search as the foundation, followed by off-site brand mentions and third-party citations that signal authority to AI systems, and finally, on-site content structure optimized for AI retrieval. 

For enterprise clients specifically, our Tier 1 work goes beyond ranking for high-intent buying keywords. We build what we call a GEO topic map: a collection of content covering every product angle, use case, buyer persona, and competitive scenario in which an LLM could recommend you.

Our goal is to produce content that teaches LLMs to associate your brand as a possible solution when enterprise buyers describe their specific pain points in a conversation — content written at enough depth and specificity that when an AI tool searches the web while formulating a response, it finds clear, detailed signals about when and why your product is the right fit.

We track AI visibility at the topic level for every enterprise client using Traqer.ai (our own AI visibility tracking tool), which monitors brand citation rates across ChatGPT, Perplexity, and Google AI Overviews over time. We’ve also written about how we build topic-based GEO for clients in detail here.

Factor 4: We build content for enterprise buying dynamics, not just enterprise scale

Most agencies that serve enterprise companies understand enterprise operations. They know how to manage approval workflows, navigate brand governance, and produce content at scale inside a large organization. What they often don’t understand is how enterprise deals actually happen and what content moves enterprise buyers through the purchasing decision.

Our content strategies for enterprise clients are built around that buying process specifically. We map keyword strategies to the stages of an enterprise evaluation, from the initial queries a VP searches when they first recognize a problem, through the comparison and vendor evaluation searches that happen mid-cycle, to the compliance and implementation questions that come up before a deal closes. 

We write for the business outcomes and risk considerations that senior decision-makers care about, not the feature-level details that would appeal to an individual contributor. And because enterprise deals involve multiple stakeholders, we think about the full buying committee when developing content, not just the primary user persona.

You can learn more about working with us here, or read our full guide to enterprise content marketing strategy here. 

2. Tendo Communications

Tendo Communications is a B2B content and UX agency that works exclusively with large enterprise companies. Founded in 1999, they provide services including content strategy, content creation, UX design, content operations, and content performance. They are one of the oldest agencies in this space and one of the few that focuses entirely on the operational complexity of large B2B organizations rather than growth-stage or mid-market clients.

Unlike most agencies in this space, they integrate content strategy with user experience design. They acquired a UX design agency to bring both disciplines under one roof, making them well-suited for enterprise organizations managing large content ecosystems across multiple products, regions, and audiences.

Their production model centers on a Content Studio approach that provides dedicated editorial teams for sustained content production at scale. They use a proprietary audience-centric framework built around persona development and journey mapping, and work with clients on a long-term strategic basis across multiple regions, languages, and business units rather than as a project-based content vendor.

They have worked with companies like Cisco, Salesforce, HPE, Autodesk, VMware, Informatica, and Okta.

Visit their site for more information.

3. Animalz

Animalz is a content marketing agency that works with B2B SaaS companies, enterprise software companies, and venture capital firms. They describe their programs as built to drive organic growth through content that reflects genuine domain expertise rather than research-assembled writing, and they have worked with enterprise technology companies alongside growth-stage SaaS clients.

Their production process starts with a deep onboarding phase where they study the client’s product, industry, and go-to-market strategy before developing a content strategy and moving into production through direct SME interviews. They emphasize building content that requires insider knowledge, which they believe is what separates effective B2B content from generic writing assembled from existing search results.

They also offer Answer Engine Optimization as a dedicated service, which includes brand visibility audits across Google AI Overviews, ChatGPT, Perplexity, and Gemini. They have published detailed frameworks on improving AI citation rates and employ a dedicated AI and marketing operations function internally.

They have worked with companies including Google, Amazon, Atlassian, GoDaddy, Airtable, Amplitude, Intercom, UiPath, and Ramp.

Visit their site for more information.

4. Siege Media

Siege Media is an organic growth agency specializing in SEO, GEO, content marketing, and digital PR. Their content strategy begins with a proprietary keyword prioritization framework, and they are explicit that their writers are senior specialists matched by vertical expertise rather than generalist freelancers.

Their production process emphasizes quality over volume, with approximately half of their content creation time dedicated to design alongside writing. They are also vocal about their selective use of AI in content production, stating that while they use it for certain tasks like brainstorming and outlines, writing is rarely one of them.

On the AI search side, they have built two proprietary tools. BlueprintIQ audits content against live AI search results across ChatGPT, Gemini, and Perplexity to identify gaps in topical coverage. DataFlywheel refreshes existing content assets on a rolling quarterly basis to maintain relevance for AI citation. These tools put them among the more technically developed agencies on this list when it comes to GEO execution.

They have worked with companies like Zapier, Figma, Instacart, Asana, and Intuit.

Visit their site for more information.

5. Brafton

Brafton is a full-service content marketing agency that has been operating since 2008. Their services cover blog writing, graphic design, video production, email marketing, social media management, PPC, web design, and SEO consulting. They work with a wide range of company sizes across industries including technology, healthcare, finance, and manufacturing, and have offices across the United States, United Kingdom, and Australia.

A distinguishing feature of their model is a proprietary content marketing platform they have built for workflow, project management, and content performance tracking. Each client is assigned a dedicated content marketing strategist who provides strategic direction alongside the production team. Their writers are described as full-time in-house staff rather than outsourced freelancers, matched with clients based on industry expertise.

They also offer generative engine optimization as a dedicated service, covering content optimization for AI retrieval, structured data, and citation tracking across AI platforms. Their engagement model is unit-based rather than per-piece pricing, which gives clients flexibility across different content types and volumes.

They have worked with companies like Webex, ViewSonic, Oxford University Press, and TruGreen.

Visit their site for more information.

6. Column Five

Column Five is a B2B content marketing agency that focuses on brand storytelling for enterprise SaaS and AI companies. Founded in 2009, they position themselves around helping companies develop a differentiated point of view and then expressing it across multiple content formats, including video, motion graphics, infographics, interactive content, and written content. Their tagline is “the best story wins,” which reflects their emphasis on narrative and brand identity alongside organic growth.

Their process begins with a proprietary Story Scan Program that audits a client’s brand messaging, competitive positioning, and content gaps before any production begins. They describe their approach as audit-first, strategy-second, and execution-third, and have published case studies showing multi-year engagements with enterprise clients where they work across multiple internal teams simultaneously.

They also offer Answer Engine Optimization consulting and AI-powered content services as part of their service line, and they explicitly position these services to help brands get found by both human audiences and LLMs. They also operate a proprietary AI content tool called Iris for clients that need content at scale.

They have worked with companies including Instacart, Databricks, Vercel, Salesforce, LinkedIn, GitHub, Microsoft, and SAP.

Visit their site for more information.

7. Omniscient Digital

Omniscient Digital is an organic growth agency that helps B2B software companies drive pipeline through SEO, GEO, and content. Founded in 2019 by alumni from HubSpot, Shopify, and People.ai, they position themselves around revenue as the end goal rather than traffic growth, and they publish detailed thinking about content strategy and organic growth through their own blog and podcast.

Their research methodology, which they call OmniscientX, includes competitive analysis, buyer research, content gap analysis, LLM visibility indexing, keyword exploration, product alignment, and SME interviews before strategy development begins. They emphasize hiring for business acumen alongside SEO skills and integrate closely with client product marketing, sales, and go-to-market teams rather than operating at arm’s length.

Their GEO work incorporates LLM visibility indexing and citation analysis from the research phase onward, and they apply RAG principles and semantic modeling to improve how AI systems retrieve and surface client content.

They have worked with companies like Jasper, SAP, Asana, Loom, Hotjar, Adobe, and Smartling.

Visit their site for more information.

8. Foundation Inc

Foundation Inc is a distribution-first content marketing agency for B2B companies, founded by Ross Simmonds. Their core premise is that most agencies invest heavily in content creation while underinvesting in distribution, and their entire process is built around that belief. They describe their methodology as research, create, distribute, and optimize, with distribution treated as equally important as production.

Before producing any content, their team studies client audience newsletters, online reviews, sales calls, Reddit communities, social platforms, and competitor content. They then build a 12-month content engine with specific distribution playbooks for each piece, covering newsletter placement, founder content, social media, YouTube, Reddit, and sales enablement channels.

Their content creation process includes formats built specifically for LLM visibility, and their optimization work targets ChatGPT, Claude, SGE, Gemini, and Perplexity. Their homepage explicitly positions them as an agency that increases LLM visibility alongside pipeline generation.

They have worked with companies including Webex, Mailchimp, Snowflake, Canva, Procore, and Paychex.

Visit their site for more information.

9. Power Digital

Power Digital is a full-service performance marketing agency that works with both B2B and consumer brands. Founded in 2012 and backed by private equity, they are one of the largest agencies on this list by headcount and operate across a wide range of services, including SEO, paid media, paid social, email, influencer marketing, PR, affiliate marketing, programmatic, and content. 

Their core technology is a proprietary platform called Nova, which connects first-party client data to inform strategy and performance measurement across channels.

Content marketing sits within their broader owned media offering rather than as a standalone specialty. Their model is built around integrating content with paid and earned media channels rather than treating organic content as an independent growth driver. This makes them a strong fit for companies seeking a single agency to manage multiple marketing channels simultaneously.

They offer generative engine optimization as a dedicated service, covering LLM visibility audits, content structuring for AI responses, technical schema implementation, and citation engineering across ChatGPT, Perplexity, Gemini, Claude, and Microsoft Copilot. Their Nova platform also connects AI search performance data to broader marketing attribution.

