For example, if you’re a software company that helps small business owners track employee time off, then a blog post or landing page written to rank for the keyword “best employee time off tracking software” is BOFU content. People searching for queries like that are ready to make a purchase.

That conversion potential is BOFU content’s primary selling point, and we’ve been preaching the benefits of bottom of the funnel content for years.

We think too many brands overfocus on top of the funnel content and would be better off prioritizing BOFU instead. We have plenty of data to back that up, including a conversion analysis of 95 articles we’ve written for clients.

We’ve noticed, however, that even when marketers publish BOFU content, they make several mistakes:

• Using the wrong criteria to pick topics. In our experience, marketers pick keywords using one of two frameworks: (a) researching what the competition is ranking for and (b) prioritizing keywords by search volume. Both criteria are worth considering as you develop your strategy, but neither is a strong enough indicator on their own when choosing which topics to write about. 
  
  • First, just because a competitor ranks for a keyword doesn’t mean that keyword is driving leads, or that it would drive leads for you. A competitor might be ranking for topics with no buying intent. They also might be ranking for BOFU topics where you cannot meaningfully differentiate yourself (i.e. topics where you’re not going to connect with your target audience).
    
  • Second, higher keyword volume doesn’t translate to more conversions. In fact, we have seen organic leads grow for our clients when targeting mini-volume or even zero-volume keywords. (Keyword volume becomes even less meaningful when your goal is to show up in AI search. No one knows what queries are being typed into AI tools like ChatGPT and Gemini, but we do know that ultra-specific writing is showing up in AI search results.)

• Writing bad content. This can negate the benefits of publishing BOFU content. For example, the content may not match search intent and fail to show up in search engines, or it may not address specific customer pain points and struggle to convert. 
  
  At Grow and Convert, we’ve always championed the importance of addressing the exact pain points your customers have, not surface-level overviews, in our content. This approach has paid off for our clients in the past, and is continuing to pay off as more customers use AI search. This is because AI search results tend to be very specific to the user, based on chat history. We wrote about this aspect in our piece on Invisible Prompts. The more specific and precise your content, the more likely LLMs and Google are going to connect you with your target audience.
  
• Not knowing how to make strategic choices based on what’s working. As you publish more content on your site, you’ll start tracking success through various metrics, such as page views, rankings, AI visibility, and new leads. But often companies focus on the wrong metrics or miss the full picture — like seeing declining Google traffic and concluding their BOFU content isn’t working, while not realizing that the same content is being recommended in AI search tools they aren’t tracking.

At Grow and Convert, we create organic channels that bring in qualified leads for brands. We do this by writing bottom of the funnel content that matches what your target customers are looking for.

We publish this content on a client’s blog so it can rank in search engines (both traditional and  AI search). This creates an organic channel that drives conversions. We’ve been doing this for a decade, across a wide variety of clients, and have a lot of data on what bottom of the funnel content types convert the most. 

In this article, we look at different types of BOFU content (with examples of content we’ve written for clients), how to find the best BOFU topics for your company, and how we write BOFU content.

Table of Contents

• 5 types of BOFU content. We cover five types of BOFU content and show actual data on how well they convert for our clients.
• How to come up with bottom of the funnel content ideas. We share how we ideate topics with new clients and why we don’t start with keywords, even though SEO is key to our process.
• How to write bottom of the funnel content. We discuss our research-based method, which lets us write high-quality, rankable content across various industries.
• Measuring your BOFU content and making data-informed decisions. We cover what metrics to track — including conversions, rankings, and AI visibility — and how to use that data to decide which content to double down on, re-optimize, or deprioritize.

5 Types of Bottom of the Funnel Content Shown to Drive the Most Leads

To help us find out what type of BOFU content converts the most, we analyzed 95 articles across clients, totaling over 123,000 organic pageviews and 4,687 conversions. 

Across that dataset, we identified five types of content: 

1. Category keywords
2. Side category keywords
3. Category keywords with a layer of specificity 
4. Comparison and alternatives keywords
5. Jobs to be done keywords

(Read more about the conversion rates across these keywords.)

1. Category Keywords (4.85% Average Conversion Rate)

These are queries that describe exactly what you offer. Think of topics like “best accounting software” or “men’s running shoes.” For Grow and Convert, a category keyword would be “content marketing agency,” because that’s what we are.

Most people don’t expect blog posts to convert at multiple percentage points, but our data shows they can when they rank for the right keywords and are written to sell the product. For example, Geekbot, a past client and a solution for running online standup meetings, ranks #2 for “Slack standup bot” via a post we wrote for them, and that single blog post has a lifetime conversion rate of 8.36%.

