Of all the verticals we’ve worked in, fintech companies deal with everything a typical B2B content program deals with, plus a set of challenges that are specific to financial services, which most marketers or agencies have never had to work through.

Fintech content has to satisfy YMYL (Your Money or Your Life) standards, which means Google holds it to the highest quality bar of any content category. Fintech companies also compete for rankings against publishers like NerdWallet, Bankrate, and the content teams at major banks, all of which have domain authority that’s nearly impossible to match head-on.

On top of that, at many fintech companies, content goes through a compliance review, which slows production and limits what you can say. And the audience, whether it’s a CFO evaluating a payments platform or a consumer comparing lending products, may be skeptical of vendor content about financial products.

We’ve seen most fintech companies respond to these challenges by doing exactly what doesn’t work:

• They publish high-volume, top of the funnel educational content because those keywords have the highest search demand and feel safe from a compliance standpoint
• They avoid mentioning their product in blog content because the compliance team is more comfortable with content that doesn’t make specific claims, and
• They measure success by traffic and publishing cadence because they don’t have attribution set up to connect content to funded accounts, demo requests, or applications

In this article, we’ll walk you through how our content marketing strategy applies specifically to fintech companies and addresses each of these challenges. We’ll cover how to: 

• Prioritize high-buying-intent keywords that fintech companies can actually rank for
• Produce content through expert interviews, not just online research
• Sell your product in each piece of content without triggering compliance issues
• Optimize for AI search visibility in financial services
• Set up conversion tracking that connects content to revenue

Grow and Convert is a content-focused SEO and GEO agency that works with fintech and financial services companies. If you want to hire us to handle your fintech content marketing, you can learn more about working with us here.

Step 1: Prioritize High-Buying-Intent Keywords That Fintech Companies Can Actually Rank For

Most fintech companies and marketers we’ve spoken to build their content calendars by pulling a list of keywords from Ahrefs or Semrush and sorting them by search volume. The result is a blog full of glossary-style content that large finance publishers like NerdWallet, Bankrate, and Investopedia have been ranking for years and are not easy to replace. 

The problem is that most people searching for these keywords are not really ready to apply to the service, schedule a demo, or open an account.

At Grow and Convert, we take a completely different approach. Instead of sorting keywords by search volume, we look for buying intent and target keywords where the searcher already knows they have a problem and is actively looking for a product or provider to solve it. We call this Pain Point SEO, and it’s the foundation of our approach to content marketing for the fintech companies we work with.

Here are the types of keywords we prioritize for fintech clients, roughly in the order we’d target them.

Category Keywords

These are some of the highest-intent keywords available because the searcher is explicitly looking for a type of product or provider. We start here because these keywords capture people who are ready to evaluate and choose a solution. 

Across our clients, bottom of the funnel content targeting these types of keywords converts to qualified leads at roughly 25x the rate of top of the funnel content.

They follow patterns like “best [product type] for [audience or use case]” or “top [financial service] platforms.”

For a lending company, this means keywords like “best DSCR lenders for rental property investors” or “top hard money lenders for fix and flip.” 

For a payments company, it’s “best payment processing platform for SaaS companies” or “top B2B payment automation software.” For a banking infrastructure company, it’s “best KYC compliance software” or “top banking-as-a-service platforms.”

Comparison Keywords

These are keywords where the searcher is evaluating your product against a specific competitor or looking for alternatives. 

They follow patterns like “[competitor] alternatives,” “[brand] vs [brand],” or “companies like [competitor].”

According to our client data, these are some of the highest converting keywords anyone can publish.

Comparison research tends to be more thorough in fintech than in other industries because the switching costs are high. 

A business choosing a payment processor or a borrower selecting a lender is making a decision they’ll live with for months or years, and the financial consequences of choosing wrong are real. That means people spend more time on comparison queries before making a decision, and content that answers those queries with genuine depth earns more trust than a surface-level feature table.

A payments company competing with Stripe might target “Stripe alternatives for high-risk merchants” or “Stripe vs [brand] for subscription billing.” A lending company might target “[competitor] alternatives for investment property loans” or compare specific rate structures and closing timelines that differentiate them from competitors.

Use Case Keywords

These keywords describe a specific scenario or application of your product. The searcher knows what they’re trying to do and is looking for the right tool or approach. They follow patterns like “how to [accomplish specific financial task]” or “[product type] for [specific situation].”

In fintech, use case keywords tend to be highly specific because financial products are applied in narrow contexts. 

“How to finance a short-term rental property” is a use case keyword for a lender. “How to automate invoice reconciliation” is a use case keyword for a payments or accounting platform. “How to accept ACH payments for recurring billing” is a use case keyword for a payment processor.

What makes these different from educational keywords is that the searcher already has a specific situation in mind. They’re trying to figure out how to do something, and they’re open to a product that helps them do it.

Template and Checklist Keywords

These keywords indicate someone is looking for a practical resource to help them execute a specific task. They follow patterns like “[financial document] template,” “[process] checklist,” or “[calculation] calculator.”

A lending company might target “DSCR calculation spreadsheet” or “rental property cash flow template.” A compliance-focused fintech might target “KYC onboarding checklist” or “AML compliance audit template.” A payments company might target “payment reconciliation template” or “chargeback dispute letter template.”

These keywords work well in fintech because the audience is often looking for something they can use immediately, and the content gives you a natural opportunity to show how your product automates or simplifies the manual process the template addresses.

Pain Point Keywords

These are keywords where the searcher is expressing a specific frustration or problem. They tend to be phrased as questions or problem statements rather than product searches. They follow patterns like “why does [financial problem] happen,” “how to fix [financial issue],” or “how to reduce [cost or friction].”

A payments company might target “how to reduce payment processing fees” or “how to lower chargeback rates.” A lending company might target “why do hard money loans take so long to close” or “how to get approved for an investment property loan with no W2.” 

These searchers have a real problem and are looking for a solution. Content that diagnoses the problem accurately and explains how your product addresses it converts well because the reader can see the direct connection between their pain and your product.

Jobs-to-be-Done Keywords

These keywords describe the outcome the searcher wants to achieve rather than the product category or problem. They follow patterns like “[desired outcome] for [audience]” or “how to [achieve result] as a [role].”

Jobs-to-be-done keywords capture searchers who are thinking about their goal rather than the tool they need to get there. “How to build a rental property portfolio with no money down” is a JTBD keyword for a lender. “How to get paid faster as a freelancer” is a JTBD keyword for a payments platform. “How to track expenses across multiple business entities” is a JTBD keyword for a financial management platform.

These keywords are valuable because they meet the searcher where they are in their thinking, before they’ve started evaluating products but after they’ve committed to achieving a specific result. 

Step 2: Produce Content Through Expert Interviews, Not Desk Research

Most fintech content writing processes work like this: a content marketer or freelance writer is assigned a keyword, reads the articles already ranking for it, and produces a synthesized version of what they say.

The result is an article that covers the same ground as everything else on page one, adds nothing the reader couldn’t find elsewhere, and reads as if it were assembled by someone on the outside looking in.

This is what we call the “Google Research Paper” approach, and it’s a problem in every industry. But in fintech, it’s especially damaging because the audience knows more than the writer.

A CFO evaluating payment infrastructure has spent years dealing with interchange fees, PCI compliance, and failed payment recovery. A real estate investor comparing DSCR lenders understands debt service coverage ratios, prepayment penalties, and recourse structures at a level no generalist writer can fake. A compliance officer researching KYC automation tools can tell within two paragraphs whether the person who wrote the article has ever actually built a compliance workflow. When the content doesn’t reflect that level of knowledge, they don’t trust the company behind it, and they don’t convert.

The fix isn’t hiring writers with financial services backgrounds, because (1) even experienced writers are still working from secondhand knowledge when they research a topic on Google and (2) just because a writer has experience with an industry, it doesn’t mean they know your brand’s arguments or takes on that industry. You need the expertise, ideas, arguments, and strong opinions from within your company expressed in your content to ensure it’s high quality. 

To accomplish this, at Grow and Convert, we build every article around interviews with people inside the company who actually do the work. For a lending company, that means talking to the head of originations about what borrowers get wrong in their applications, or talking to the underwriting team about why certain deal structures get declined. For a payments company, that means interviewing the solutions engineering team about the integration challenges their enterprise clients face, or talking to account managers about the specific pain points that come up on sales calls.

These interviews surface what we call “originality nuggets,” which are specific details, examples, and insights that only come from doing the work. An article about DSCR lending written from Google research will explain what a DSCR ratio is and list some general requirements. An article built from an interview with a loan officer will explain why a 1.25x DSCR requirement works differently for a short-term rental property than a long-term rental, what seasoning requirements actually mean for a borrower’s timeline, and which deal structures lenders are most likely to approve in the current rate environment. 

That second article ranks better because Google rewards depth and expertise, converts better because the reader recognizes real knowledge, and can’t be replicated by competitors who are still writing from desk research.

This production process also solves a problem that’s specific to fintech content. In regulated industries, writers who research topics on their own often end up making claims that compliance teams have to flag and rewrite. 

For example, an article about lending products might include rate comparisons that aren’t accurate, or an article about investment platforms might make performance claims that violate SEC guidelines. 

When the content is built from interviews with internal experts, the information is accurate from the start because it comes from the people who understand the regulatory boundaries. This also makes compliance reviews faster and produces fewer rewrites because the source material is already grounded in what the company can and can’t say.

For category and comparison content, the best interview sources are account executives and sales engineers because they hear competitive objections every day and know exactly how the product stacks up. 

For use case and pain point content, product managers and customer success leads are the strongest sources because they understand how the product solves specific problems in practice. 

For compliance-related content, interviewing the compliance team directly gives the article a level of regulatory precision that no external writer could produce on their own.

Step 3: Sell Your Product in Each Piece of Content Without Triggering Compliance Issues

Most fintech content teams treat blog content as a brand awareness exercise. We’ve seen many top performing articles deliberately avoid mentioning the company’s product, explaining specific features, or making any claims about how the product compares to alternatives.