They have worked with companies like ASICS, Crocs, Taylor Guitars, and Data.World.

Visit their site for more information.

10. iPullRank

iPullRank is an enterprise content marketing and SEO agency based in New York. They describe their focus as Relevance Engineering, a proprietary framework that integrates content strategy, technical SEO, information retrieval, digital PR, and user experience to optimize content for both traditional search and AI search platforms. 

They work primarily with enterprise and mid-market companies and claim to have delivered over $4 billion in organic search results for their clients.

Their approach is built around the idea that enterprise brands need specialized partners rather than generalist agencies that treat content and SEO as interchangeable services. Their Relevance Engineering framework uses semantic content structures, passage scoring, and retrieval modeling to engineer brand visibility across AI search platforms alongside traditional Google rankings. They also offer AI search audits that surface a client’s current presence across both traditional and generative platforms.

On the content side, their team combines technical SEO expertise with content strategy and content engineering, and they are explicit that their writers and strategists are specialists rather than generalists. They publish detailed original research and thought leadership on natural language processing, machine learning applications in SEO, and how AI systems retrieve and rank content, which reflects how technically grounded their methodology is.

They have worked with enterprise companies like LG, American Express, Citi, and DocSend, as well as Fortune 500 companies across financial services, media, and retail.

Visit their site for more information.

Want to work with us or learn how to implement our enterprise content marketing strategy?

• Our Agency: You can learn more about working with us here.
• Our Content Marketing Course: Individuals looking to learn how to grow their enterprise business with content can join our private course, taught via case studies, here. We include a lot of information and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we and other members give feedback. We also get on live Zoom calls about once a month and dissect members’ actual content strategies and brainstorm ideas on how we’d form content strategies for their businesses.
• Join Our Content Marketing Team: Alternatively, if this style of enterprise content marketing appeals to you, consider joining our content marketing team as a writer or content strategist. We have awesome clients. We’re a remote company. We pay well. And you won’t have to stress about getting your own clients or spend a bunch of time doing outreach to get them.

While it’s great that customers are finding us and our clients through AI search, this creates the problem of reproducibility.  How do you get more of these leads if they’re coming from extremely unique and personal conversations with, say, ChatGPT?

You can’t see these “AI rankings” in your AI visibility software. Peec, Profound, or our tool, Traqer are not going to show you the three-page conversation your recent lead had with ChatGPT. No one has that data (besides OpenAI, who obviously hasn’t revealed it). These are private, highly personalized conversations where the AI tool knows an astonishing amount about the user’s pain points and needs. 

Company after company we talk to is saying they’re getting these leads, but they don’t know how. They don’t know exactly what customers are typing into LLMs, yet the leads keep coming.

We’re calling this phenomenon Invisible Prompts because you can’t see what they are. 

But they’re very real, and as I’ll argue below, they’re only going to grow. 

What Are Invisible Prompts? 

It’s important to clarify that by “Invisible Prompts,” we don’t just mean prompts of a slightly different wording that you happen to not be tracking — like “what are some good content agencies?” versus “find me the best content agencies”. 

It’s not as simple as just thinking of those alternative wordings and sticking them into your AI visibility tool, feeling like you have everything covered. 

This is not what we mean. It’s not a wording issue. It’s much deeper.

The issue is that even if a real user types in the exact wording you are tracking in some AI visibility tool, ChatGPT (or Claude or Gemini) will factor in a massive amount of heavily personalized information about your customer that you can’t predict. This extra personal information about the user will completely change what ChatGPT recommends to them. 

We’ve started thinking about this as the difference between the literal prompt and the effective prompt. The literal prompt is what the user literally types into ChatGPT. The effective prompt includes all the additional information ChatGPT knows about them. 

For example, one of our leads could type “find me a good content marketing agency”. What ChatGPT will actually do to generate an answer is take into account everything it knows about that customer — things like what size business they have, their budget, their industry, what they’ve tried in the past, who is on their team already, and more. 

So, although their literal prompt may just be “find me a good content agency”, their effective prompt is more like a 1000-word essay on their business situation that ends with “…and so taking all that into account, what do you think would be a good content marketing agency for me?”

This is fundamentally not trackable. At least not in the “SEO style” that marketers are used to, where prompts are like keywords, each with some search volume. 

What we’re saying is we are moving to a world where the search volume of most people’s prompts is one. 

Your AI tracking tool isn’t surfacing these prompts to you — even ones like Profound that claim to have data from millions of “real” chats. That data is coming from people who have volunteered (knowingly or unknowingly) to have their web browsing tracked. Tools like Profound summarize patterns in that data, and then say “these prompts” have “this many” searches. So, fundamentally, it’s still the old SEO way of thinking: there are keywords and those keywords have certain search volumes. 

What Profound or any other tool doesn’t know is the deep personalization underneath each prompt. 

That personalization is what’s deciding whether or not you are recommended to the user. 

Examples of Invisible Prompts 

Back in early 2025, Benji wrote about how AI search would reward ultra-specific, bottom-of-funnel content. That post was an early version of this argument: that LLMs would factor in all this personal context when making recommendations. 

In that post, he shared this example of one of our current clients who found us on ChatGPT. We asked her to share the chat where it recommended us. Here is her exact prompt and the beginning of ChatGPT’s answer, where it recommends us: 

Her literal prompt was just: “What are the best SEO companies in your opinion who have driven massive results with endless leads for people in our kind of space?”

But you can see from the response that the effective prompt was longer. Look at how ChatGPT knows her space, knows her goals, knows her constraints. She didn’t actually type that in. 

So here’s the issue: If we track this exact prompt in an AI visibility tool, we won’t see ourselves (Grow & Convert) mentioned, even though in real life, we were. 

I actually tested this. I opened an incognito ChatGPT window and typed in her prompt, and even replaced “our kind of space” with “the business loans and funding space” to help it along.

The response is totally different from hers and doesn’t mention us at all, much less as the top option: 

This is the visibility issue we’re talking about. 

The real prompt — with all the context, nuance, and detail shaping whether ChatGPT, Claude, Gemini, or any other AI recommends you — is far longer, more detailed, and more personalized than you will ever see.

It’s invisible. 

Why Invisible Prompts Are Only Going to Grow 

This phenomenon, from what we can tell, is only growing as AI tools become more and more ingrained in everyone’s personal and professional lives and workflows. More people are logging in and staying logged in (Do you know anyone who uses ChatGPT or Claude regularly but in incognito?). 

ChatGPT, Claude, and Gemini are building longer and longer histories of us. People are even using agents now and giving those agents access to their email and calendar. The context windows in these tools are growing (meaning they remember and factor in more information), and the tools themselves have memory features that remember things from all of your chats when answering new questions. 

In other words, ChatGPT knows more about a user who’s been chatting with it for a year than one who just signed up. That history is context. And context changes recommendations.

All of which means: the gap between the literal prompt and the effective prompt is only getting wider.

How to Get More Invisible Prompt Leads

So what do you do to get more AI search leads if the true prompts are invisible? That’s what every company who observed this asks us. 

Our answer is the same thing we’ve been saying since Benji’s AI search post last year: create ultra-specific content that teaches LLMs all the myriad use cases and pain points where your solution is a good fit. You need to feed LLMs the details they need to recommend you in specific scenarios even though you can’t predict the searches a priori. 

For example, we didn’t know our financial sector client above would ask ChatGPT for an SEO agency, much less how. We didn’t create content specifically for her prompt. We’ve just been writing about how most content marketing does an awful job of generating leads, and how we fix that, for 10 years. 

That’s what you need to do. Create content for as many specific use cases and scenarios as possible. Start with the places and ways you shine. Start with actual case studies. Then move to features, pain points you solve, and the specific situations where your product or service is the right fit.

This Is Why Owned Content is Tier 1 in Prioritized GEO

This is also exactly why we have “owned content” as the highest priority tier in our Prioritized GEO strategy. 

Creating a variety of your own content, as opposed to chasing mentions in Reddit, gives you the room to discuss all the nuance and detail of where and when you shine, what pain points you solve, and more. 

You can do this easily on your own site. On Reddit, what are you going to do? Drop your name in one sentence? That’s not enough context for an LLM to connect you to a specific user’s needs.

What Not to Do: Over-Focus on Single Prompt Visibility

It also explains why we de-emphasize Tier 3, the on-site content tweaks. Adding a schema or an LLMs.txt file won’t help you cover these unique scenarios. An FAQ might help at the margins, but what’s far more effective than that is actual detailed content.

It also means that trying to game the system and show up for a single really specific prompt doesn’t make sense, because no one is actually going to ask that exact prompt. If you produce a dedicated article for one narrow prompt and show up for it, it may look good inside AI visibility tracking software, so your client or management team might be happy. But possibly no one is going to have that exact exchange with an LLM, so it’s not likely to help in a real scenario.

How to Measure Progress  

So is measurement totally hopeless? No. This is all still evolving, but we don’t think so. We’re actually leaning heavily into Topic-Based GEO to solve this, and here’s why.

While you can’t know the exact prompts people will use, you can observe, across clients and over time, trends in how much you’re showing up in AI search at the topic level.

We can see that clients with established brands and years of rock-solid SEO presence in their industry have visibility numbers like this:

While clients that are much younger, with newer domains, and without a long history of content and SEO presence, have numbers like this:

That matters. 

And as per Topic-Based GEO, we can see this across topics. For example, our own AI visibility for “B2B content marketing services” versus “B2C content marketing services” looks completely different:

While we’ve had both B2B and B2C clients and our process works on both, we’ve had more B2B clients, have written more about B2B challenges, and published more B2B case studies. So the industry — and by extension, the LLMs — know us better in a B2B context, and that shows in visibility differences.