To be clear, this is extremely high, and essentially unheard of for a typical blog content strategy (which heavily skews to top of funnel content). For example, in one of our clients we compared the conversion rate of BOTF vs. TOF content. 

Their TOF content converted at just .19%, while their BOTF content had a 4.78% conversion rate.

We’ve seen this pattern across clients: when you target variations of your core product keywords, most variations convert well. A video marketing client we ranked on page one for app, software, and service variations of their category saw conversion rates of 5.73%, 3.31%, and 3.00%, respectively. Not every variation will convert equally, but category keywords consistently convert at 3–5% or higher across our clients.

When you’re developing your BOFU content strategy, you want to identify your main category keywords.

2. Side Category Keywords (1.94% Average Conversion Rate)

These are queries that show buying intent for a secondary feature or use case — not your core offering. Many companies build content to promote these offerings, and while these keywords don’t usually convert as well as your main category keywords, they still can drive leads.

For example, one of our clients sells digital asset management (DAM) software. Based on our conversations with their sales team, we wrote a BOFU post on storing video assets, a specific use case within their broader product. That post converts at 1.45%. That’s still far higher than typical top of funnel content, but their posts targeting general DAM keywords convert at 3%, 6%, and even 15%. The difference comes down to fit: users who only need video storage could opt for something simpler, whereas users searching for general DAM software need exactly what this client offers.

3. Category Keywords + Layer of Specificity (2.96% Average Conversion Rate)

These are queries that add a word or two that make them more specific to a particular feature, use case, or customer type. For example, adding “for small business” to “best accounting tools,” or adding “for trail running” to “men’s running shoes.” You’re targeting searchers who already have some knowledge of the category and are searching for a very particular solution.

Here’s a recent example from our client that works in the private education space. Their main category keywords are topics like “best private schools in california” or “best private schools in the US.” But a key part of their offering is working with students that have learning differences, such as dyslexia, ADHD, and autism.

For this client, we targeted the topic “dyslexia private schools.” That’s a more specific query than “best private schools.” It has a smaller audience and smaller search volume, but it’s been a major driver of conversions for our client. It has an average conversion rate of 1.11%.

Taking this learning, we also targeted “private schools for autism,” which while bringing in less traffic, has brought in more conversions with a higher average conversion rate of 2.32%.

Note this kind of specificity has an additional benefit for AI search, where LLMs recommend products that are highly specific solutions to the users situation or problems. More on this below.   

4. Comparison and Alternatives Keywords (8.43% Average Conversion Rate)

These are queries that mention competitor names, indicating the searcher is aware of other brands in your industry, is looking for alternatives, and could be a good candidate for your solution. This includes keywords like “salesforce alternative” or “Salesforce vs. Pipedrive.”

These were the highest converting keyword types in our analysis. This makes sense: these searchers already know about your industry, are familiar with competing products, and are actively evaluating options with a serious intent to buy.

But you want to be strategic about which competitor keywords you target. From our data, keywords mentioning direct competitors convert significantly better than those featuring loosely related competitors. Although the average conversion rate is 8.43%, the majority of the 23 competitor keywords we analyzed actually converted at less than 4%. The closer the competitor is to your product, the higher the conversion rate.

That said, even conversion rates between 1–4% are far higher than top of funnel content. And many comparison and alternative keywords have very low search volume — but as we’ve seen with our analysis of mini-volume keywords, the high conversion rates make them well worth targeting.

VS Keywords

These are a specific flavor of comparison keywords — queries comparing two solutions head-to-head, like [Brand #1] vs. [Brand #2]. The searcher is trying to decide between two specific options they’re already considering, which is why these converted at 5.45% in our analysis.

5. Jobs to Be Done Keywords (2.44% Average Conversion Rate)

These are queries that describe a job to be done (which is a problem to be solved) that your product offers the best solution for. These are mostly “how to” queries: how to organize design files, how to do a poll in Slack, how to get video testimonials from customers, etc. Although these users may not be aware of your brand or competitors, they still have the potential to buy because they have a job or problem that you solve.

While these types of keywords have a lower conversion rate than the above buckets, 2.44%, they still convert leagues higher than typical top of funnel blog content. Also these JTBD keywords often have higher search volume, which means more traffic. 

And when the job in the query maps directly to what your product does, these keywords can convert incredibly well. For example, “collect video testimonials” — a keyword we targeted for a video marketing client — converts at 9.71%, because the searcher needs software to do that exact task and our client’s product is built for it. 

The takeaway is that looking at the highest average conversion rate and focusing only on those keyword types is not a strategy. There is a large variety in what converts well, based on the client and the types of customers they serve.