The reasoning is usually some version of “we don’t want to be too salesy” or “compliance won’t approve content that makes specific product claims.”

The result is an article that does all the hard work of ranking for a high-intent keyword, gets in front of a reader who has a real problem, and then fails to explain how the company’s product solves that problem. The reader finishes the article, learns something general about the topic, and leaves without any reason to take the next step. The content generated traffic but not pipeline, and the team points to pageviews in their monthly report instead of leads.

This is the opposite of what high-intent content should do. If someone searches “best payment processing platform for SaaS companies” and lands on your article, they are actively looking for a product like yours. Not mentioning your product in that article isn’t being helpful or objective but leaving the reader without the information they came for.

In fintech, every product claim has to be accurate and substantiated. You can’t say your lending platform offers “the lowest rates in the industry” unless you can prove it. You can’t claim your payment processor has “99.99% uptime” unless you have documentation to back it up. You can’t make forward-looking statements about returns if you’re in the investment space. These constraints are real, and they’re the reason most fintech content teams default to generic, claim-free content.

But compliance constraints don’t mean you can’t talk about your product at all. They mean you have to be specific and accurate instead of vague and aspirational. And that’s actually a better approach to selling in content anyway.

Instead of writing “our platform offers industry-leading rates,” you write about the specific rate structures you offer for specific deal types and explain how they compare to what borrowers typically see in the market. Instead of writing “our payment processing is faster than the competition,” you explain the specific technical architecture that reduces settlement times and walk the reader through what that means for their cash flow. Instead of writing “our compliance platform simplifies KYC,” you describe the specific workflow steps the platform automates and explain how that reduces onboarding time from weeks to days.

This kind of product selling actually makes compliance review easier, not harder. When product claims are specific and factual rather than superlative and vague, compliance teams have something concrete to verify. “We offer DSCR loans with a minimum ratio of 1.0 and no tax return requirement” is a verifiable claim that compliance can approve. “We offer the best DSCR loans on the market” is the kind of claim that gets flagged and rewritten.

The structure we recommend is to weave the product mention into the section of the article where it’s most relevant to the reader’s question. In a category article targeting “best DSCR lenders for rental properties,” the product appears as one entry in the list alongside competitors, with the same level of detail given to each. In a use case article targeting “how to finance a short-term rental property,” the product appears in the section where you explain the lending options available, because that’s where the reader expects to see specific solutions. In a pain point article targeting “why do hard money loans take so long to close,” the product appears when you explain what causes delays and how certain lenders have eliminated them.

In each case, the product mention isn’t an interruption but answer to the question the reader came with. When the content is built around a high-intent keyword and the product genuinely solves the problem that keyword represents, selling is just good content.

Step 4: Optimize for AI Search Visibility in Financial Services

There’s a growing conversation around how to optimize content for AI search engines like ChatGPT, Perplexity, and Google AI Overviews. Most of the advice in this space treats AI search optimization as if it requires an entirely new set of tactics, separate from traditional SEO. We’ve written about why we think most of that advice is wrong in our article on AI SEO strategy.

The short version is that AI search engines pull from traditional search results to generate their responses. When a user asks ChatGPT or Perplexity “what’s the best DSCR lender for investment properties,” the AI tool searches the web, finds the pages that rank for related queries, and synthesizes a response based on what those pages say. If your content ranks well for that query in Google, you’re likely to be cited and recommended in the AI response. If it doesn’t, you’re invisible.

This is why the keyword strategy we outlined in Step 1 is the foundation of AI search visibility, not a separate tactic. The same bottom of the funnel content that drives organic leads from Google also drives AI search recommendations, because both systems are evaluating the same content.

We’ve seen this play out directly with Constitution Lending, a financial services client we work with. Constitution Lending has a domain authority under 40, which means they can’t compete with major financial publishers on broad, educational queries. But because we focused their content strategy on bottom of the funnel keywords across their lending, investing, and real estate business lines, they now appear in the top AI search recommendations for over 50 bottom-of-funnel queries.

The reason this works is that AI search tools behave very differently depending on the type of query. For a top-of-funnel question like “what is a DSCR loan,” ChatGPT simply explains the concept in its own words. It doesn’t need to cite any source or recommend any brand, because the answer is factual and widely available. The user gets their answer and never visits a single website.

But for a bottom of the funnel query like “best DSCR lenders for multifamily properties,” the AI tool can’t just explain a concept. It has to recommend specific companies, and to do that, it needs sources. It looks for content that evaluates and compares lenders, and it pulls its recommendations from the pages it finds. If your content is one of those pages, you get recommended by name. If your content only covers educational topics, you never appear in these responses.

This is what makes the top of the funnel content strategy so damaging in the AI search era. It was already converting poorly in traditional search, but at least it drove traffic to your site. Now, AI tools are answering those broad questions directly, which means the traffic is disappearing too. A fintech company that built its entire content program around educational keywords is losing on both fronts: no conversions and declining traffic.

We call our approach to AI search optimization Prioritized GEO, and it’s built on this connection between traditional search rankings and AI search visibility. Rather than chasing unproven tactics like llms.txt files, key takeaway sections, or short-form blog posts optimized for AI crawlers, we focus on ranking for bottom of the funnel queries in traditional search across different core topics, which in turn drives AI search visibility.

For fintech companies, this connection between SEO and AI search is especially valuable because of the trust problem we described earlier. When ChatGPT or Perplexity recommends a financial product or service by name, it carries a level of implicit endorsement that traditional search results don’t. A user who sees your lending company recommended in a ChatGPT response alongside two or three competitors is starting from a different level of trust than someone who clicks a blue link on page one of Google. 

And because AI search is still early enough that most fintech companies haven’t optimized for it at all, the competitive window is wide open for companies that move now.

Step 5: Set Up Conversion Tracking That Connects Content to Revenue

Most fintech content teams report on metrics that don’t connect to the business. They track pageviews, time on page, keyword rankings, and sometimes email signups or whitepaper downloads. When leadership asks whether content marketing is working, the team puts together a report showing traffic growth and a list of articles published that month.

The problem is that none of those metrics tell you whether content is generating funded accounts, demo requests, applications, or any other action that the sales or product team actually cares about. Traffic can grow every quarter while pipeline contribution from content stays flat, and without conversion tracking, nobody notices until the budget review.

This gap between content activity and business results is why content marketing gets treated as a cost center in most fintech companies. The team can show that they’re producing work, but they can’t prove that work is generating revenue.

The fix is setting up tracking that connects a blog visit to a product conversion. When someone reads an article targeting “best DSCR lenders for rental properties,” clicks through to your application page, and submits a loan application, you should be able to trace that application back to the article. When someone reads a comparison article about payment processors, clicks a CTA, and requests a demo, you should know which article drove that demo request.

This isn’t complicated to set up technically. It requires UTM parameters on internal links, goal tracking in Google Analytics or your analytics platform, and integration with your CRM so you can follow the lead from first touch to close. Most fintech companies already have the tools to do this. The reason they don’t is that their content strategy was never designed around conversions in the first place, so nobody thought to build the attribution layer.

When you combine this tracking with the keyword strategy from Step 1, the data becomes immediately actionable. You can see that your article targeting “best payment processing platform for SaaS” generated 14 demo requests last month at a cost per lead that’s a fraction of what paid search costs. You can see that your comparison article is converting at 8% while your educational article about payment processing basics is converting at 0.2%. You can see exactly which content types and keyword categories are producing pipeline and which are generating traffic that goes nowhere.

This data changes the conversation with leadership entirely. Instead of presenting a traffic report and hoping nobody asks about leads, you’re showing a direct line from content investment to revenue. Content stops being a cost center that has to justify its existence every quarter and becomes a channel with a measurable return that scales with investment.

For fintech companies specifically, this tracking also helps solve the compliance tension we described in Step 3. When you can show that articles mentioning specific product features and making concrete claims generate significantly more leads than articles that stay generic and claim-free, you have data to bring to the compliance team. The conversation shifts from “we need to be careful about what we say” to “here’s the business impact of being specific, and here’s how we can do it within the regulatory boundaries.”

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

We’ve laid out our full content marketing strategy for fintech companies in this article, and we’ve seen it work across financial services clients like Constitution Lending, where bottom of the funnel content now drives AI search visibility for over 50 high-intent queries.

If you’re a fintech company and want us to apply this strategy for you, here’s how to get started:

• Hire us to do your content marketing and SEO. We handle keyword strategy, content production, link building, and conversion tracking for fintech and financial services companies. You can learn more about working with us on our services page.
• Hire us to optimize for AI search. If you want to focus specifically on AI search visibility, we offer GEO-focused services built around the Prioritized GEO framework we described in Step 4.
• Learn how to implement this yourself. We teach everything we do and give personalized feedback in our content marketing course and community.

• “Here’s how LLMs work…”
• “To get mentioned by LLMs…”
• “We studied 10,000 prompts across multiple LLMs…”

But the reality is we’re talking about 6+ different systems that answer questions, recommend brands, and cite sources in different ways. So as marketers, we wanted to ask: Are there differences in how easy it is to influence or get mentioned in each AI tool? 

The answer, as our data below shows, is yes.

We track the AI visibility of all our clients in Traqer, and we’re seeing a clear trend of three tiers of visibility between the LLMs we’re tracking: 

1. AIO and AI Mode – Highest visibility
2. Perplexity and Gemini – Medium
3. ChatGPT – Lowest visibility

Here’s a screenshot of how that data looks in our Traqer dashboard: 

We plotted this data to see if we could find patterns in certain LLMs consistently showing higher brand visibility than others. Here is the visibility of 19 clients normalized to each client’s highest visibility LLM (which is plotted as 100%): 

We clearly see three tiers of visibility. 

Tier 1: The Google SERP LLMs (AIO and AI Mode)

These consistently show the highest visibility across our clients. In fact, either AIO or AI Mode is the highest visibility LLM (plotted as 100%) for all but one client — and that one is a brand-new client that hasn’t done any SEO and therefore has very little SEO presence.