These topic-level differences are actionable. It means if we want to get more B2C clients, we need to start producing more content about our B2C work. That way, AI tools will have that information and context and be more likely to recommend us when someone who works at or owns a B2C business asks about content agencies.

That’s how you work invisible prompts in your favor. The prompts themselves may be invisible, but you can still monitor your presence when certain topics are broached.

Which takes me to a final analogy before we close.

Invisible Prompts Are Digital Word of Mouth

This is all curiously similar to the oldest and most effective marketing channel of all time: word of mouth. 

Word of mouth is the holy grail of marketing and brand building. You eventually want to just be recommended whenever someone asks for a product or service in your space. 

But think about what actually happens when you ask your friend for product recommendations. That conversation has all the nuance and detail about your specific situation behind it. 

For example, if I ask Benji for surfboard recommendations, my literal question to him may just be “Hey what board should I buy?” but in his head, he’s going to apply the same complexity that ChatGPT does. He’ll factor in all he knows about me and my surfing: the fact that I’m a beginner, that I’m 6’6” tall, that I currently ride a 10’ foam board, and that I’m extremely handsome. All of it matters. 

So while my literal question to him is short (“What board should I buy?”), the effective question he’s answering is much longer. It includes all the details he knows about my surfing. 

That’s true if someone asks a friend about buying software, hiring a law firm, buying clothes or shoes, or even finding a good coffee shop. 

AI search is similar. Just replace the friend or colleague who knows a lot about you with ChatGPT, Claude, or Gemini. 

The only difference: Benji knows about surfboards from years of actually surfing. ChatGPT only knows what it has read. So if you want LLMs to recommend to you the way a knowledgeable friend would, you need to have published the content that gives them the context to do it.

You can’t predict the invisible prompts. But you can build the content foundation that gives LLMs enough detail to recommend you when those conversations happen.

It’s not that search engine optimization doesn’t work. It’s that the way most companies do SEO doesn’t work. There’s a difference. 

In our experience working with over 100 clients at Grow and Convert, the reasons fall into the same three categories almost every time: 

• They’re targeting the wrong keywords
• The content itself doesn’t convert
• They’re measuring the wrong things

Let me break down each one. Then I’ll show you what it looks like when you fix all three, using real conversion data from one of our clients.

Problem 1: You’re Targeting the Wrong Keywords

This is the most common reason SEO “doesn’t work.” Most companies miss it because, on the surface, their keyword strategy looks fine.

Here’s what typically happens: a company hires an SEO agency or brings someone in-house. That person does keyword research in Ahrefs or Semrush, sorts by search volume, and picks keywords with the highest numbers. They target terms like “what is time tracking” or “best practices for employee scheduling” or “how to improve productivity.” These are real searches. They have volume. They look like opportunities.

The problem is that the person searching “what is time tracking” isn’t looking to buy time tracking software. They might be a student writing a paper, a manager doing preliminary research, or someone who just wants a definition. There’s no buying intent behind the search.

Yet SEO firm after SEO firm prioritizes keywords this way: by search volume over buying intent. So brands end up with a library of content that may drive traffic (sometimes a lot of traffic) but has extremely low conversion rates to leads, pipeline, or revenue. 

Our approach is different. Instead of prioritizing keywords by search volume, we prioritize by buying intent. We ask: 

Which keywords indicate that the searcher has a problem our product or service solves? 

Those are the keywords that convert.

We call this Pain Point SEO. 

The difference in results isn’t small. Across our client base, high buying-intent keywords convert at 10x–20x the rate of top-of-funnel informational keywords. 

I’ll show you the data later in the piece — and the gap is striking.

The Other Keyword Mistake: Spreading Too Thin

There’s a second keyword problem I see constantly, especially from agencies. Instead of focusing each article around one keyword and writing the best possible piece for that specific search intent, they try to stuff multiple keywords into a single article. The logic is tempting: “If we target five keywords instead of one, we’ll rank for more things.”

But when you do this, the opposite happens. The article doesn’t rank well for any of them because it’s not focused enough to satisfy any single searcher’s intent. Search engines can tell when a piece is trying to be about five things at once. So can readers.

Each article should target one primary keyword. Build the entire piece around what that specific searcher needs to know — and how your product solves their problem. Search engines reward that kind of focus. 

Do that one thing well.

Problem 2: Your Content Doesn’t Convert (Even If It Ranks for High-Intent Keywords)

You can do everything right on the keyword side — rank high for high buying-intent keywords, get real traffic — and still generate almost no leads from your blog. Why? Because the content itself isn’t written to convert.

Most blog content reads like Mirage Content. It looks good when you skim it, but when you actually read it, there’s nothing there that a competitor couldn’t say. Nothing that shows the reader why this company, specifically, is the one to solve their problem. 

Here are the typical problems we see on the content side:

• The writer lacks subject matter expertise. Most content is written by freelancers or generalists who Google the topic for 30 minutes before writing (we call content like this “Google Research Papers”). The result is surface-level content because the writer doesn’t know enough to go deep. Your target customer, who has been in their industry for years, can tell immediately. They’ll read two paragraphs, realize the writer doesn’t actually understand the space, and leave.
  
• The content doesn’t sell the product. There’s a widespread belief in content marketing that blog posts shouldn’t be “salesy.” We disagree. Your blog posts should demonstrate how your product solves the reader’s problem. Not with a hard pitch, but with specificity. Show your product in action. Walk through real use cases. Include screenshots. The reader is searching for a solution. If your content doesn’t show them that you have one, you’re leaving conversions on the table.
  
• AI is making this worse. These content problems existed long before AI (we wrote about Mirage Content in 2017), but they’re now being amplified by it. As our survey of marketers about AI writing showed, the vast majority of in-house teams and agencies are using AI assistance to produce content. We do too. But most are using it the wrong way and just producing more low-quality Google Research Papers. AI isn’t the problem. It’s just speeding up how fast teams can produce mediocre content. 

The fix is writing content that demonstrates real expertise. That means interviewing subject matter experts inside the company (founders, product leads, salespeople), outlining the piece around the specific searcher’s problem, and writing at the depth level of someone who actually works in the space.

Problem 3: You’re Measuring the Wrong Things

Many in-house content marketers and digital marketing teams focus on metrics like pageviews, number of pieces published per month, and keyword rankings. Why? Because if they were measured by leads, demos, or pipeline, the numbers wouldn’t look good. So they focus on metrics that prove “success” without actually moving the needle for the business.

Here’s a real example. One of our clients, a time and attendance software company, came to us after working with another agency for over a year. The business owners told us their SEO wasn’t working. But when they showed us the reports the previous agency had been sending, the agency’s metrics told a different story. Rankings were improving. Organic traffic was growing. Articles were being published on schedule.

The problem? The agency had never set up proper analytics to track conversions. They were reporting on rankings and traffic, sending polished monthly decks that showed upward-trending charts, and counting how many pieces they published each month. But they weren’t measuring how many trial signups or paid conversions the content was actually driving. 

The founders couldn’t see business impact from SEO because no one was tracking business impact.

This pattern is more common than people think. A lot of agencies and in-house teams measure SEO performance by:

• How many articles we published this month
• How much organic traffic the blog is getting
• What keywords we’re ranking for
• The domain authority of the backlinks we built
• Bounce rate and page speed scores

None of these are business metrics. They’re activity metrics. They tell you whether your SEO efforts are happening, not whether they’re working.

The metrics that actually matter are: 

• How many conversions (demo requests, trial signups, contact form submissions) did each blog post and landing page drive this month? 
• What’s the conversion rate by article? 
• Which pieces are generating real pipeline? 

If you can’t answer those questions, you don’t actually know whether your SEO is working. You just know you’re publishing content. And publishing content doesn’t mean SEO is working.

What Happens When You Fix All Three: A Case Study

Here’s what happens when you get all three of these right. This is the same time and attendance software client mentioned above.

When we started working with them, we did three things differently from their previous agency.

First, we identified their highest-intent keywords. Instead of targeting broad terms or stuffing multiple keywords into each article, we focused each piece around one bottom-of-funnel keyword where the searcher had a real problem the software solves. Category keywords like “[product type] app,” category keywords with a specificity layer like “[product type] for [industry]” or “[product type] with [feature],” and use-case keywords like “apps to [job to be done].”

Second, we produced the content by interviewing their founders. We didn’t hand topics to freelancers. We interviewed the company’s founders and small business operators to capture their actual expertise about the time clock software space. The content reflected real industry knowledge, specific product details, and the kind of depth that only comes from people who’ve built the product and talked to thousands of customers.

Third, we invested in link building and set up analytics tracking to measure conversions by article. Every blog post was tracked individually for trial signups, paid conversions, and conversion rate. This allowed us to see exactly which pieces were driving business results and double down on what worked.

The Results

We worked with this client for about three years. Here’s how the numbers progressed:

• Organic traffic: 69 visitors/month → 11,555 visitors/month
• Free trial signups from the blog: 0/month → 295/month (at peak)
• Paid signups from the blog: 0/month → 55/month (at peak)
• Blog conversion rate: 1.7% → 3.9% (visitor-to-trial)

Over the full engagement, we published 111 blog posts that collectively drove 6,680 trial signups. But here’s where it gets interesting.

Not All Keywords Are Created Equal

When you look at the conversion data across all 111 articles, the distribution is extreme.

15% of the articles drove 80% of all conversions.