How to Come Up with Bottom of the Funnel Content Ideas for Your Business

Often agencies approach content ideation in one of two ways: they open a keyword tool and target whatever keywords have the highest search volume, or they look at what competitors are ranking for and try to cover the same topics. 

Neither approach is wrong on its own, but when your goal is to build an organic channel that generates qualified leads, they’re not the best starting points. High-volume keywords may not align with what your ideal customers are actually searching for when they’re ready to buy. And just because a topic works for a competitor doesn’t mean it’s the right fit for your business — their customers, positioning, and strengths may be completely different from yours.

Instead, the more effective approach is to start by asking: what problems do our customers have that our product solves?

At Grow and Convert, we begin every new client engagement with a series of interviews with people from different departments,  sales, customer success, founders, anyone who has direct interaction with customers. The goal is to understand the details of the product, the ideal customer personas, and most importantly, the specific pain points that the product solves.

Outside of speaking directly with customers, this is the fastest way to identify topics that resonate with potential buyers and naturally position your product as the solution.

These interviews are what allow us to choose topics based on how your customers actually search, rather than defaulting to whatever has the highest volume, mimicking what competitors are doing, or even relying on average conversion rates amongst types of BOFU content.

For example, one of our clients made a straightforward employee time off tracking tool for small business owners. Through our interviews with their team, we learned that most of their customers had been managing time off in spreadsheets and physical wall calendars. They weren’t coming from competing software. That insight changed the entire content strategy. Instead of prioritizing comparison and alternative keywords, which have the highest average conversion rate in our data, we focused on JTBD keywords like “how to track employee time off,” because that matched how their customers were actually searching. It worked: we grew a profitable organic channel for them by targeting keywords that matched their specific customers’ pain points.

Find Keywords That Correspond to Your Topics

Once you have topic ideas grounded in customer pain points, you can plug them into keyword research tools like Ahrefs or Semrush to check search volume and keyword difficulty. The role of keyword tools in our process is to confirm that people are actually searching for the topics you’ve identified, not to generate the topics in the first place.

The most important thing to resist here is letting search volume override buying intent. A keyword with 200 searches per month and high buying intent will almost always outperform a keyword with 10,000 searches per month that attracts people who are just learning about a topic. We’ve seen this repeatedly in client data: the posts that drive the most leads are rarely the ones with the highest traffic.

That said, not every topic you identify needs a Google keyword to be worth pursuing. With more customers using AI tools like ChatGPT and Gemini to find products and services, there’s demand happening in conversations that no keyword tool will ever show you. 

As we mentioned above, LLMs recommend products based on the specificity and relevance of your content to a user’s situation, not based on whether you targeted a high-volume keyword (though we do see a positive correlation between ranking #1 for a keyword and showing up in AI results). So if your interviews surface a pain point that your product solves well but there’s no obvious keyword to match it to, it can still be worth producing content around that topic.

We’ve seen this with clients whose content is now showing up in AI search results for prompts we never could have predicted. We call this new type of search query answering Invisible Prompts, and it’s a big part of why we’ve developed a topic-based approach to GEO that doesn’t rely on keyword volume.

How to Write Bottom of the Funnel Content That Ranks and Converts

Choosing good high-intent keywords to target is the most important step but a close second in importance is how these pieces are written. If the content itself is generic or surface level (for example a very basic listicle of top software in a category) it risks you (a) not ranking and (b) not impressing or converting visitors that do land. 

We’ve noticed this is actually extremely common, and we think it happens because most writers — and now most AI tools — don’t have deep enough knowledge of the product or customer to write anything original. Without that knowledge, they default to copying what’s already ranking in the SERP, which produces content that says the same thing everyone else is saying.

At Grow and Convert, we use a content writing process that helps us overcome these challenges.

• We interview subject matter experts. For every piece we write, we interview subject matter experts from within our client’s organization — sales leads, founders, product managers, whoever knows the product and customer best. This is the step most content teams skip. Without it, writers default to “Google research papers” — reading what competitors have published and repackaging it into a new article that says nothing original. The interview surfaces the specific details, positioning arguments, and customer insights that make a piece actually useful to a buyer. (Learn more about our content writing process here.)

• We conduct a SERP analysis. Before writing, we analyze what’s currently ranking on Google for the target keyword. We review the types of content on page one (listicles, how-to guides, landing pages), identify the subtopics and themes that come up repeatedly, and assess what existing results do well and where they fall short. This tells us what our piece needs to cover to compete, and where there’s an opportunity to differentiate. (Learn more about our SERP analysis process in our article on SEO Content Writing.)