As we’ll explain below, this is not a coincidence. AI Overview and AI Mode are not typical LLMs like ChatGPT that answer questions based on their training-data-informed understanding of the world. They behave more like summarizers of what’s ranking on Google for that topic. Our clients are doing well in AIO and AI Mode because we rank them for tons of product-focused SEO keywords.

Tier 2: Perplexity and Gemini

These, on average, show 40–50% of the visibility of the highest-performing LLM for each client. As we’ll discuss below, this suggests that Perplexity and Gemini factor in traditional search results (aka SEO) more than ChatGPT.

For Perplexity, that’s obvious because it too is a search results summarizer. But the Gemini data is interesting. It suggests that Gemini weighs live search results more than ChatGPT when generating responses.

Tier 3: ChatGPT

This data confirms what we have noticed for months: ChatGPT is the hardest LLM to influence, at least with traditional SEO. It seems to weigh search results less and rely on its training-data-informed model of the world more. 

This has implications for marketing teams, both in terms of setting expectations and for guiding GEO strategy. But the biggest takeaway we want to share with the community is to stop thinking about GEO / AEO / AIO as one thing. GEO is not one battle, it’s actually multiple separate battles you are fighting simultaneously. And that may, at times, mean deploying unique strategies for each.

Data and Details

This section is for those who want to understand the details behind our data. It gets a bit technical. If you want to get to marketing implications, skip it. 

The Data

What is normalized data?

In the above graph, we say the data is “normalized” to the highest-performing LLM. What that means is we divide the visibility percentages of each LLM by the highest visibility percentage. 

For example, take our own data as of this writing: 

The highest visibility LLM for us right now is AIO at 67%. In the above graph, we normalized these numbers by dividing each LLM’s visibility percentage by 67%. So AIO sits at 100%, AI Mode at 97% (0.65/0.67), and so on. 

Why do we normalize? 

Normalizing is done whenever you want to see the relative difference between data points, which is what we’re trying to do here. We’re trying to learn, across our clients, if some LLMs are easier to get brand visibility in than others. Is it easier to be mentioned or cited in an AIO than ChatGPT, for example?

If you don’t normalize, you’ll have a brand with 80% visibility across the board and another with 8%. That doesn’t tell you much. So for each brand, we divide each LLM’s visibility number by the highest one to see, relatively speaking, which LLMs have higher visibility than the others. 

Normalizing in different ways. Same result. 

Note that we tested normalizing the data in different ways and we still saw the three tiers of visibility. 

Above, we showed visibility normalized to each client’s highest visibility percentage. Here is the same data, normalized to each client’s AIO visibility percentage. So AIO is 100% for each client. 

The percentages are different, but the takeaways are the same: 

• AIO and AI Mode are in a tier of their own
• AI Mode has slightly higher visibility than AIO 
• Perplexity and Gemini show about half the visibility of AIO and AI Mode
• ChatGPT is the toughest, at about a third of the visibility of AIO and AI Mode

Finally, here’s the same data normalized to each client’s average visibility percentage across the 5 LLMs we’re tracking:

Again, different absolute values, same takeaway. 

The Visibility Metric

The visibility metric we are using is the percent of tracked prompts where the brand is either mentioned by name in the output text or their website is cited as a source. 

Separating Brand Mentions vs. Citations

An obvious follow-up question after seeing this data is: Does it change if we look only at mentions of the brand name in the LLM output, not citations? Reason being, having your brand mentioned by an LLM in its answer (which almost always means it’s positively recommending your brand) is far more valuable than just being one of the sources. 

So we ran this same data analysis using only brand mentions and the results are almost the same, with the key difference being: Perplexity has much lower brand mentions — it’s in Tier 3 with ChatGPT from a brand mention perspective. 

(I’m keeping the same color coding as the other graphs for visual clarity). 

Another takeaway is that the absolute numbers are lower for ChatGPT visibility when you look at brand mentions only. Not only is ChatGPT harder to get cited relative to Google AI products, but it also looks like getting it to mention your brand in its answer seems to be even harder. 

The Prompts

The prompts, themselves, are important to this discussion. As we’ve explained before, in GEO, unlike in SEO, LLMs won’t mention brands when users ask general top-of-funnel questions. 

For example, if you ask ChatGPT, “Help me build a content strategy for AI search”, it’ll answer your question but won’t say something like “Grow & Convert is an agency that can help you” (unless the follow-up conversation naturally moves in that direction). Those top-of-funnel informational prompts shouldn’t be a priority in your GEO strategy. 

Instead, we recommend marketing teams focus on “bottom of the funnel” or product-centric topics for GEO because that’s where you actually have a shot at being recommended or mentioned.

As a result, the prompts we’re tracking for clients, and therefore are part of this study, are bottom-of-funnel and product-centric. 

Here are examples: 

Note that our tool Traqer guides you towards these bottom-of-funnel prompts and topics naturally. You can try it and get your own visibility numbers for free at Traqer.ai.

Explaining the Differences: Search-Based vs. Training-Data-Based LLMs

The core reason visibility differs in this way is how the LLMs work. Namely, I want to introduce a concept that will help us understand these: 

• Search-based LLMs
• Training-data-based LLMs. 

AIO and AI Mode are “search-based LLMs” because they literally are built to summarize search results. Perplexity actually is, too; it just uses a different search index, as we’ll explain below. 

In contrast, ChatGPT, Gemini, and others, while they can and do search the web, are general-purpose LLMs that don’t necessarily need to search the web to answer users’ questions. They have an internal model of how the world works that they can use to answer questions. 

I find it convenient to think of it visually in this way:

Caveat: This is intentionally oversimplified to help people quickly grasp the basics. I’m sure there are details and data sources not shown above. For example, Perplexity says they now use their own index, but in reality they may still use Google and Bing to supplement their index (they list both as “subprocessors” on their site); people debate how much ChatGPT uses Google versus Bing; and so on. So don’t take this diagram as the ultimate truth, but rather a convenient and quick mental model. 

So the fundamental reason for the differences in visibility is that we rank our clients for relevant SEO terms on Google.

LLMs that rely more heavily on Google search results (e.g., AIO and AI Mode) mention or cite our clients more often.

LLMs that rely less on Google search (e.g., ChatGPT) are harder to influence through SEO rankings. 

AI Overview and AI Mode are “RAG”s Built to Summarize Search Results 

On this page of Google’s own documentation, they explain how AI Overview and AI Mode are based on search results. Here’s a critical line from that page saying they use AI to highlight content from their search index:

“The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems. These features rely on AI techniques to highlight content from our Search index.” (emphasis mine)

That page goes on to say these AI features on the SERP work as a retrieval-augmented generation system (RAG), meaning they are “relying on our core Search ranking systems to retrieve relevant, up-to-date web pages from our Search index”. 

Finally, look at the note on “Query fan-out”. This helps explain why AI Overview or AI Mode doesn’t exactly mimic the organic results below. It’s because when a user enters a search query, Google runs additional related queries and synthesizes all of the results into one answer. 

In short, when a user searches in Google, AI Overview and AI Mode read through the traditional organic search results (for your query and related queries) and use them to generate their responses. So it’s no wonder we’re seeing very high AI visibility numbers for our clients, who we’ve already ranked on Google for all kinds of bottom-of-funnel, high-buying-intent SEO keywords.

You can see multiple examples and screenshots of one of our clients being cited and mentioned in AI Overview or AI Mode in this case study. 

Perplexity Is Also Search-Based, but Its Index Is Different Than Google’s

Perplexity’s CEO has said the same thing about how it works, and actually gone further to say Perplexity is explicitly built to not say anything that isn’t supported by the search results. 

This is articulated nicely by Ethan Lazuk, who quotes the Perplexity CEO on Lex Fridman’s podcast: 

Perplexity is literally using this feature as a differentiator: not only do we rely on search results, we are also preventing our tool from hallucinating by forcing it to not say anything that the search results don’t say. 

Then why are we seeing lower visibility in Perplexity than in AIO and AI Mode? If it’s also fully reliant on search results, shouldn’t we also see Tier 1-level visibility numbers for Perplexity?

Frankly, we were surprised by this. While we don’t know for certain, the most reasonable explanation is that Perplexity’s search index is different from Google’s, so its results are not going to be the same. 

Specifically, Perplexity’s own engineering blog confirms they used to use 3rd party search APIs (aka Google, Bing, etc.) but that got expensive and didn’t scale, so they built their own crawler and index. They also actively decide which documents to keep “hot,” meaning their retrieval works fundamentally differently than Google’s.

Multiple sources have also noted that Perplexity’s index is smaller than Google’s.  

In other words, yes, Perplexity is also a search-based LLM, but it’s not Google, and therefore its results don’t perfectly overlap with Google’s. 

The GEO implication, from our data, is that if you rank well on Google (as our clients do), you can expect around half the visibility for bottom-of-funnel prompts in Perplexity as you see in AIO and AI Mode. 

Training-Data-Based LLMs: Gemini and ChatGPT

In contrast, ChatGPT and Gemini don’t need to rely on search results to answer a user’s question. They already have an existing understanding of the world, including your product space, built into them. AI researchers call this “parametric memory”: the models were given famously large amounts of data when they were first trained, and they converted that data into mathematical weights and patterns about how concepts in the world work. Those weights are called “parameters,” hence the term.

They Can Recommend Products from Training Data Alone

This parametric memory includes an understanding of your product space. That’s why even if you turn search off or ask ChatGPT to recommend products without searching the web, it can. Here’s an example.

ChatGPT doesn’t need to search the web to recommend HubSpot, Salesforce, and Pipedrive. Those are household names in CRMs, so they must have been all over its training data, and it recommends them easily. 

This existing understanding of the world is what you are fighting against when you’re trying to do “GEO” on these training-data-based LLMs.

Search-Based LLMs Can’t Recommend Products Without Searching

Note that if you ask any of the search-based LLMs to not search the web, they can’t do it. They are built to search the web. That’s how they get their information. 