And those top-performing articles were almost exclusively targeting high buying-intent keywords: core category keywords and category keywords with a specificity layer.

Here’s the breakdown by keyword type:

Look at that last row: 16 informational articles. 40 total conversions. An average of 2 per article. 

This is the data behind why your SEO isn’t working. If your content strategy is built around informational, top-of-funnel keywords, you could publish content for years and barely move the needle on leads. It’s not that SEO is broken. It’s that your keyword strategy doesn’t match your business goal.

What’s the value of going after more keywords if 15% of the articles drove 80% of conversions?

Fair question. If 15% of articles drove 80% of conversions, what’s the point of the other 85%?

Two things. 

First, you can’t predict which articles will be top performers before you publish them. Some keywords you’d expect to perform well (like broad category terms) underperform, while narrower, long-tail terms you’d consider secondary end up driving hundreds of conversions. The only way to find your winners is to publish a portfolio of high-intent pieces and let the data tell you.

Second, the other 85% of articles aren’t worthless. They still drive incremental conversions that add up over time. They build topical authority, which helps the entire domain rank better in search engines. They support link building and earn quality backlinks. Internal linking strengthens the whole site. And they cover the full range of ways your prospects search for solutions. 

You get to a total of 295 trial signups per month (our all-time high for this client) from dozens of articles working together, not from a single post.

The takeaway isn’t to publish fewer articles. It’s to make sure every article you publish targets the right keywords — ones with real buying intent. 

How to Diagnose If Your SEO Isn’t Working

If you read this and thought “Ok, our SEO probably isn’t working,” and you want a specific checklist to figure out which of these problems is the cause, here it is. 

Step 1: Look at your keyword list. 

Pull up every keyword you’re targeting. For each one, ask: is the person searching this keyword looking to buy or evaluate a product like mine? If the majority of your keywords are informational (“what is X,” “how to Y,” “best practices for Z”), that’s likely your primary problem.

Step 2: Read your content as if you’re a buyer. 

Pick your top five posts by traffic. Read them. Ask yourself: does this article show how a specific product solves a specific problem? Or does it read like a Google research paper that could appear on any competitor’s blog? If it’s the latter, your content isn’t converting because it isn’t written to convert.

Step 3: Check your analytics setup. 

Can you tell me, right now, how many conversions each individual blog post drove last month? If you can’t, you don’t actually know whether SEO is working. Use Google Search Console to monitor search performance, and set up goal tracking in Google Analytics or HubSpot so you can measure conversions at the article level.

Step 4: Look at the results through the right lens. 

Once you have conversion data by article, sort by conversions instead of traffic. The posts driving the most traffic and the posts driving the most conversions are almost certainly different lists. That gap tells you everything you need to know about your keyword strategy.

If you’re looking for SEO experts to fix your strategy, you can learn more about our process and inquire here.

You see the same AI-generated LinkedIn posts with the same structure and the same lazy arguments. AI-generated comments that add nothing. AI slop blog posts that say what every other blog says, just reworded slightly. It feels like the entire internet is getting worse.

But I think the reason people have such a negative view of AI is because of how most people are using it. They’re using it to try to think less and get out of putting in hard work.

That’s not the best use of AI.

Over the past few weeks, I’ve gone down the AI rabbit hole and I’ve gone from an “AI skeptic” to thinking it’s going to create a renaissance in marketing.

From what I’ve seen, AI is most powerful when you already have expertise, you already know what you want the outcome to be, and you use it as a tool to get there faster or build something you couldn’t have built before. 

The marketers who will win with AI aren’t the ones trying to remove themselves from the process. They’re the ones asking:

What can I create now that I couldn’t before?

And this is where it gets interesting for marketers.

AI Accelerates Whatever You Already Are

Here’s a key thing I don’t see enough people talking about: AI accelerates what you do well and what you do poorly.

If you’re an agency producing bad content with no real strategy, AI will happily help you produce bad content at scale. Congratulations, you can now publish 10x more mediocre blog posts per month.

But if you already have a great process, smart people, and creative ideas, AI unlocks things you simply couldn’t do before. It doesn’t replace thinking. It removes the bottlenecks that used to prevent your thinking from coming to life.

Here’s an example:

I recently wrote a case study we’ll be publishing on Grow and Convert. We like to make our case studies data-driven, not just “traffic went up,” but digging into the data to uncover patterns that actually make the story compelling.

For this particular case study, we had spreadsheets full of conversion data broken out by month and by individual article. In the past, I would have needed someone on my team to help me analyze all of that data, find the interesting patterns, and build the charts and data visualizations. That’s at least two or three people involved: an analyst, a designer, and me writing.

Instead, I did the entire thing myself using AI. 

I had it analyze the conversion data and surface patterns I wouldn’t have spotted manually. I built data visualizations that previously would have required our designer. I queried Ahrefs to compare the keywords the client ranked for before we started working with them versus after, and used AI to help analyze the shift in keyword strategy. 

The result was clear: AI identified that every single keyword they ranked for previously was informational and top-of-funnel, while the keywords we helped them rank for were almost entirely buying-intent. Pairing that with conversion data to quantify the business impact made the case study dramatically stronger.

Had I done this without AI, the analysis wouldn’t have been as thorough, the arguments wouldn’t have been as sharp, and it would have taken me significantly longer to complete.

I didn’t ask AI to “write me a case study about X client” (which is how most people are using it right now). It didn’t know what story to tell or what arguments would resonate. I did. AI just removed the resource constraints that would have made that level of depth impractical.

We’re Entering the Age of the Creative Marketer

For as long as I can remember, creative marketers have been severely undervalued compared to performance and data-driven marketers.

They’d have great ideas but sometimes had challenges bringing them to life. Because to execute anything interesting, you needed other stakeholders involved. A designer, a developer, or multiple people on the team. The ideas were sometimes costly and resource intensive, and the great ideas would die in a Google Doc because there was no way to build them.

AI changes that. If you can dream it, you can build it.

And I think that’s going to unlock a new era of marketing, almost like a marketing renaissance, where the most creative ideas drive the most attention. Not the biggest budgets. Not the largest teams. The best ideas.

The marketers to win in this era will be the ones creating the coolest, most original stuff. Not the ones publishing the most AI-generated volume.

Constraints Have Shifted From “Can I Build This?” to “What Can I Build?”

We used to be constrained by resources. We had more ideas than people or time to execute them.

With AI, that constraint has shifted. We’re no longer limited by execution capacity. We’re limited by the quality of our ideas and our creativity.

That’s a fundamental change, and most people haven’t caught up to it yet.

If all you bring to the table is execution speed — writing fast, designing fast, shipping fast — AI is going to be a problem for you. Because it can do that too.

But when you contribute original thinking, creative ideas, a deep understanding of your customer, and a clear point of view, AI makes you 10x more dangerous. You can bring those ideas to life without waiting on anyone else.

The age of the creative marketer is here. 

The marketers who build the coolest stuff win.

Both are built on the principle that most content teams waste time on well-established but ineffective tactics that, at best, drive traffic with extremely low conversion rates to qualified leads. 

Over the years, we’ve noticed that this problem is especially acute in enterprise companies. Enterprise organizations have all the content operations infrastructure you’d expect: content teams, agency retainers, editorial calendars, brand guidelines, content governance policies, multi-step content workflows, and they publish content consistently and in large volume. But where those resources are pointed to is at a strategy that’s fundamentally flawed: it’s focused on chasing search volume in SEO or citations in AEO/GEO (or frankly, in AEO/GEO, many organizations right now simply don’t have a coherent strategy and aren’t sure what to focus on). 

So,  when leadership asks what all that investment actually produced and how it connects to business goals, the content team can only point to traffic numbers or publishing volume, and they can’t really trace content to pipeline or revenue.

Specifically, here are the flaws in the typical enterprise content strategy: 

• Traffic, not conversion-focused strategy: Their content strategy is built around high-volume, top of the funnel keywords that drive traffic but have extremely low conversion rates to sales-qualified leads (SQLs). Enterprise content marketing teams gravitate toward broad, educational terms mostly because they operate in a silo and are disconnected from sales data or product teams, and traffic is the metric that’s easiest to report on.
• Content just summarizes Google: Enterprise companies often have large content teams or multiple agency partners, but the actual writing process follows the same playbook: a writer or AI tool researches the keyword, reads what’s already ranking, and produces a synthesized version of the same information. At enterprise scale, this is a particular problem because the people reading this content are practitioners who already know the basics and can immediately tell when an article was written by someone who has never actually done the work.
• Too little product sales copy: They avoid selling their product in their content. Enterprise content teams often treat blog content as a brand awareness exercise, so articles deliberately avoid explaining how the product actually solves the problem being discussed. This exacerbates the low conversion problem. 

The approach we’ve built at Grow and Convert addresses all three of these problems, and in this article, we’ll walk you through our entire enterprise content marketing process:

1. Prioritize high-buying-intent keywords: Prioritize keywords based on buying intent rather than search volume, focusing on the terms enterprise buyers actually search when they’re evaluating solutions to specific problems. Traffic and brand awareness are fine goals, but if the brand isn’t consistently visible for bottom of funnel, high-intent searches, you’re leaving SQLs on the table for no reason. This is even more pressing in AI search because AI tools simply don’t link to or mention brands for top of funnel queries (more on this here). 
2. Produce content through internal employee interviews, not via genAI or humans regurgitating Google: Instead of having writers (or an AI tool) research topics on their own, which simply creates generic, me-too content, conduct interviews with internal subject matter experts to create content that contains genuine product and domain expertise. Enterprise buyers are not beginners, they’re advanced, and therefore your content also needs to be advanced. 
3. Sell your product in each piece of content: Because we emphasize producing high buying intent content, in this content, you need to talk about your product extensively, including specific features, differentiators, and use cases, rather than staying vague in the name of brand awareness.
4. Build links and AI search citations: Generate backlinks and optimize for visibility in AI search tools like ChatGPT and Perplexity.
5. Set up conversion tracking and attribution: Track actual pipeline metrics like demos, trials, and sales conversations sourced from content, not just traffic and rankings.