• We use the Grow and Convert Questionnaire, not a formulaic template, to help us outline the piece. Most content briefs focus on logistics — word count, how many internal links to include, which heading tags to use, what Grammarly score to hit. But that kind of information doesn’t help a writer produce great content that will rank and convert.
  
  Our questionnaire is fundamentally different. After the interview, writers answer a series of questions designed to help them synthesize what they learned from the interview and their SERP analysis. Our Questionnaire focuses on understanding the target audience at a level deeper than “our audience is a startup looking for accounting software” and understanding the SERP. 
  
• We draft the piece. The draft is written with the product woven throughout, not just mentioned in a CTA at the end. This means opening with the customer’s pain points, explaining the product’s positioning early to grab the right reader’s attention, and highlighting differentiating features as the foundation of the article. For example, when we wrote a piece for TapClicks targeting “paid search dashboard,” we structured the entire post as a walkthrough of how to create a dashboard using their product, which let us naturally showcase the features that made them different from competitors. (Learn more about our writing style in our post on How to Improve Your Content Writing.)

We’ve also built an AI writing tool, Wave Writer, that helps with this process. Unlike other AI writing tools that only learn your brand voice, Wave Writer ingests your business’s products, features, positioning, and customer pain points. It analyzes a SERP for any given keyword and suggests angles tailored to your business, produces outlines grounded in your actual arguments, and drafts content that says something substantive.

We used Wave Writer to do an SEO analysis for this very post, which you can view here, along with my comments on the output.

Measuring Your BOFU Content and Making Data-Informed Decisions 

As discussed in this article, writing BOFU content is a good strategy to grow your organic channel. When done correctly — that’s writing about the right topics and writing content that ranks — your BOFU content can lead to new leads, free trial signups, demo requests, new customers. 

But a key aspect of developing your BOFU content strategy is knowing what types of content to double down on, and what to deprioritize. At Grow and Convert, we track three things to create a full picture of your organic channel: conversions (our primary metric), keyword rankings and traffic, and AI visibility. 

• Conversions. This is our primary metric. Traffic and rankings matter, but only because they lead to conversions — new leads, signups, demo requests. We track conversions from each piece of content so we can see which posts are actually driving business results, not just pageviews. (Learn how we track conversions in our article on measuring conversions in GA4.)

• Keyword rankings and traffic. Rankings and traffic tell you where you’re positioned, what’s growing, and where you’re slipping. For example, when one client saw a dip in conversions despite still ranking for their target keywords, we discovered they had lost rankings for secondary keywords — related keywords their posts had been ranking for that were quietly driving conversions. Re-optimizing 22 posts brought monthly conversions from 171 to 258. (Read the full process in our article on how to find and use secondary keywords to increase conversions.)

• AI visibility. Traditional search metrics don’t capture whether LLMs are recommending your brand. We track AI visibility at the topic level, not the individual prompt level. Our client Constitution Lending now appears in AI search results for 50+ bottom of the funnel prompts across ChatGPT, Perplexity, and Google AI Overviews — competing with lenders that have been in the market for decades. There’s still a gap in attribution here — it’s more difficult to track customers who found you through AI search — but visibility is the leading indicator. (Read more about our topic-based approach to GEO.)

Over the past eight years, we’ve built organic channels for clients across dozens of industries — from SaaS to healthcare to private education — using the process described in this article. Our BOFU content has driven thousands of conversions for clients, with conversion rates routinely hitting 3-10% on individual posts.

Our clients are now showing up in AI search results alongside competitors with decades of industry presence. If you want to build an organic channel that drives real leads, learn more about working with us.

Their goal was simple: generate qualified leads from both traditional SEO and AI search. 

To achieve this, we: 

• Applied Pain Point SEO to rank for high-buying-intent keywords in Google
• Built a GEO Topic Map that teaches LLMs when to recommend them in AI search

So whether prospects search on Google or ask LLMs (like ChatGPT or Claude) about survey software, they have a strong chance of being recommended as a solution.

Below, we explain the principles behind our GEO and SEO strategy and share examples of our results.

Our Strategy for Gaining Both Traditional SEO & AI Search Visibility for Our Client

The core principle of Pain Point SEO is prioritizing high-buying-intent keywords over the top-of-funnel informational content most brands focus on. Our data shows these bottom-of-funnel keywords convert 10x to 25x better than low-intent queries.

For example, instead of targeting keywords like “What is a net promoter score” or “What is a focus group,” where the reader still has a lot of learning to do before buying, we prioritize keywords like “best survey software for enterprises” or “best customer research tools.”

The same principles apply to AI search visibility, since LLMs rely on web results when users ask for product or service recommendations. But there’s an important distinction.