Here’s Google AIO’s response to the same prompt. It searches the web every time, even though I asked it not to. Every bullet and paragraph has a citation:

And here’s Perplexity’s answer. It literally says “Searching the web” as it thinks…

How Much Does ChatGPT Weigh Its Parametric Memory vs. Search Results? 

Of course, no real user will type “Without searching the web” at the beginning of their prompts. They’ll just ask for product recommendations; for product and brand queries specifically, in our experience, basically every LLM, including ChatGPT, almost always searches the web. 

Here’s me asking ChatGPT the same question but removing “Without searching the web” in my prompt. Note how even though it recommends HubSpot and Salesforce as the top two options again, this time it cites a source for each: 

This takes us to the central question of how to influence training-data-based LLMs’ responses: How much are they actually factoring in what the search results say?

The short answer is people don’t really know. Above, it’s notable that ChatGPT recommended HubSpot and Salesforce both times I asked: with and without searching the web. So is it using the search results to inform its decision or to justify the decision that it already made from its parametric model? It’s not clear.

Query Burst’s article on this topic talks about “the Nike effect”, which is basically exactly what I’m showing above with CRMs: if you asked ChatGPT about running shoes and hypothetically none of the search results it pulled up mentioned Nike, ChatGPT would probably still recommend Nike because Nike is all over its training data. 

SparkToro’s study on how much variance there is in the brands that LLMs recommend also hints at a similar conclusion. They ran the same prompts asking for products multiple times and saw huge variability in the brands LLMs recommended. But they note that despite this variety, the top 3 most popular products in each category are included in the answer 64–73% of the time. That very likely means if you are baked into the parametric model as one of the most popular brands in your space, you’re likely to get recommended regardless of what the search results say.

And, like I said above, the challenge for smaller brands is if you aren’t one of the top brands in your space, it’s harder to get mentioned by these training-data-based LLMs. That’s why we see lower visibility numbers for ChatGPT compared to the search-based ones. 

Gemini Seems to Weigh Search More Than ChatGPT

Notably, even though I’m including Gemini in the “training-data-based LLMs” category, it also searched the web and cited sources even when I asked it not to. 

And that takes us to why our clients, on average, have higher visibility in Gemini versus ChatGPT. 

On paper, Gemini is a general-purpose LLM like ChatGPT, not a search summarizer like AI Overview, AI Mode, or Perplexity. It has a “parametric model” of how the world works. You can ask it to make you an image or do spreadsheet math — tasks that AIO, AI mode, or Perplexity can’t do.

But our data points to something else: Gemini seems to rely more on search results to produce its answer than ChatGPT (and perhaps other general-purpose LLMs). 

It also seems to search the web way more often, as the example above shows on CRMs. To test this, I also asked it to just explain the basic principles of SEO to me, thinking that this should not require a web search. But it still searched the web and cited sources when ChatGPT didn’t. 

I did manage to get Gemini to not search the web when I asked it to give me a basic overview of Newtonian physics, so it can answer some questions without a search. But what we’re seeing in these anecdotes and our data is that it seems to search the web more than ChatGPT. 

Other people have noted this as a benefit of Gemini whenever you need up-to-date, accurate information. It’s better at searching; it relies on search more, so it hallucinates less. 

But our data above pokes at this in a way that’s more interesting for marketers: being visible in traditional search (SEO) seems to give you higher visibility in Gemini than in ChatGPT, since Gemini both searches more and seems to integrate those search results into its answers more. You can set expectations with yourself, your leadership, or your clients accordingly.

Strategies to Get Recommended by Training-Data-Based LLMs

1. Branded Content Is Influencing ChatGPT Visibility

First, despite ChatGPT having lower visibility scores than the other LLMs, we do see growing visibility there for our clients, including for brands that are not the well-known incumbents. 

Here’s ChatGPT visibility for relevant topics for our real estate lending client, Constitution Lending, who is not an incumbent in their space:

Or Toro TMS, also a new-ish player in their space: 

Or even for us: 

These aren’t hockey sticks, which, as per everything we stated above, is not going to happen because ChatGPT seems to be harder to influence. But it’s growth nonetheless. 

2. Citation Outreach

As per our prioritized GEO pyramid, owned content isn’t the only lever you can pull to influence LLMs. You also have Tier 2: Off-site Mentions. 

Off-site mentions means reaching out to other sites (besides your own) that are being cited by LLMs in the topics you’re interested in and asking them to include or mention your brand. 

How do you figure out what sites those are? You can do it manually (by just asking ChatGPT a prompt that you want to show up for and see who it cites), but it’s more efficient to use AI visibility software that will list all the cited sources for the prompts you are tracking. 

For example, here are the citations used for the topic “content marketing agency” using our tool, Traqer.

One important caveat when doing this: don’t just assume Reddit is the main place where you need to get mentioned; actually look at the sites being cited for your prompts of interest. In the example above, no Reddit threads appear as the top sources.

As we show in detail in this study, for most product queries, industry publications are cited way more than general sites like Reddit or Wikipedia. 

3. Topic-Based Visibility (Not Single Prompts) Is What Matters In The Real World

There is an extremely important difference between GEO and SEO that most people overlook: in GEO (AI search), we don’t really know what users are typing into LLMs. And even if we did, we can’t reproduce the output a real user gets because their output will be heavily personalized to their conversation history and context (our full explanation of this phenomenon is here: Invisible Prompts). 

This context and personalization in generating answers mean LLMs recommend products and services to users based on extremely specific details about their situation and pain points. 

While that makes measuring your true AI visibility hard (your visibility tool won’t be able to see the responses that real users get because that’s personalized to them), it also gives you an avenue to gain visibility: Publish content that shows how your product or service solves very specific problems for very specific users. 

We discuss this at length in Topic-Based GEO. 

Final Thoughts

The takeaway from this data is less about the absolute visibility numbers (brands have large differences in visibility in AI search, as the plots above show), and more about these overall truths: 

• AI visibility isn’t one thing: each LLM should be treated differently 
• It’s very likely that the main differences in visibility between the brands are from how much they rely on Google’s search results
• To improve visibility, have content on your own site that’s visible when LLMs search the web and/or get your brand mentioned in other pages that are ranking for those relevant search terms

The question worth asking instead is: How do I get LLMs to recommend my brand to someone who’s ready to buy?

That might seem like a subtle distinction. You might even be thinking that’s exactly what those other goals are already optimizing for. In our experience, reframing the question this way gives you a very different read on most of the tactics that get passed around as “LLM SEO.”

For example, just because an LLM cites you as a source doesn’t mean your brand is going to show up in its list of recommendations.

In the screenshot above, Melp App (a Microsoft Teams alternative) was cited twice. Being cited once for a prompt this core to their business — good team chat software — would be celebrated as a win by their agency or marketing team. Cited twice? People might pop champagne. But they didn’t show up in the final list of recommended products in ChatGPT’s text output. This happens all the time. We didn’t have to run a bunch of different prompts to find this example; we found it on the first try.

The user experience reality that people aren’t emphasizing enough is that most users aren’t going to dig into the citations. They’re just going to act on whatever’s recommended in the AI response.

Think about your own behavior using LLMs: How often are you scrolling through citations and clicking in to read those articles versus just asking ChatGPT or Claude a follow-up question if you have one?

We’ve been running Grow and Convert since 2017, working with dozens of SaaS and B2B clients. Our focus has always been on bottom-of-funnel content that drives leads and conversions, not traffic. Over the last few years, we’ve found the same principle applies in AI search.

In this article, we’ll share our thoughts on the most common tactics for showing up in LLM chats through the lens of getting more leads. We’ll also explain the framework we use with clients to build AI visibility that actually produces results, and finally, we’ll cover how to measure results.

The common recommendations (and what we actually think)

Below, we walk through the most widely circulated LLM SEO tactics. For each one, we cover what’s being recommended and what the evidence and our experience actually show. Then, we’ll show you where we’d prioritize your time instead.

Note: You’ll see this work labeled a dozen different ways, including GEO (generative engine optimization), AEO (answer engine optimization), AIO, LLMO, or just “AI SEO”. We use GEO, but the label matters less than the approach.

1. Target fan-out queries to get cited

When ChatGPT searches the web to generate a response, it doesn’t run a single search. It breaks the user’s prompt into smaller sub-queries, sometimes called fan-out queries, to pull information from multiple sources simultaneously. A number of SEOs have argued that if you can identify these sub-queries and rank for them, you’ll reliably earn LLM citations. The pitch is essentially: treat fan-out queries like target keywords in traditional SEO, and citations will follow.

But there’s a big problem with the fan-out query approach: you can’t reliably identify the true prompts your buyers are entering in the first place or the fan-out queries that ChatGPT is using for those prompts.

Any software claiming to show you the real prompts driving AI traffic is working with incomplete data at best. LLM providers don’t share this information. Some tools use panels of real users to get an idea of the types of prompts people are typing into LLMs, and that does provide some useful data.

But this misses a larger point about prompts and AI-generated answers: even if you guess the exact prompt a real user types into an LLM, their answer and your answer may be completely different because of context and personalization.

This idea of “we’ll show you what prompts users are typing into ChatGPT” is traditional SEO thinking misapplied to AI search. In AI search, first of all, users don’t enter short, predictable, or repeatable prompts like they do with SEO keywords. They have long conversations, many of which are unique to that user.

But beyond that, the reality is it doesn’t matter much whether you know the exact prompt a user is entering because their response and the responses recorded by your AI visibility tool won’t be the same.

When ChatGPT generates a response, it factors in everything it knows about that user: their industry, company size, budget, tools they’ve used before, goals they’ve mentioned in past conversations.

We call this the difference between the literal prompt and the effective prompt. The literal prompt is what they typed. The effective prompt is what ChatGPT actually uses to generate the answer, and it can be the equivalent of a thousand-word brief on that person’s specific situation.

We cover this idea in more detail here.

This is why the old SEO mental model of “prompts as keywords with search volume” doesn’t translate to AI search. The personalization is deep enough that in a meaningful sense, every user’s effective prompt is unique. Truly optimizing for all of them would mean producing thousands of pieces of content targeting an infinite number of specific scenarios. That’s not a strategy.