Grow and Convert is an enterprise content marketing and SEO agency. If you want to hire us to handle your enterprise content marketing, you can learn more about working with us [here]. 

Our 5-Step Enterprise Content Marketing Process

We’ve written at length about each of these steps individually across our site. Here, we’re going to walk through how each step applies specifically to enterprise content marketing and link to further resources for each step:

Step 1: Identify High-Intent Keywords

The most fundamental part of an enterprise content marketing strategy is keyword selection. Keywords (or equivalently, prompts and topic areas in AI search) indicate the level of buying intent of the user.

If you’re only going after low-intent keywords, ones where the searcher isn’t in the market for your product at all,  then nothing else in your enterprise content marketing strategy is going to matter. The volume of content you publish doesn’t matter. The quality of your editorial calendar doesn’t matter. Your brand guidelines and approval workflows don’t matter. You will simply generate low intent, low converting traffic over and over again. 

Instead,  to create an enterprise content marketing strategy that actually generates appreciable pipeline and revenue, you need to prioritize keywords based on buying intent, not search volume. We’ve written extensively about this concept in our article on Pain Point SEO, the strategy we coined that prioritizes content ideas around high-intent keywords over high-volume ones to drive conversions.

Most enterprise content teams do exactly the opposite. They build their content calendar around broad, high-search-volume keywords that attract the widest possible audience, because the logic is that more traffic means more potential customers, but that’s not really the case. 

In practice, ranking for a keyword like “what is supply chain management” at 8,000 monthly searches will generate far fewer leads than ranking for “manufacturing supply chain management software” at 350 monthly searches, because the second keyword captures someone who already knows they need a solution and is actively looking for one.

This problem is especially pronounced in enterprise companies, where content teams don’t have visibility into which keywords actually drive the pipeline. If they operate in a silo, disconnected from sales data, they usually default to optimizing for the metric they can see: traffic.

We approach keyword selection differently. Instead of starting with search volume, we start with the customer journey and work backward to find the keywords that indicate someone is ready to evaluate or purchase a solution.

This means understanding your target audience and buyer personas deeply enough to know the language they use when actively looking for a product like yours, not just when researching a general topic.

These keywords are also where enterprise content marketing becomes a genuine lead generation channel rather than a brand awareness exercise. When someone searches for a specific problem your product solves, they’re further along in the buyer’s journey than someone searching for a general industry term, and content that meets them at that moment with real expertise and a clear product connection will convert at a fundamentally different rate.

We break these keywords into a few main categories and prioritize them roughly in this order.

Category Keywords

Every enterprise content strategy should start here. These are the keywords that indicate someone is actively looking to buy or evaluate a product like yours right now. Software category keywords describe the type of product someone is searching for, and for enterprise companies, they tend to follow patterns like:

• “Best [use case] software for enterprise”
• “[Use case] platform for large teams”
• “Enterprise [use case] software”
• “[Use case] software for [industry]”

Enterprise keyword research also tends to surface modifiers that don’t appear in SMB or mid-market searches. 

Enterprise buyers search for solutions that meet compliance requirements (“SOC 2 compliant [use case] software”), specific integrations (“marketing analytics platform with Salesforce integration”), and scale indicators (“project management software for 500+ users”). The more use cases your product serves and the more verticals you operate in, the more keyword opportunities you’ll find in this category alone.

These are the keywords that most enterprise companies are bidding on in paid ads, but, in our experience, aren’t pursuing organically through blog content. The search results for many of these terms are dominated by software review sites like G2 and Capterra, which means a well-written, expert-driven article from the company itself can compete effectively by going deeper into how the product actually works and what makes it different.

Comparison Keywords

The second category of high-intent keywords we prioritize is comparison keywords, including “[competitor] alternatives” and “[brand] vs [competitor]” searches. These are valuable because the searcher is already in evaluation mode. They know which product category they need and are now deciding between specific options.

For enterprise companies, comparison keywords often look like:

• “[Competitor] alternatives for enterprise”
• “[Brand] vs [competitor] for [industry]”
• “[Competitor] vs [competitor]” (where you insert your brand as a third option)
• “Best [competitor] alternatives for large teams”

The third variation is particularly useful for enterprise companies that are newer or less well-known in their space. If two established competitors have significant search volume for their head-to-head comparison, you can create content targeting that keyword and position your product alongside both of them. This puts your brand in front of buyers who are already deep in the evaluation process, even if they haven’t heard of you yet.

Enterprise comparison content also tends to be more nuanced than what works at the SMB level. Enterprise buyers are evaluating products across multiple dimensions (security certifications, integration depth, implementation timelines, support SLAs), so the comparison content needs to address these factors specifically rather than just listing features side by side.

Use Case, Pain Point, and Jobs-to-be-Done Keywords

After covering the core bottom of funnel category and comparison keywords, we move to use case, pain point, and jobs-to-be-done keywords. These are terms where the searcher has a specific problem or operational need but isn’t yet searching for a product category by name. They’re either describing a challenge they’re trying to solve, or a specific task they need to accomplish.

For enterprise companies, pain point and use case keywords often follow patterns like:

• “How to [solve specific operational problem] at scale”
• “[Specific pain point] for [industry] companies”
• “How to manage [process] across multiple teams”
• “[Operational challenge] for [enterprise role]”

Jobs-to-be-done keywords are closely related but the distinction matters. Pain point keywords describe a problem the searcher is experiencing, while JTBD keywords describe a specific task or outcome the searcher needs to accomplish. The searcher isn’t necessarily in pain — they just have a job to do and are looking for the best way to do it. These tend to follow patterns like:

• “How to [complete specific task] for [enterprise context]”
• “Best way to [achieve specific outcome] across [multiple departments]”
• “[Specific task] template for [industry]”
• “How to automate [process] at scale”

Both types convert well because the searcher is describing something that the enterprise product directly solves, even if they aren’t aware a product category exists for it. Someone searching for “how to standardize vendor onboarding across 12 offices” might not know that vendor management platforms exist, but that search describes exactly the job your product was built to do.

This is also where selling your product in the content (which we’ll discuss in Step 3) becomes especially important — if you write a thorough article about the problem but never explain how your product solves it, you’ve done all the hard work without capturing any of the value.

Enterprise pain points and JTBD keywords are where internal knowledge from sales and customer success teams becomes critical. The best keywords in this category often come from listening to the specific language enterprise buyers use during sales calls, reading through support tickets, and understanding the operational problems that drove your largest customers to seek out a solution in the first place. Asking your CS team “what are the top five things enterprise customers use the product to accomplish in their first 90 days?” will often generate keyword ideas that no research tool would surface on its own.

We recommend enterprise companies only move to top of the funnel content after they’ve exhausted their bottom of the funnel and middle of the funnel keyword opportunities. Top-of-the-funnel content can help build topical authority and support rankings for higher-intent pages, but it should never be the starting point for an enterprise content strategy.

This is even more important now that AI search engines like ChatGPT, Perplexity, and Google AI Overviews are answering broad, educational queries directly without sending users to your site. A top of the funnel strategy that used to drive significant traffic now drives very little, and that traffic still doesn’t convert well, making it the worst of both worlds.

In contrast, bottom of funnel queries where users are asking for product recommendations are exactly where AI tools list and cite brands, which makes BOFU content valuable for both traditional search and AI visibility.

One exception to the bottom-up prioritization is worth mentioning. In the first few months of an enterprise content marketing initiative, SEO content takes time to rank, which creates a gap between when you start investing and when you start seeing organic results. 

To bridge that gap and show early traction to leadership, we produce what we call “Disruption Stories” alongside our SEO content. These are non-SEO narrative pieces that tell the story of why the company was founded, what specific problems the product was built to solve, and how it works differently from existing solutions.

We then promote these through paid channels, primarily LinkedIn, to drive traffic and conversions while the SEO content is ramping up. This gives leadership visibility into early results and buys the time needed for the organic content strategy to compound.

Step 2: Produce Content Through Expert Interviews That Deeply Satisfies Search Intent

Finding the right keywords is only half the problem. The other half is producing content that does two critical things simultaneously: 

1. Fulfill search intent enough to actually rank for those keywords
2. Is written well enough to actually  convert your ICP when they read it.

The “Google Research Paper” Problem

Most enterprise content programs get both of these parts wrong in a specific and consistent way. Whether the content is produced by in-house writers, freelancers, or agency partners, the content creation process is usually some variation of the same thing: the writer receives a keyword and a brief, researches the topic by reading what’s already ranking on the first page of Google, and writes a synthesized version of that information in the company’s brand voice.

We call this the “Google research paper” approach, and it produces content that is factually accurate but offers nothing the reader couldn’t find elsewhere. This is a problem for any company, but it’s a particular problem at enterprise scale for two reasons.

First, the people reading enterprise content are practitioners, decision-makers, and technical evaluators who are not beginners, they are advanced in their field. Their needs and questions are advanced. They don’t need a “beginners guide” to their own field or to know the “10 trends” in that field “to look out for” next year. They can immediately tell when an article was written by someone who doesn’t have hands-on experience with the subject matter.