Unlike Google, where people type short, predictable queries, LLM conversations are longer, more detailed, and often informed by prior interactions. In many cases, these systems have weeks or months of context about a user, which they factor into their recommendations.

As a result, LLM recommendations are more personalized than Google search results.

Take two users evaluating our client’s survey software: a startup running feedback loops with early users and an enterprise looking for affordable scale. If both search “best survey software” on Google, they’ll see similar results. But the same query in an LLM would factor in previous conversations, and the recommendations would differ based on each user’s context.

In AI search, it’s less about ranking for well-defined keywords and more about being a brand the model recommends when a relevant topic comes up. 

That’s a fundamentally different challenge than ranking for short keywords. LLMs need enough context to make a compelling case for your product — and that context has to be relevant to each user’s situation.

We call this approach Topic-Based GEO.

Topic-Based GEO argues that to maximize AI search visibility, you need published content that gives LLMs what they need to position your product as the solution to specific use cases and situations (“topics”).

We visualize the entire universe of topics relevant to your business as a map and call it your GEO Topic Map. 

To build it, we start by brainstorming every use case and pain point someone might ask an LLM about survey software.

The result is our Topic Map: a body of content that addresses every conversation or situation in which an LLM might reasonably recommend our client’s survey software.

For example, our client works with:

• Startups using survey software to gather early customer feedback
• Enterprises with strict security requirements
• DIY market researchers looking for affordable tools

We then created highly specific content for each of these use cases. Below, we share our results.

Examples of Keywords We’re Ranking For & Our AI Search Visibility

Example 1: “DIY market research tools”

One of the first articles we published for our client targets conversations where a user is asking about DIY market research tools, a common use case among their ideal customers.

If we look inside Traqer.ai — the tool we developed to track AI search visibility — we’re showing up in the top 5 positions in ChatGPT, Perplexity, and Google AIO for the topic “DIY market research tools.”

In Traqer, we also see that whenever someone enters a prompt about DIY tools for market research, our article is cited more than any other.

As a bonus, we’re ranking 4th for “DIY market research tools” on Google and generating leads from organic search.

Example 2: “CPG market research companies”

We also want LLMs to recommend our client’s survey software when someone asks for CPG market research companies, since CPG (consumer packaged goods) brands are among the heaviest users of market research.

So, we published a highly differentiated article on “CPG market research companies” that shows how our client’s survey software addresses the specific pain points of CPG brands.

In Traqer, we can see that we’re in the top 3 recommendations in ChatGPT, Perplexity, and Google AIO for prompts related to CPG market research companies.

LLMs also pull from our article more than any other piece on this topic.

We’re also currently ranking 1st for the keyword “CPG market research companies” on traditional Google search.

Example 3: “Enterprise survey software”

A large percentage of our client’s customer base is enterprise clients looking for software that scales well and doesn’t become increasingly expensive as they add users. So, we knew that enterprise survey software is an important topic to show up for on LLMs.

In Traqer, you can see that we consistently rank in the top 5 for queries related to enterprise survey software.

Our article is also the source AI cites most often when users ask about enterprise survey software.

On top of that, the article we published targeting this keyword is ranking 2nd on Google.

These are just a few examples, since we can’t realistically cover every topic we have visibility for in AI search.

Overall, the topic map we built gives our client AI search visibility across 100+ high-buying-intent topics.

Our Client’s AI Search Visibility Versus Competitors

We also think it’s worth comparing our number of AI citations with those of competitors, as this helps put the effectiveness of our AI visibility strategy into context.

As shown in the screenshot below, our article is cited nearly twice as often as SurveyMonkey, Involve.me, and Quantilope on the topic of “AI survey tools.”

On the topic of “enterprise survey software,” AI tools are citing our client more than competitors like SurveyLab and Zonka Feedback.

The same is true for “conjoint analysis software.” We have more citations than Conjointly, OpinionX, and Qualtrics.

These are just a few examples, but the pattern holds across most topics. For most high-buying-intent prompts, AI tools cite our articles more frequently than those of our competitors.

Key Takeaways

This case study highlights two key takeaways for brands looking to appear in LLMs:

1. Prioritize high-buying-intent terms your target audience is likely to use in LLMs. This increases the chances your content is surfaced and cited when generating responses.
   
2. When writing articles targeting high-buying-intent keywords, be extremely detailed about your value proposition, differentiators, and why someone should choose you. This gives LLMs the context they need to present your solution clearly and persuasively.

Improve Your AI Search Visibility with Grow and Convert

Book a call with us to learn more about how we can help your brand show up for high-buying-intent queries in LLMs.

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.