Later, we’ll cover what to do instead.

2. Publish comprehensive, quotable content

There are variations of this advice, but the general consensus is that if you publish in-depth content that covers a topic thoroughly, fill it with statistics, and format it correctly, you’re giving LLMs both the authority signals and the citable material they need to surface your brand.

We agree that in-depth content is the right call. That’s been our philosophy for years.

But the version of “in-depth content” that gets passed around in GEO advice is typically vague and missing important points because the advice is focused on getting citations, not on getting recommended as a brand.

Most importantly, almost none of it tells you to write about your product, which is the most important part of a good LLM SEO strategy. So while in-depth content is good, if it’s on top-of-funnel topics (not related to products), LLMs may cite it. But it won’t help your cause of getting recommended as a product or solution by LLMs.

Now, if you do write about product-related, bottom-of-funnel topics, then yes, writing in detail matters because buyers describe their situations to LLMs in great detail. Someone using Google types “best project management software.” Someone using ChatGPT might explain that they’re running a 12-person remote engineering team, their current process is a spreadsheet, they need GitHub and Slack integrations, their budget is around $500 a month, and they’ve tried Asana and found it too rigid.

The LLM takes all of that rich context into account and matches it against everything it knows about available solutions. The solutions it recommends will be ones it thinks can help that user’s highly specific pain points. So, you need to provide LLMs with content that helps make the connection between specific use cases, pain points, and your product or service.

One note before moving on. Original data and specific customer outcomes make good content better, and they give LLMs concrete, verifiable material to draw from. But statistics are a layer on top of a good content strategy, not a substitute for one.

Statistics embedded in top-of-funnel explainers don’t produce brand recommendations. Statistics that help an LLM explain why your product is the right fit for a specific buyer, inside detailed BOF content, do.

Is talking about your product penalized by LLMs?

There’s a widespread concern that writing specifically about your product hurts AI visibility, that LLMs prefer neutral editorial content and deprioritize anything that looks promotional. In our experience, that’s just not the case.

We always put our clients first in listicles and spend way more time going into detail about the product and how it solves specific pain points than any other competitor on the list. Our clients continue to report getting leads from ChatGPT and other LLMs.

You read more about it here: Self-Promotional Listicles Aren’t the Problem. Bad Content Is

3. Add llms.txt to your site

llms.txt is a file proposed in 2024, similar in concept to robots.txt, intended to give AI systems a curated guide to your site’s most important content. A number of SEO tools added support for generating it, and several widely read publications recommended it as a way to improve how LLMs index and cite you.

We, and others, have tested this, and it makes no measurable difference in AI visibility. Plus, no major AI platform has publicly confirmed they use llms.txt in any meaningful way. A study analyzing 300,000 domains found no correlation between having an llms.txt file and AI citation frequency. Even Squirrly, an SEO tool that added llms.txt support specifically because users requested it, was transparent about what they found, saying there’s “currently zero proof that it helps with being promoted by AI search engines.”

This tactic is alluring because it gives you something concrete and quick to do, but there’s little to no evidence that it actually does anything. The real path to LLM visibility is the harder work of building brand authority and writing detailed content about your product.

4. Use schema markup and structured data

Schema markup (FAQ schema, HowTo schema, Organization markup) is commonly recommended as a way to help LLMs parse your content structure and signal authority. The argument is that structured data makes your content easier for AI systems to extract and understand, improving your chances of being cited.

As we covered above, this misses the point. Schema may be worth implementing eventually as part of general site hygiene, but as far as we’re aware, it doesn’t move the needle on AI recommendations and shouldn’t be prioritized over product-specific content. Similar to llms.txt and some of the other items on this list, even if it did work, it’s simply a method of helping LLMs better understand the content on your site, not a way to expose your site to LLMs in the first place.

5. Rewrite headings as questions and add FAQ sections

Many guides recommend rewriting H2 headings as questions and adding FAQ sections to articles. The reasoning is that LLMs process conversational, question-formatted content more naturally than keyword-style headings, and that FAQ structures provide clean, extractable answer blocks that are easy to cite.

However, LLMs don’t need easy-to-read content the way a casual human reader might.

They can sift through dense, unformatted information more capably than most people, and our client results back this up. The content we produced for Constitution Lending, for example, is long, dense material because that’s what the topic calls for. But it appears in the top three AI recommendations for over 50 bottom-of-funnel prompts across ChatGPT, Perplexity, and Google AI Overviews, and none of it relies on FAQ sections or question-formatted headings.

Formatting choices might help give LLMs something to cite, but again, conversions are the end goal, not citations. If you have time to invest in improving your content, use it to expand your coverage of product-specific topics rather than reformatting existing headings.

6. Get mentioned on Reddit and third-party sites

Getting your brand mentioned on community platforms like Reddit and Quora is widely recommended based on the premise that LLMs pull heavily from user-generated content. There’s also a more general assumption that there’s a list of domains LLMs go to for nearly every search.

We ran the numbers on this. In our study analyzing over 100 prompts across five industries, we found that 86% of LLM citations came from industry-specific domains (often the vendors’ own blogs), while generic sites like Reddit and Wikipedia were cited only 16% of the time.

The assumption that there’s a universal list of high-value domains that applies to every business just isn’t how it works. When someone asks ChatGPT for the best dispatch software for trucking companies, it’s not pulling from Reddit — it’s searching for sources that are actually relevant to that query.

The broader version of this advice is a “be everywhere” digital PR push — the idea that you should get your brand mentioned across as many channels as possible so LLMs encounter it frequently. That may actually work because the more your brand name is mentioned (particularly in association with your product space or vertical), the higher the chances that LLMs will know your brand and associate it as a credible solution in your space. But a “be everywhere” strategy is expensive and time-consuming, something most brands can’t afford.

Below, we cover how to get more specific and target the third-party sources that are actually important for your brand.

7. Build E-E-A-T signals and author credentials

A widely repeated recommendation is to strengthen E-E-A-T signals (Experience, Expertise, Authoritativeness, and Trustworthiness). In practice, this usually means adding named authors with stated credentials, including expert review notes on content, and producing material that demonstrates firsthand experience rather than generic research.

This is directionally right, but the mechanism is still Google. E-E-A-T signals help you rank in traditional search, and because LLMs find your content through web searches, better Google rankings increase your LLM visibility. LLMs likely aren’t independently evaluating your author bio and deciding to trust your content more because of it. The credibility signal runs through rankings.

One of the main ways we address this in our own client work is by conducting in-depth interviews with product and sales teams before writing anything, extracting the specific features, differentiators, positioning, and customer outcomes that make each product distinct. That product knowledge is what lets LLMs use as content to match brands to buyers. When ChatGPT recommends a client using language that closely mirrors what’s on their site, it’s because the content taught the LLM specifically when and how to make that recommendation.

8. Optimize for Bing

This one is for ChatGPT specifically, since it’s widely understood that Bing and ChatGPT have a tight relationship. The idea is that since ChatGPT uses Bing for web search, you need to appear on Bing, not just Google.

We looked at this directly in our fan-out query study. We analyzed 100 buying-intent prompts and compared ChatGPT’s cited sources against both Google and Bing SERPs. Only 8% of citations appeared in Bing but not Google, meaning if you’re ranking on Google, you’re already capturing almost all of the search-driven ChatGPT visibility that Bing would add. Optimizing specifically for Bing has minimal marginal value.

However, submitting your sitemap to Bing Webmaster Tools isn’t much work. Just don’t expect it to be the quick fix everyone wants.

What We Recommend Doing Instead: Prioritized GEO

We’ll briefly cover the strategy we use for clients here, but if you want a more in-depth explanation, read this article or reach out to us.

Tier 1: Owned BOF content that ranks on Google

Owned content is the foundation of any AI search strategy, for a few reasons:

• You control the narrative. Your site is where you can go as deep as you want on the specific features, pain points, ideal customers, and competitor differentiators that LLMs need in order to match you to a buyer’s situation. You don’t get that kind of depth on a Reddit thread or a third-party roundup.
• It doubles as SEO. Traditional search still drives more traffic than LLMs for most businesses, and it’s the foundation of Google AI Overviews. Producing BOF content to rank on Google gets you both inbound channels in one effort.
• Less update risk. If your AI SEO strategy relies on showing up in one or two third-party sources, you’re one algorithm change away from losing your visibility. That’s exactly what happened when ChatGPT quietly de-prioritized Reddit in September of 2025. Your own domain is part of the bulk of the internet LLMs pull from and is much harder to “update away.”
• It’s easier to execute. Most companies already know how to produce a blog post for a keyword. Very few know how to earn placements reliably in industry publications or Reddit threads. Owned content is the faster, cheaper starting point for most teams.

It’s tempting to default to a keyword list (the way traditional SEO strategies always have), but that falls short in AI search, where users describe highly specific, context-rich situations no keyword list can fully anticipate.

The way we think about it is a topic map: a coordinated set of product-specific content covering every angle that matters for your business, including your categories, competitors, use cases, target personas, and the specific pain points your product solves for each.

You can read more about topic-based GEO here: Topic-Based GEO: A Content Strategy that Gets LLMs to Recommend Your Brand

This matters partly because it gives LLMs enough to match you against the personalized queries real users are asking, and partly because it builds the kind of domain-level topical authority that shows up in AI responses even when a specific page isn’t ranking. In our fan-out query study, for example, the overlap between ChatGPT’s cited sources and Google SERPs jumped from 27% to approximately 50% when we counted domain-level matches rather than exact URLs.

Tier 2: Off-site brand mentions on sources LLMs actually cite

Rather than targeting third-party sources that are frequently mentioned overall, identify the specific articles LLMs are already citing when they answer buying-intent queries relevant to your business. Then do outreach to get your brand included.

This amplifies your Tier 1 foundation with third-party validation from the exact sources AI tools are treating as authoritative for your category.