Second, enterprise buying decisions involve multiple stakeholders, which means the content needs to be substantive enough that a champion inside the organization can share it with their team and it holds up to scrutiny.

AI-generated content has made this problem worse, not better. AI tools can produce content faster and cheaper, but the output is fundamentally the same as the Google research paper approach — it can only synthesize what already exists. It cannot produce original insights from someone who has actually built, sold, or implemented the product.

Our Solution: Internal Interview-Based Content Writing

At Grow and Convert, we solve this by building every article around a direct interview with a subject-matter expert within the client’s company. This is not a casual conversation or a quick fact-check. It’s a structured 30-45 minute recorded interview designed to extract the specific insights, examples, and perspectives that make the article genuinely different from everything else ranking for that keyword.

Before the interview, our content strategist researches the target keyword, analyzes search intent, reads top-ranking content, and identifies specific gaps and angles that existing content doesn’t cover. They then prepare a set of interview questions designed to pull out exactly those missing pieces from the expert.

During the interview, the goal is to get the expert talking about their actual experience: how the product works in practice, what specific problems enterprise customers face, how implementation typically plays out, what objections come up during the sales process, and what makes their approach different from competitors. These are the details that a freelance writer or AI tool could never produce on their own because they simply don’t have access to this information.

After the interview, the writer builds the article around these insights, weaving them into a piece that satisfies the search intent for the target keyword while containing perspectives and details that are genuinely original. We call these details “Originality Nuggets,” the specific pieces of information that make the content impossible to replicate by reading other articles or prompting an AI tool.

Writing With Specificity, Not Generalities

We also apply what we call the “Specificity Strategy” throughout the content creation process. This means replacing generic statements with concrete, specific details from the interview.

Instead of writing “our platform helps large companies streamline their onboarding process,” the writer uses the specific details the expert provided: “our platform reduced onboarding time from 6 weeks to 9 days for a manufacturing company with 12 regional offices, primarily by automating the document collection and compliance verification steps that previously required manual coordination across three different departments.”

That level of specificity is what makes enterprise content credible to practitioners, and it’s only possible when the content is built from expert interviews rather than desk research.

Enterprise content needs to demonstrate real product and domain expertise because the buyers are more sophisticated and the stakes of the purchase are higher. An SMB buyer might read a comparison article and sign up for a free trial within the same session. An enterprise buyer is going to share that article with their procurement team, their IT security team, and their VP, and every person in that chain needs to come away feeling like the company genuinely understands the problem and has a credible solution.

Interview-based content also creates a competitive moat that scales. Every article you produce through this process contains insights that exist nowhere else on the internet, which means competitors cannot replicate it and AI tools cannot synthesize it from existing sources. Over time, this builds a library of genuinely differentiated content, which becomes increasingly valuable as AI search engines begin to favor original, expert-driven content over recycled information.

Matching the Right Expert to Each Piece of Content

For enterprise companies with large teams, matching the right expert to each piece of content is also equally important. Not every article should feature the same person. The best approach is to match the expert to the keyword category:

• Product managers and engineers are the best sources for software category content, because they can explain how the product actually works, what architecture decisions were made and why, and how specific features solve specific problems at enterprise scale.
• Sales leaders and account executives are the best sources for comparison and alternatives content, because they hear directly from prospects why they’re evaluating competitors, what gaps they’ve found in other products, and what factors ultimately drive the buying decision.
• Customer success managers are the best sources for use case, pain point, and jobs-to-be-done content, because they see how enterprise customers are actually using the product day to day, which workflows they’ve built around it, and which outcomes they’re measuring.

This process ensures that every article draws from the most relevant firsthand experience available inside the company, which produces content that feels authoritative and specific rather than generic and surface-level.

It also solves a problem that enterprise content creators consistently struggle with: producing content that is both on-brand and genuinely expert-driven. When content workflows rely on writers working from briefs and existing materials alone, the output tends to be either on-brand but shallow or detailed but off-message.

The interview process bridges that gap because the writer gets the substance directly from the expert and shapes it within the company’s messaging and content management guidelines.

Step 3: Sell Your Product in Each Piece of Content

This is one of the most counterintuitive parts of our process for enterprise content teams, and it’s also one of the most impactful. Most enterprise content programs operate under an unspoken rule that blog content should educate, not sell. The assumption is that if you provide enough value upfront, the reader will eventually find their way to your product on their own. In practice, that rarely happens.

We see this play out in a very specific way across enterprise blogs. The article will do a thorough job of explaining a problem, walking through different approaches, and providing useful information. But it will never explain how the company’s own product solves that problem. It might mention the product once in a CTA at the bottom of the page, or it might not mention it at all. The content team considers this a feature — they believe that “selling” in content would undermine the reader’s trust.

We think this is a mistake, and a costly one. If someone is reading your article about vendor onboarding challenges for enterprise companies, and your product directly solves that problem, not telling the reader how it works is doing them a disservice. They came to your site looking for a solution. They’re reading your content because the keyword they searched indicated they have a real problem. The most helpful thing you can do is show them exactly how your product addresses it.

Selling your product in content doesn’t mean turning every article into a product landing page. It means weaving specific, relevant product details into the article at the points where they naturally answer the reader’s questions.

For a software category article targeting “enterprise project management software,” this means going beyond a generic feature list and explaining how your product handles the specific concerns enterprise buyers have: how permissions and access controls work across teams of 500+ people, how the platform integrates with the tools their teams are already using, how implementation and onboarding work for a company with multiple departments, and what the security and compliance posture looks like.

For a pain-point article targeting “how to reduce vendor onboarding time across multiple offices,” this means explaining the problem thoroughly, acknowledging the different ways companies try to solve it, and then walking through how your product automates the parts of the process that create the most friction. Show screenshots. Describe the actual workflow. Explain what changes for the user on day one versus day thirty.

For a comparison article targeting “[competitor] alternatives for enterprise,” this means being direct about where your product is stronger and where the competitor might be a better fit for certain use cases. Enterprise buyers are sophisticated enough to know that no product is perfect for everyone, and being transparent about tradeoffs actually builds more trust than claiming superiority across the board.

The underlying principle here is what we call “Customer-Content Fit,” which means matching the content format, depth, and messaging to where the reader is in their buying journey and what they need at that specific moment. Not every piece of enterprise content should look the same. A software category article needs detailed product walkthroughs and feature explanations. A pain point article needs problem diagnosis and workflow-level product demonstrations. A comparison article needs honest, side-by-side analysis.

This also applies to content formats beyond blog posts. Many enterprise organizations spread their marketing campaigns across case studies, white papers, webinars, podcasts, and video content without a clear strategy for how each format connects to the pipeline. The same Customer-Content Fit principle applies: case studies work best at the bottom of the funnel when a prospect needs social proof to share with their procurement team. White papers can work at the middle of the funnel when a prospect needs to understand a problem more deeply before evaluating solutions. But every format should tie back to a specific stage in the customer journey and include clear product messaging rather than treating content as a brand awareness exercise with no conversion path.

Enterprise content teams resist selling content for several reasons, all understandable but ultimately counterproductive.

The first is organizational. In many enterprise companies, the content team sits under brand or communications, not under demand generation. Their mandate is awareness and thought leadership, not pipeline, so they naturally produce content that prioritizes brand perception over conversion. Shifting this requires either changing the team’s mandate or building a content function that reports into a revenue-focused leader.

The second is the belief that buyers don’t want to be sold to. This is partially true because enterprise buyers don’t want to read a thinly disguised product pitch that provides no real value. But that’s not what we’re advocating. We’re advocating for content that provides genuine value through expert insights and thorough problem analysis, and then earns the right to explain how the product fits into the solution. When the content is good enough, talking about the product feels natural, not salesy.

The third is that many content teams simply don’t know their own product well enough to write about it in detail. This is where the interview-based process from Step 2 becomes essential. When the writer has conducted a 45-minute interview with a product manager or solutions architect, they have the specific details, language, and examples they need to talk about the product credibly. Without that interview, the writer defaults to vague descriptions pulled from the marketing site, which is exactly the kind of content that enterprise buyers ignore.

The difference between content that mentions the product and content that doesn’t is significant. Across the articles we’ve produced for clients, bottom of funnel content that includes detailed product positioning converts at rates that are dramatically higher than top of funnel content that doesn’t. 

We published the data behind this in our article on what keywords convert the highest, where we analyzed conversion rates across 95 blog posts and found that bottom of the funnel posts converted at 4.78% on average, compared to 0.19% for top of funnel posts. That’s a 25x difference, and a large part of it comes down to whether the content actually sells the product or stays deliberately vague.

For enterprise companies, where each conversion can represent a six or seven-figure deal, that difference is even more consequential. A single well-positioned article that ranks for the right keyword and clearly demonstrates how the product solves the reader’s problem can generate more pipeline than an entire quarter’s worth of top of the funnel blog content.

Step 4: Build Backlinks and Brand Mentions

Even with the right keywords and expert-driven content, enterprise articles won’t rank if the site doesn’t have the backlink profile to compete. Link building is especially important for enterprise content marketing because the keywords worth targeting tend to be competitive, and the sites currently ranking for them, software review platforms like G2 and Capterra, established industry publications, and competitor blogs with years of domain authority, have strong link profiles.

Most enterprise companies approach link building in one of two ways, and neither is particularly effective.

The first is to do nothing and hope that good content attracts links on its own. This occasionally works for top of the funnel content that gets shared on social media, but it almost never works for bottom of the funnel content because articles about “best enterprise project management software” don’t tend to go viral.

The second is to run generic outreach campaigns that blast hundreds of sites with templated emails asking for links. This produces low-quality links from irrelevant sites that do little to move rankings.

We approach link building differently. We focus on building a smaller number of high-quality, relevant links from sites that actually have authority in the space. The specifics of how we do this are covered in depth in our articles on content distribution, but the core idea is that relevant links from sites in your industry or adjacent industries carry far more weight than a large volume of links from random directories and guest post farms.

For enterprise companies, there are a few link building advantages that large companies don’t typically leverage. Enterprise brands tend to have existing relationships with industry publications, analyst firms, partner companies, and professional organizations. These relationships can be leveraged for link building in ways that feel natural rather than forced, like co-authored research reports, contributed expert commentary, partner integration pages, and industry roundtable content all generate high-quality links while also serving a legitimate business purpose.

Repurposing existing content assets, such as webinar recordings, research data, or customer interviews, into linkable digital assets can also generate backlinks without requiring new content creation. LinkedIn, in particular, is a valuable distribution channel for enterprise content because it’s where enterprise buyers and decision-makers already spend time, and content that performs well there tends to attract organic links and mentions from industry peers.

Beyond traditional link building, brand mentions, and citations across authoritative third-party sites are becoming increasingly important for AI search visibility. AI tools like ChatGPT, Perplexity, and Google AI Overviews decide which brands to recommend based partly on what we call third-party consensus — how frequently and consistently your brand is mentioned, reviewed, and cited across independent sources on the web. When multiple authoritative sites mention your product in the context of solving a specific problem, AI tools interpret that as a signal that your brand is a credible option worth recommending.

This is why we actively build third-party citations as part of our link building process. Getting your brand mentioned in relevant industry publications, software review sites, partner directories, expert roundups, and contributed articles creates the kind of consensus that both Google and AI search tools reward.

For enterprise companies, this matters because when a buyer asks ChatGPT or Perplexity “what are the best vendor management platforms for enterprise companies,” the tools are pulling from that web-wide consensus to decide which brands to include in the response. Companies that only have their own website talking about their product will lose out to competitors who have built that third-party presence.

We’ve written extensively about how to approach this systematically in our article on Prioritized GEO, which lays out our three-tier framework for improving visibility across both traditional and AI search.

Step 5: Set Up Conversion Tracking and Attribution

This is the step that most enterprise content programs skip entirely, and it’s the reason content gets treated as a cost center. If you can’t show leadership that content generated 40 qualified leads last month, or that three enterprise deals currently in the pipeline originated from blog content, then content will always be the first budget line to get cut when the company needs to tighten spending.

The mistake most enterprise content teams make is treating measurement as something they’ll figure out later, after they’ve published enough content to have something worth measuring.

We take the opposite approach. We set up conversion tracking before we publish the first article because every piece of content we produce targets a high-intent keyword where the reader has a real problem and a real reason to take the next step. If we’re not tracking whether they take that step, we’re flying blind from day one.

Enterprise content teams tend to report on the metrics that are easiest to measure: pageviews, organic traffic, keyword rankings, time on page, and social shares. These metrics are fine for understanding content reach, but they tell you nothing about whether content is actually contributing to the business. A blog post that gets 10,000 monthly visits and zero demo requests is not a successful piece of content, no matter how impressive the traffic number looks in a slide deck.

The KPIs that matter for enterprise content marketing are conversion metrics: demo requests, trial signups, contact form submissions, and sales conversations that originated from or were influenced by content. These are the numbers leadership cares about because they directly connect to the pipeline and revenue.

This doesn’t mean you should ignore traffic and rankings entirely. They’re useful leading indicators, especially in the first few months of a new content marketing initiative when content is still building rankings and it’s too early to expect significant conversion volume. But they should never be the primary metric for evaluating content performance.

For our clients, the specific attribution setup depends on the enterprise company’s tech stack, including their content management system and CRM, but the core approach is consistent. Every article needs a clear call to action that matches the intent of the keyword it’s targeting.

For bottom of the funnel content, that’s typically a demo request or free trial signup. For middle of the funnel content, it might be a consultation request or a product walkthrough. The CTA needs to be tracked as a conversion event in your analytics platform so you can see exactly which articles are generating leads.

We use GA4 conversion tracking and BigQuery as the foundation. For each article, we track the number of users who arrive from organic search, the number who click through to a conversion page (demo request, trial signup, contact form), and the number who actually complete the conversion. This gives us a clear conversion rate for every piece of content, which we can then report on by keyword category, by funnel position, and by individual article.

For enterprise companies with longer sales cycles, we also recommend connecting content attribution to the CRM. When a lead comes in through a blog post and eventually closes as a six-figure deal, that information needs to flow back to the content team so they can demonstrate the revenue impact of their work. Most enterprise CRMs (Salesforce, HubSpot) support this kind of first-touch or multi-touch attribution, but it requires intentional setup and coordination between marketing and sales operations.

When you have conversion data, the conversation with leadership shifts fundamentally. Instead of presenting a content report that says “we published 12 articles and grew organic traffic by 15%,” you can present a report that says “our content generated 47 demo requests this month, 12 of which are now in active sales conversations, and three articles account for 70% of that volume.”

That second report is the one that gets content marketing treated as a revenue driver rather than a cost center. It’s also the report that justifies continued investment, because you can show exactly what the company is getting for its content spend and which types of content are producing the best returns.

We build these reports for every client engagement using what we call ROI graphs, which plot the cumulative number of conversions generated by content against the monthly investment over time. These graphs make it easy to see when content marketing becomes profitable and how the return compounds as more articles rank and generate leads.

For enterprise companies with larger budgets and higher deal values, the break-even point is often surprisingly fast, because a single enterprise deal closed through content can pay for months of content investment.

Conversion tracking is also not just a reporting function but a strategic advantage. When you know which keywords and articles are driving the most conversions, you can double down on what’s working, adjust what isn’t, and make every subsequent content decision with real data instead of gut instinct.

Want to Work With Us or Learn More About How We Approach Enterprise Content Marketing?

We hope this article gives enterprise content teams a clear framework for building a content program that drives pipeline, not just traffic. If you’d like to take the next step, there are a few ways to work with us:

• Hire our agency. Grow and Convert is an enterprise content marketing and SEO agency that handles strategy, keyword research, content production, link building, and conversion tracking. If you want us to execute enterprise content marketing for your company, you can learn more about working with us here.
• Join our content marketing course. If you’d like to learn how to do this yourself, we teach our full content marketing process in our course and community. Members get access to detailed lessons on every step covered in this article, plus live calls where we review real content strategies and give feedback. You can learn more and join here.
• Write for us. If you’re a content writer or strategist who wants to work with us, you can learn about open positions here.

But from what we see, there’s no hard data proving these tactics actually work.

Based on data from 20+ clients, we know that exposure in LLMs comes from our Prioritized GEO strategy: ranking for traditional SEO bottom-of-the-funnel keywords with highly differentiated content.

So, we wrote this article to show an example of how we used Prioritized GEO to help Constitution Lending, a private money lender, appear in the top three AI recommendations for 50+ bottom-of-the-funnel prompts, despite having a domain authority under 40.

How LLMs Recommend Products and Services: They Search the Web When Users Ask for Recommendations

Our AI search strategy is based on a simple reality: all major LLMs search the web to help formulate responses. This process is known as grounding.

In the example below, you can see how Google AIO — the largest AI search tool today by traffic — uses our content to generate its AI summary for the query “LLC mortgage lenders.” This is an important keyword for Constitution Lending, as a large portion of their target market consists of borrowers looking to secure mortgages under an LLC structure.

In this case, Google AIO is literally an SEO summarizer (an “overview” of the search results below), so it’s influenced by traditional SEO rankings. 

Even OpenAI says that ChatGPT searches the web whenever it determines users can benefit from web information — not only when someone explicitly clicks the “search” button.

You can see this when asking ChatGPT for product recommendations. It will almost always say, “Searching the web” (as shown below).

Sometimes, it even tells you what it’s searching for: 

Perplexity also lists its sources when formulating a response, which typically correlate with articles ranking on the first page of Google.

This means AI search visibility is fundamentally linked to traditional SEO. If we rank for “best private lender,” for example, LLMs are more likely to surface our content when searching the web to provide recommendations for private lenders.

In fact, there’s a strong correlation between the keywords we rank for and our visibility in AI search results.

For example, one of our most valuable keywords is “best LLC mortgage lenders,” where we currently rank #1 on Google. We also rank for close variations, including “mortgage loans for LLC” and “mortgage lenders for LLC.”

Using Traqer, the tool we developed to track AI search visibility, we can see that Constitution Lending appears as a top recommendation in ChatGPT (Column #1), Perplexity (Column #2), and Google AIO (Column #3) when users ask for LLC mortgage lenders.

Side note: Nobody knows exactly which prompt users are entering — especially in ChatGPT — because users often have long, multi-turn conversations. As a result, tracking visibility for a single prompt isn’t very useful. Instead, Traqer is built to monitor visibility across topics, where multiple prompts approach the same topic from different angles or with different wording. 

We don’t know exactly how users will phrase a given question, but we can run a range of natural-sounding prompts and measure overall visibility for that topic. If visibility is high, we know we’ll show up regardless of how users ask. If it’s low, we know there’s work to do.