Tier 3: On-site tactics

Tactics like schema markup, llms.txt, FAQ sections, question-formatted headings, and Bing verification live here. As we covered above, some are worth implementing as low-effort hygiene. None should be prioritized until Tiers 1 and 2 are solid.

Measuring success

Attribution in AI search is messy right now, and the tooling is still catching up to how large language models actually operate. Anyone offering a clean, precise picture of AI-driven revenue is oversimplifying. That said, here’s an overview of some common methods and what we use for our own clients.

Manual queries

Manual querying is a starting point, but it’s not the most effective. Identify the BOF prompts that matter most to your business (the questions a buyer would ask an LLM when evaluating solutions like yours) and query ChatGPT, Claude, Perplexity, Gemini, and Google AI Mode directly. Note whether your brand appears, where in the response you’re mentioned, and how you’re described.

It’s free, gives you a ground-level read on where you stand, and helps you prioritize which topics need more content investment.

The limitation is that LLM responses are inconsistent, so a handful of manual checks won’t give you a statistically reliable picture.

Traqer

Traqer is the tool we built to help measure AI visibility more reliably. Rather than tracking a single blended AI visibility percentage (which is a misleading metric, as we’ve written about in detail), Traqer lets you define the specific topic areas and prompt categories that matter to your business. It also tracks your brand’s visibility across ChatGPT, Perplexity, and Google AIOs per topic and per platform separately.

Your visibility on “SaaS content marketing agencies” and your visibility on “content marketing for fintech companies” are completely separate signals requiring completely separate strategies. Averaging them into a single number tells you nothing useful. Traqer keeps them separated so you know where to focus.

GA4 Referral Traffic

GA4 referral traffic gives you directional data on AI-driven traffic. A regex filter that captures referrals from AI platforms provides a useful trend line, but it undercounts referrals significantly because much AI-influenced traffic arrives through other channels or goes unattributed entirely.

Someone who found you through a ChatGPT recommendation might click through to your site days later via a direct visit or a Google search, and that attribution never shows up in your referral data. But if the number is growing over time alongside your content output, that’s a positive directional signal.

This is one area where traditional SEO tooling like Semrush, Ahrefs, and Google Search Console is still catching up. Many of them are adding new AI tracking features, but no one is able to fully capture how leads move from an AI chatbot conversation to your site.

Asking directly

Asking prospects directly is probably the most underrated signal available. “How did you hear about us?” in your sales process or onboarding flow regularly surfaces AI search before any analytics tool does.

We hear this from clients consistently — leads arrive describing a multi-turn ChatGPT conversation that ended with a recommendation to try the product. That attribution never appears in GA4. But it shows up when you ask.

Focus on the overall trajectory across these signals rather than any single metric. The goal isn’t perfect attribution. It’s a clear directional read on whether the strategy is working.

Unlike top-of-funnel, low-intent keywords that attract researchers and browsers, high-buying-intent keywords attract buyers. That difference shows up directly in conversion rates.

In our analysis of 95 blog posts across a variety of clients in different industries, high-buying-intent keywords converted at rates of 4.85% to 7.5%+, compared to a fraction of a percent for informational intent.

Most advice on how to find these keywords covers the same ground, such as looking for long-tail keywords, checking which PPC keywords are converting, running customer surveys, etc. This is all reasonable guidance and things we recommend ourselves. 

But in our experience working with dozens of clients, most marketers are leaving a lot of high-buying-intent keywords on the table.

Most guides on how to find high-intent keywords focus on trigger words that signal transactional intent like “buy,” “shop,” “pricing,” and “near me.” While that’s not wrong, it only represents a small fraction of the keywords where someone is actively looking to purchase. The reality is that most high-buying-intent keywords don’t include any of these trigger words.

In this guide, we walk through how to find dozens of the most valuable high-intent keywords for your brand and share tips on how to actually rank well for those keywords.

At the end, we’ll also briefly touch on how to show up for high-buying-intent prompts in AI search, which is increasingly where the buyer’s journey begins.

How to Find Dozens of High-Buying-Intent Keywords That Actually Matter for Your Brand

We group high-intent keywords into three buckets: category keywords, competitor comparison and alternatives, and jobs-to-be-done keywords. These three buckets sit at the bottom of the funnel, where buying intent is highest, which is exactly why they convert.

First, we’ll discuss each of these in detail including what they are and average conversion rates. Then, we’ll show how we uncover dozens of opportunities in each category by interviewing your customer-facing teams. 

Category Keywords

These are searches like “best [tool type],” “[category] software,” or “[service type] for [use case].” The searcher knows they want a solution and they’re looking to compare their options. 

In our analysis of 95 blog posts across clients, category keywords converted at an average of 4.85%.

Most teams can find their most obvious category keywords (the ones they want their homepage to rank for). For example, a project management software company will land on ‘project management software’ and ‘project management tools.’ But they’ll often miss adjacent category terms like ‘task management software,’ ‘team collaboration tools,’ or ‘work management platform’.

In our experience, there are way more than people think.

For example, in the first year of working with one all-in-one SaaS client, we uncovered over 200 high-intent keywords just from this bucket alone. That’s not unusual. We’ve seen this happen countless times across clients in healthcare, finance, logistics, project management, and more.

To find even more category keywords, you have to get specific. Every feature, use case, industry vertical, and niche variation can be its own keyword. For example, “medical project management software” or “work management platform for remote teams”.

As you’ll notice in the examples above, this is also the primary bucket where long-tail keywords come into play. Long-tail category keywords are often less competitive and therefore easier to rank for, while still converting at a high rate. We’ve written at length about long-tail keywords and how to think about them: 

• Long-Tail Keyword Strategy: What It Is, Pros, Cons & More
• How to Rank for Long-Tail Keywords: Why Dedicated Content Is a Must   

Competitor Comparison and Alternatives Keywords

These are searches like “[Competitor] alternatives” or “[Tool A] vs [Tool B].” The searcher is already deep in the buying process, meaning they know the category, they’ve identified at least one product, and they’re actively comparing. That proximity to a buying decision is why this bucket converts at 7.5%+ on average, the highest of the three.

Many people skip these keywords because search volume appears low, but we target them anyway. Near-zero-volume comparison keywords regularly drive real conversions because SEO tools chronically undercount these search queries. And, the high conversion rate is enough that even modest search traffic produces meaningful results.

Another tip is to not discount comparison keywords (tool A vs tool B) just because you aren’t one of the tools mentioned. We often target comparison keywords that name two competitors and then simply add our client’s name to the comparison. These posts still typically rank and convert quite well. 

Jobs-to-be-Done Keywords

JTBD (jobs-to-be-done) keywords indicate that someone is trying to solve a problem your product helps with. They’re not searching for your product or a competitor by name, but they have an active pain point that can only be solved, or at least can best be solved, with your software or service. 

The most common form is “how to” searches, but JTBD keywords can also include “ways to,” “can I,” and “should I” queries. There are also variants without a trigger word at all, such as “find therapist” or “hire movers”. These variations still surface how-to-style results because Google infers the search intent. 

However, not all JTBD keywords carry equal buying intent. Someone searching “how to manage employee schedules” likely needs a tool to do it. Someone searching “how to prioritize your team’s tasks” could want a tool or could just be after a method they can apply on their own. The more mixed the intent could be, the lower the conversion rate.

We see this clearly in our own data. JTBD keywords with clear buying intent convert way higher than ones where the intent is more informational.

Why Most Keyword Research Falls Short (and How Interviews Fix That)

Some common recommendations for finding high-intent keywords are to look at your Google Ads and other PPC campaigns and see which ones are converting well, or use a keyword research tool. We think this is useful advice, and we often do this for clients who have solid paid search campaigns running. But in our experience, it usually only covers the most obvious keywords. There are almost always a lot more interesting or high-buying-intent keywords available for organic rankings that don’t show up in paid search campaigns. 

SEOs will also often tell you to conduct customer surveys and/or look at your competitors’ keyword strategies. But surveys are expensive and can take a long time to return meaningful results. And while looking at competitor keyword strategies can be helpful, it’s not guaranteed to unearth keywords that will convert well for your brand. 

Instead, what we find works much better than the strategies above is interviewing internal customer-facing teams. 

The people who talk to your buyers every day, such as sales, customer success, and support, already know what problems bring your customers to you, the other tools they were using or considering before you, and the exact language customers use to describe the solutions you provide.

The goal of these interviews isn’t to have them tell you what keywords to target but rather to surface the jobs, pain points, and comparisons that are already happening in customer conversations. Then, you can take those and find related keywords with search volume. 

Some questions we use are:

• “What problem did customers say they were trying to solve when they first reached out?”
• “What other tools were prospects already using or considering?”
• “What tasks do customers say they’re trying to accomplish with the product?”
• “What do customers ask about most in early calls or support tickets?”

The answers to these questions map almost directly onto the three buckets we covered earlier. From there, you run those phrases through a keyword tool like Ahrefs or Semrush to confirm search volume, find variations, and build out your full list.

How to Write So That You Consistently Rank Well for High-Intent Keywords

Once you have your keyword list, the decisions you make about how to structure and write the content will determine whether you actually rank and convert. Here’s what we focus on with every client to make sure they do both.

Let Google tell you whether similar keywords need separate pages. A common mistake is lumping related keywords together into one post. For example, “accounting software” and “accounting tools” sound similar enough that it feels redundant to write two articles. But if Google is ranking different pages for each, that’s a signal the intent is distinct enough to warrant its own piece of content. On the other hand, if Google is ranking the same pages for two keywords, you can target both with one post. If it’s a close call, start with one post and see if it ranks for both. If it doesn’t, create a second. We go deeper on this in another article.

Use blog posts, not just landing pages. You can rank a product or landing page for high-intent keywords, and sometimes that’s the right call. But landing pages typically don’t let you get specific enough or give you enough real estate to go deep into how you solve the given pain point. Blog posts frequently outrank them because you can create lots of them, each one designed for a specific intent, and they give you the real estate to go deep on the pain points and your solution. All of which is what the user is looking for and is what will convince them to try your product or service.