For Constitution Lending, examples include topics like “private lenders” or “LLC mortgage lenders.” We’re also ranking on the first page for “best DSCR lenders,” “top DSCR lenders,” “DSCR lenders,” and “best DSCR providers,” among other variations.

In Traqer, we can see that we’re consistently in the top 1–3 brands cited for prompts related to DSCR lenders.

Here are the exact prompts we’re tracking for that topic and where Constitution is being mentioned or cited as a source:

What’s also interesting is that we see very little AI visibility for keywords we aren’t yet ranking for. 

For example, Constitution Lending recently started offering cash for houses, and we began publishing content on that topic not long ago. Some of those articles aren’t ranking yet. When we check Traqer for “sell my house fast Connecticut,” Constitution Lending rarely appears as a recommendation, showing how a lack of SEO rankings directly correlates with a lack of AI visibility.

These are just three examples, but the pattern is consistent across different keywords and clients. When we rank well in traditional SEO for a keyword, our AI search visibility for related prompts increases. When we don’t rank well in SEO, AI search visibility is limited. 

Below, we walk through our process for ranking in traditional Google search and, as a result, gaining exposure to LLMs.

Our 4-Step AI Search Visibility Strategy for Constitution Lending

Step 1: Identify Bottom-of-the-Funnel, High Intent Keywords

At the start of the engagement, we used Pain Point SEO to identify bottom-of-the-funnel keywords that someone urgently looking for a private lender would search for on Google.

We’ve written extensively about why you should target bottom-of-the-funnel keywords in your SEO strategy. In short, bottom-of-the-funnel keywords convert 10x to 25x better than top-of-the-funnel keywords because the reader is actively looking for a solution and there’s significant buying intent. 

In Constitution Lending’s case, that means borrowing intent (and yes, these keywords are still generating traffic and leads through traditional SEO, even in today’s post-ChatGPT and post-AIO world).

For Constitution Lending, these bottom-of-the-funnel keywords included:

• Best DSCR lenders in Connecticut
• Best LLC mortgage lenders
• Best fix and flip lenders
• Fastest closing private lenders

Articles that rank for these keywords — and therefore gain AI visibility — are far more likely to convert than top-of-the-funnel keywords (e.g., what is a DSCR lender, how do I calculate DSCR), where readers still need to do research before applying for a loan.

Focusing on bottom-of-the-funnel keywords is even more important in the context of AI search visibility because these are the only topics where LLMs mention brands, products, or services in their responses. If you ask an LLM a top-of-the-funnel question like “What is a DSCR loan,” it’ll simply explain the concept without recommending or linking to any brand, because there’s no buying intent.

The old content marketing convention — where a company like Constitution Lending would write content like “What is a DSCR loan” in hopes of getting traffic — is being upended by AI search. Content like that never converted well into leads (as we’ve shown for years), but it at least used to generate traffic. Now it doesn’t even do that. 

Step 2: Prioritize Lower-Competition Keywords Using Underdog SEO

Normally, we’d start producing differentiated content to rank for the bottom-of-the-funnel keywords identified in Step 1.

However, many of the lenders we were competing against had 80+ domain authority, thousands of backlinks, and decades in the lending space. Constitution Lending, by contrast, was a relatively new website with lower domain authority. We knew we were unlikely to outrank these high-authority competitors in the short term by targeting the same bottom-of-the-funnel keywords.

Instead, we applied our Underdog SEO framework to start ranking in Google and AI search results sooner.

Underdog SEO focuses on prioritizing keywords tied to specific use cases or verticals. These long-tail, vertical-specific keywords are typically easier to rank for because larger lenders aren’t targeting them, which means there’s less competition and less tailored content on the web. Since traditional SEO is the path to AI visibility, targeting lower-competition keywords helps us get on LLMs’ radars faster.

For example:

• What we didn’t target first: Best hard money lenders

• What we targeted: Best hard money lenders for first-time investors

• Why? Only one article on the first page is specifically tailored to first-time investors. The rest cover hard money lenders broadly and simply happen to rank for “best hard money lenders for first-time investors” due to limited competition. By creating content specifically tailored to first-time investors, we’re far more likely to rank well.

These are some of the vertical-specific keywords we ended up prioritizing for Constitution Lending:

• DSCR lenders for multifamily homes
• Private money lenders for residential real estate
• Hard money lenders for first-time investors
• DSCR loans for construction
• Commercial loans for rental properties
• DSCR loans for wholesalers and brokers

Step 3: Write and Publish Highly Differentiated Content on Constitution Lending’s Website

With a list of bottom-of-the-funnel keywords prioritized using Underdog SEO, we began drafting long-form blog posts to rank for them.

A common mistake at this stage is hiring freelance writers with little to no domain expertise. This often results in what we call a Google Research Paper: generic content that simply regurgitates what’s already been said online.

This is problematic because AI effectively acts like a salesperson. When LLMs search the web, they consume your content and use it to communicate your value props and differentiators to users. In other words, the content you publish directly shapes how LLMs describe and position your solution. 

For example, below you can see ChatGPT using nearly identical language to what we published in our article:

If your content lacks detail on how you address customer pain points better than the competition, AI won’t have enough information to make a compelling case for your solution. Instead, it will default to the same generic language it uses for every other option on the market.

Additionally, AI develops a deep understanding of users (their context, intent, and pain points) through both current and past conversations. When it searches the web, it looks for solutions that best align with that context. 

If your content doesn’t clearly explain how your solution addresses those needs, LLMs won’t connect your offering to users’ unique circumstances, which ultimately limits visibility.

This means your content needs to clearly articulate your differentiators and speak directly to the pain points your target customers are likely to express to LLMs.

• How are we better than the competition?
• What problems do we solve?
• Who do we serve best?
• In which scenarios or use cases do we truly shine?

With this in mind, we interviewed experts at Constitution Lending who work with borrowers every day and deeply understand their challenges. Those conversations surfaced a few consistent themes:

• Most lenders are slow. Borrowers want fast funding.
• Many lenders reject applications at the last minute. Borrowers want early certainty that they’ll get funded.
• Many lenders require large down payments. Borrowers want lower down payments.

We then created content that directly explains how Constitution Lending solves these problems.

As a result, when a borrower tells an LLM they’re looking for a fast, low-down-payment lender that won’t reject applications at the last minute, LLMs are far more likely to recommend Constitution Lending. We’ve essentially given LLMs the exact information they need to make that connection.

Step 4: Get Brand Mentions on Other Sites That Appear as Citations

Another important component of our Prioritized GEO strategy is securing brand mentions on sites that already appear as citations for the AI prompts we want to rank for. This places Constitution Lending within the articles LLMs actively reference when formulating responses. Brands shouldn’t build citations from just any website in their industry — only from sites that LLMs actually cite.

This works through the same mechanism described earlier. When LLMs search the web for solutions that match a user’s query, appearing across multiple cited sources (including your own site) increases overall AI visibility.

That said, we haven’t begun citation outreach for Constitution Lending yet. So far, we’ve focused on publishing high-quality content on their own site, which has already driven strong AI visibility. In the near future, we may begin outreach to cited websites to secure inclusion in their articles.

Note: We suspect that the lack of brand mentions across multiple third-party sources explains why they appear less frequently in ChatGPT compared to Google AIO and Perplexity. Our data suggests ChatGPT prioritizes diverse brand mentions and places less emphasis on owned content alone. We’ll continue monitoring ChatGPT visibility as we begin citation outreach.

Results: We Appear in the Top 3 AI Recommendations for 50+ Bottom-of-the-Funnel Prompts

Without getting too deep into the specifics of Constitution Lending’s business, our content strategy focuses on four core areas:

1. Lending: Constitution Lending provides financing to borrowers purchasing investment properties.

2. Investing: Constitution Lending allows investors to purchase shares in loans in exchange for borrower interest payments.
   
3. Cash for houses: Constitution Lending buys houses directly, helping homeowners sell in as little as 24–48 hours.
   
4. Non-performing loans: Constitution Lending operates a real estate fund that invests in non-performing loans.

Lending Results

On the lending side of the business, the goal was to get LLMs to recommend Constitution Lending to users when they search for any type of real estate investment lender. 

In the screenshots below, you can see that they’re first in Google’s AI overview for numerous keywords:

If we look inside Traqer, you’ll see that they have strong visibility for several bottom-of-the-funnel prompts in ChatGPT, Perplexity, and Google AIO:

Investor Results

We also create content targeting keywords investors would search for when evaluating real estate loans. 

As a result, Constitution Lending appears as a top recommendation for bottom-of-the-funnel AI prompts such as:

We Buy Houses Results

Constitution Lending recently expanded into the houses-for-cash business, helping homeowners sell quickly at a fair price. They can purchase houses in as little as 24 hours, compared to the months it often takes with a traditional real estate agent.

Our objective, therefore, is to appear in AI search results whenever someone asks for help selling their house fast.

We only began publishing “houses for cash” content a few months ago. Even so, Constitution Lending is already being recommended for several bottom-of-the-funnel AI prompts such as “sell my house fast Waterbury” and “sell my house fast Bristol.”

Non-Performing Loan Fund Results

The non-performing loan fund is another part of the business where we only recently began publishing content. Many of the real estate investment funds we’re competing against are massive — firms like Blackstone and Vanguard that have been around for several decades.

As a result, we expected it would take several months to gain traction in AI search results. However, we’re already seeing AI search visibility for prompts where users are actively looking for a fund.

Increase Your AI Search Visibility with Our Prioritized GEO Strategy

Schedule a quick call to learn how we can help your brand increase AI search visibility for high-buying intent keywords.