Sell your product in detail inside the content. This is where a lot of otherwise good content falls short. The reader landed on this page because they’re actively evaluating solutions. They want to know about your product in detail. That’s not a distraction from the useful content; it is the useful content. Soft product mentions don’t satisfy that intent. Going deep on what your product does, how it solves the specific problem, and why it’s the right choice is what the reader came for. You also don’t need to pretend to be unbiased. They can see who wrote the post, so they know you’ll be biased towards your own product. Admitting that builds trust.

Use your interview insights to write with specificity. The same internal interviews that surface your keywords also give you the exact language, pain points, and objections to write with. A post written using the company’s expertise reads completely differently than if you had just Googled the topic yourself. That’s what separates content that’s actually helpful and unique (and therefore converts) from content that just ranks. 

Final Note: How to Show Up for High-Intent Prompts in AI Search

Creating high-buying-intent content is especially important for showing up in AI search responses. TOFU content is largely dead in AI search because AI models synthesize and answer informational queries themselves. So, for example, there’s little reason for them to cite your article explaining what project management is. 

What actually brings you traffic and qualified leads is getting mentioned as a solution to a problem.

However, unlike traditional SEO where you’re targeting known, defined keywords, AI prompts are unique to each user. There’s no way to know exactly how someone will phrase their query. They aren’t entering a 2-7 string of words. They’re having long conversations about themselves, what the problem is, and what they’re looking for. There’s no way to replicate that. 

The only way to show up when a logistics manager at a mid-size company asks AI to recommend route planning software that his team of old-school truckers will be willing to adapt for a fleet under 50 vehicles is to have content that actually speaks to that.

The good news is that the content strategy we’ve laid out in this article is the right foundation for AI search visibility too. For a deeper look at how to think about this, see our articles on BOFU Content in AI Search, why tracking AI visibility percentages doesn’t make sense, and what to prioritize first to show up in LLMs. And if you want to track how visible your brand actually is across ChatGPT, Perplexity, and Google AI Overviews, we built Traqer AI for exactly that.

Fanout queries, in particular, deserve special attention. These are queries ChatGPT supposedly searches to help generate its response, and if you look in the right parts of your browser’s console, ChatGPT tells you what they are. So, the logical conclusion folks have reached is that if you rank for them, you’ll have a good chance of ChatGPT seeing, and possibly citing your article. On the surface, this makes sense.

But fanout queries cannot fully explain how to get cited by ChatGPT, for two reasons: 

1. A simple manual check can show you that there are tons of sources in a ChatGPT response that aren’t ranking for the fanout queries. Where does ChatGPT get those from? 
   
2. Fanout queries shift with subtle changes in the user’s prompt. And as we’ve established before, you can’t predict users’ prompts to ChatGPT. So that means you also can’t predict the fanout queries. Then how do you know what to target in SEO?

So, in trying to figure out how to get cited by ChatGPT, we wanted to look beyond fanout queries and ask a more practical question:

Does ranking for traditional SEO keywords help you get cited by ChatGPT?

To answer this, we took 70 sources that ChatGPT cited across 10 BOTF prompts and ran them through Ahrefs to see what organic keywords each URL was already ranking for. 

(We focused on BOTF prompts only because, as we’ve written about before, those are the only AI search topics where ChatGPT mentions brands. And most of the time, these are the only queries where it cites sources rather than relying solely on its training data. So, that’s where the opportunity is for brands to get traffic and conversions.)

Of these 70 sources, 60% were ranking organically for at least one relevant keyword, and most of them were ranking for three or more. These are traditional, common-sense SEO keywords that one can easily discover doing traditional keyword research in any SEO tool. 

Additionally, half of them were ranking on page one and a third were in the top three positions. 

That’s good evidence that ranking for bottom-of-funnel keywords related to your customers’ pain points helps you get cited by ChatGPT. 

You don’t need to guess what fanout queries to target. You just need to understand the underlying intent behind them and focus on that, which is what successful BOTF SEO already does. 

Below, we look more closely at where ChatGPT citations appear to come from, what this means for your GEO strategy, and where the sources that don’t appear in live web search may be coming from.

Every source ranking for a fanout query also ranked organically for a traditional SEO keyword

While our main takeaway from this study is that you don’t need to know the exact fanout queries in order to get mentioned by ChatGPT, looking at the fanout query data adds a layer that helps us understand the relationship between traditional SEO and cited sources. 

We’ll start with the sources that did rank for fanout queries. 40% of the sources we studied were ranking in the top 10 pages of the SERP for known fanout queries and every one of them also ranked for relevant, more traditional, SEO keywords. 

For example, one of the fanout queries for the prompt shown in the screenshot below was “CRM software that helps reduce dropping leads between stages best CRM lead management features”. And the article outlined in yellow below ranked in Google’s SERP for that fanout query.

Of course no SEO would intentionally go after a query that long and specific. But checking in Ahrefs, we see it also is ranking for extremely normal keywords related to CRMs. 

Every source ranking for a long, hard-to-guess fanout query was also ranking for more traditional, discoverable SEO keywords.

Another 20% of the sources we studied also ranked for relevant SEO keywords but weren’t found in the SERPs for fanout queries. And the last 40% didn’t have any trackable organic presence at all. 

Here’s what that looks like at a glance:

We draw two conclusions from this:

1. The 20% with organic presence and not in the SERP for fanout queries was likely drawn from fanout queries we can’t see. This isn’t a new idea. Others have also noted that it’s highly likely that we are only able to see some of the fanout queries ChatGPT uses.
   
2. If you aren’t ranking for traditional SEO keywords, it’s highly unlikely that you’ll get in the SERPs for fanout queries, and therefore won’t be found via live search.

Overall this data supports the key thesis of our Prioritized GEO strategy: that ranking for relevant, bottom-of-funnel SEO keywords is a key mechanism by which you can get noticed and cited by LLMs (in this case, ChatGPT). However, as we’ll discuss in a later section, citations may not necessarily influence ChatGPT’s answers. 

But very few of the cited sources are ranking for the same keywords

We also checked whether cited sources were ranking for the same organic keywords. If most of them were ranking for the same one or two terms, those keywords would likely be worth targeting.

But we didn’t find any significant overlap, meaning most of the cited sources were ranking organically for different keywords. 

There was some overlap where one or two URLs were ranking for the same keyword, but overall there wasn’t enough of a pattern to tell us anything useful. So, our conclusion is that the more relevant keywords you rank for, the better your chances of getting picked up, but no single keyword seems to be the magic ticket to ChatGPT citations. 

This continues the trend we keep seeing: GEO or AIO seems to be less hackable than SEO, you just need to do the work of publishing helpful content on the topics your customers care about and, slowly, more of them start to be found by LLMs. 

Not all cited sources come from live search

It’s a common assumption that if ChatGPT cites a source, it found it through a live web search. But as we found in our fanout query SERP study, that’s only true about 40% of the time.

So where do the other 60% of cited sources come from? We covered the full picture in that study, but the short version is that no one really knows for certain. 

The leading theories are:

• Additional queries running behind the scenes. Like we covered in the previous section, the fanout queries that we can see are likely just a subset of what ChatGPT is actually searching. 
• Response caching. ChatGPT may be reusing sources from past queries rather than running fresh searches every time, particularly for common buying-intent prompts.
• Training data. There’s evidence that suggests ChatGPT can remember specific URLs from its training data. 
• Hallucinations. Roughly 10% of citations in our study led to error pages. Some sources simply don’t exist.

The honest answer is that there’s a randomness to it. We tend to assume AI is operating logically and systematically, but in reality it’s trained on patterns and probability and that makes its behavior harder to predict and control than traditional search.

Cited sources don’t necessarily influence answers

This entire article has been focused on where citations come from, and overall, we’re interested in anything that helps us understand how LLMs work and gives us practical information to work off of. But, as we’ve written about before, being mentioned has way more impact on traffic and conversion than being cited as a source. 

So the question is does getting cited correlate to being mentioned? 

We’re currently working on a separate study to answer this more directly, but for now we can say that having worked with dozens of clients to increase AI visibility over the past few years and looking at the evidence we’ve seen from other studies, we’ve come to the conclusion that that’s not the case.

No one really knows for sure how these LLM chats work, but evidence tells us that tools like Perplexity and Google AIO retrieve sources first and then build the answer. ChatGPT on the other hand seems to rely much more heavily on training data and generates the answer before adding citations. Because the answer is already written before sources get attached, those citations aren’t shaping what users read. And because your brand is tucked away as a citation (rather than a recommendation ChatGPT is including in its answer), it’s not driving meaningful traffic back to you.

We do track citations for our clients, but we put more weight on mentions because that’s where conversions actually come from. 

To learn more about how we increase and track mentions for our clients, reach out. Or you can sign up for our mailing list and we’ll send you studies and articles about what we’re learning as we go.

That’s exactly where Toro TMS was when they came to us.

Toro TMS makes trucking management software (“TMS”) for bulk haulers — think dump trucks, fuel tankers, and construction materials. It’s a subset of the trucking industry that most TMS platforms ignore because the larger software players are built for general freight. Toro saw that gap and built their product specifically for bulk haulers.

When we met their head of sales, he was building the outbound motion, hiring reps, and running the sales process himself. He knew inbound leads would be critical for long-term growth, but he didn’t have the time or expertise to build an inbound SEO and content marketing engine. So he hired us to build that side of the business while he focused on what he did best: sales.

When we started in early 2025, roughly 5% of Toro’s revenue came from branded inbound. Eighteen months later, that number is over 25%.

Here’s how we did it.

The Starting Point: Branded Keywords Only

When we kicked off in January 2025, here’s what Toro ranked for:

That’s it. Every keyword driving real traffic was someone who already knew Toro’s name. The handful of non-branded keywords they showed up for were all on page 2 or beyond, driving essentially zero traffic.

This is what most B2B companies look like before investing in content: you rank for your own name and nothing else. People who already know you can find you. Everyone else can’t.

What We Rank for Today (18 months later)

Here’s a sample of what they rank for today:

This is just a sample. In total, Toro went from 0 first-page rankings for non-branded keywords to 43 today. Out of the 46 articles we’ve published, 43 of the keywords we’ve targeted now rank on page 1. 

That’s a 93% first-page ranking rate in 18 months. The remaining 3 pieces not on page 1 are mostly newer publications that should reach page 1 in due time.

The Strategy: Pain Point SEO Across Every Keyword Type

Our approach with Toro was the same strategy we use with every client: Pain Point SEO. We identify the keywords that people search when they’re actively looking to buy, and we write content targeting those keywords.

The keywords we’ve targeted for them fall into three types:

Category keywords are the core of the strategy. In short, they’re keywords that indicate someone is shopping for products in your category (read more about Category Keywords). These range from the broadest version of what the product is (the head term, now ranking #1) down to specific features the product offers: payroll, dispatch, load management, ticket management. Each one maps to a real capability of the product. We also layer in specificity where Toro has a niche advantage. Most competitors are fighting over generic software keywords. Toro ranks for keywords specific to their sub-industry because they have content that goes deep on those use cases. Their competitors don’t.

Comparison keywords capture people actively comparing options. These include keywords like “toro tms vs. [competitor]” or “[competitor] alternatives.” 

Template keywords are a type of jobs-to-be-done keyword and catch people one step before the software search. Someone downloading a dispatch spreadsheet template is managing their operations manually. They’re likely outgrowing spreadsheets and will eventually need software.

For Toro, that meant mapping out every way a trucking company owner or fleet manager might search for software. Not just the obvious head terms, but every variation: category keywords for every feature the product offers (payroll, dispatch, accounting, compliance), comparison keywords (best X, X for small companies), and even template keywords that catch people one step before the software search.

These keywords aren’t massive in volume. The biggest gets about 1,500 searches per month. Many get 100 to 200. But most people searching those terms are potential buyers. That’s the whole point of Pain Point SEO. You don’t need 50,000 visitors. You need 500 of the right ones.

How We Wrote the Articles for Toro TMS

We’ve published 46 bottom of funnel, product-centric articles over the course of the engagement. As we’ve written about before, writing quality is equally as important as keyword and content strategy. Even if you target the right keywords, if the writing is low quality (whether from humans or AI), it will massively hurt your chances to both (a) rank and (b) impress your prospects well enough to convert. 

So for Toro, every article we wrote relied on a few foundational principles that we’ve relied on for 8+ years:

(1) We didn’t write generic product comparisons like a third party would. We wrote from Toro’s perspective as a company that deeply understands bulk hauling. The category keyword article doesn’t pretend to be an objective third-party review. It opens by discussing what matters in trucking management, steers toward the specific needs of bulk haulers, and then explains how Toro built their product to address those needs.

This is the approach we laid out in our self-promotional listicles article: you don’t need to pretend you’re unbiased. You don’t need to rank yourself #1 on a fake list. You just need to be honest about what your product does and who it’s for, and go deep enough that the content is genuinely useful to the reader.

(2) We interviewed Toro’s team to get the details. Every article is built on interviews with the people at Toro who know the product and the customer. That’s how we write about things like compliance reporting, ticket management, or the specific challenges of hauling construction and demolition waste. You can’t get that level of detail from Google research or AI generation. It has to come from people who actually know the industry and have a strong opinion on how and why their product or service approaches the problem the way it does. People want these unique perspectives. They live inside key employees’ heads in your organization. You need a process to extract those perspectives and get them into your content.   

(3) We wrote at the depth of a sales conversation. When a bulk hauler is evaluating TMS options, they want to know how it solves their problems in extreme detail. For example, for Toro they want to know exactly how the software handles dispatch, invoicing, driver management, and compliance. They don’t want a 300-word overview or a basic table with checkmarks, they can ask ChatGPT for that. Your content needs to go deeper. So our articles did. They go deep on features, workflows, and specific use cases because that’s what converts readers into demo requests.

The Results: Rankings

We covered the keyword types above. Here’s the distribution of where those keywords rank:

30 keywords in positions 1 through 3; 13 more in positions 4 through 10. That’s 43 out of 46 keywords on page 1, an 93% hit rate.

The Results: Demos Booked

This is the number that actually matters.

In January and February 2025, Toro booked zero inbound demos. By June, they were at 4 per month. By October, they hit 17. After a natural dip in November, the numbers climbed back to 15 in March 2026 and have been consistently in double digits since.

Over the trailing 12 months, Toro is averaging roughly 10 inbound demos per month from content. For a niche B2B software company selling to bulk haulers, that’s a significant pipeline. Every one of those demos is someone who found Toro through a blog post, read about the product, and decided to book a call. The sales team didn’t have to prospect them.

The result: inbound went from 5% of revenue to over 25%.

In even more detail on results that matter to their business, from Q1 ‘25 to Q1 ‘26:

• Opportunity volume is up 350%
• Booked revenue is up 362%

The Results: AI Visibility

As we’ve written about before, this product-centric content strategy improves AI visibility. The same content that ranks on Google also gets Toro recommended by AI search tools. We track this through Traqer, our AI visibility tool.

Across 18 topics and 90 prompts, Toro TMS has:

• 91% visibility on Google AI Overviews — when someone asks Google’s AI about trucking software, Toro appears in 91% of relevant responses
• 81% visibility on AI Mode — Google’s newest AI search feature
• 59% visibility on Perplexity
• 50% visibility on Gemini
• 35% visibility on ChatGPT — lower here, but growing

Of 90 tracked topics, Toro has some visibility in 86 of them and high visibility (appearing in more than 50% of prompts) in 65. That’s not a handful of lucky mentions. That’s broad, consistent AI coverage.

This matters because Toro’s customers are exactly the kind of people who are starting to use AI search to find software. A trucking company owner asking ChatGPT “what’s the best TMS for a dump truck fleet” is going to get Toro in the answer because we’ve published content that gives the LLM the details it needs to make that recommendation.

The content that ranks on Google is the same content that gets cited by LLMs. If you write detailed, specific content about your product and your market, AI search picks it up. That’s the thesis behind our topic-based GEO approach, and Toro is a clean example of it working.

Building an SEO and AI Inbound Channel via Content

Toro’s situation is extremely common among our clients. A head of sales, a VP of marketing who just got hired, a founder who knows they need inbound but doesn’t have the team to build it.

The playbook is straightforward:

1. Identify the buying-intent keywords in your space. For Toro, that’s every variation of “trucking software” plus adjacent searches around payroll, dispatch, invoicing, and compliance.
   
2. Write content that goes deep on your product’s strengths. Not generic overviews. Detailed, interview-driven content that explains exactly what your product does and who it’s for.
   
3. Be patient. Toro’s first two months were zero demos. That’s normal. SEO takes time. But once the rankings hit, the pipeline compounds month over month.
   
4. Create content that works across AI and SEO. We target high intent keywords on the SEO side, but make the content specific and detailed enough that LLMs will find it and recommend that brand to specific use cases like we talk about in our Invisible Prompts post.

Across many of these engagements, we’ve also heard the same story from founders, content marketing teams, and SaaS marketers that their previous SEO agency ran a monthly technical audit and showed them a running list of issues they were fixing or “actively working on.” Most of those issues were real, but the ones that actually impacted the site’s ability to rank for keywords that drive organic traffic, qualified leads, trial signups, demo requests, and pipeline usually required engineering or web development work to fix, which rarely got prioritized.

In our experience, most technical SEO audits mix together two very different types of problems. That’s why when we audit SaaS websites, we separate these issues into two categories: 

• Structural technical SEO: These are decisions made during a site redesign, replatforming, or major launch that can compound for years and are often expensive to reverse. For SaaS companies, it includes decisions such as whether to host the blog on a subdomain or subfolder, whether the marketing site renders server-side or client-side, and how the overall site structure is organized.
• Maintenance technical SEO: These are recurring, smaller fixes that audit tools tend to flag, such as broken links, redirect chains, missing meta descriptions, schema, and other crawl hygiene issues. These issues are real, but they usually don’t compound the same way and can be handled through quarterly audits or basic monitoring.

In this article, we walk you through the technical SEO issues we most often see on SaaS websites, in roughly the order they impact rankings and pipeline.

Issue 1: The Blog Is Hosted on a Subdomain Instead of a Subfolder

Google has publicly said that subdomains and subfolders are treated equally, but across many SaaS SEO engagements, we’ve seen that hosting content in a subfolder consistently outperforms a blog on a subdomain in both organic traffic and rankings.

That’s why we treat subfolder hosting as the default recommendation for any SaaS company starting a blog or planning a migration, and we often recommend migrating existing subdomain blogs back to subfolders when the SEO upside justifies the engineering effort.

We usually see this problem on SaaS websites where the marketing site is built on a custom framework or static site generator, and the engineering team doesn’t want to maintain a CMS as part of the main site. So they just stick a blog running on WordPress, Webflow, or another CMS on a subdomain. But this comes with a serious SEO tradeoff that engineering teams usually don’t consider. 

The problem is that subdomains behave like separate domains, and the backlinks you’ve earned through link building, the brand authority, and the topical relevance built on your main domain don’t carry over cleanly to the subdomain blog, even though both appear to be the same brand to users. As a result, the blog ranks with less authority than it would if it were a subfolder on the root domain.

Our Circuit case study is one of the clearest examples. Circuit had previously moved its blog to a subdomain and saw rankings drop. When they moved the blog back to a subfolder on the root domain, traffic recovered and, within six months, grew from around 920 sessions per month to over 14,577. 

How to Fix?

Host your blog at /blog/ on your root domain.

If your blog is already on a subdomain, plan a migration that includes:

• A reverse proxy or CMS setup that serves the blog from /blog/ on the main domain
• 301 redirects from every existing subdomain URL to its new subfolder URL
• Updated internal links across the marketing site, footer, and product to point to the new URLs
• An updated XML sitemap submitted to Google Search Console

If the content is intended to rank in organic search and contribute to your overall SaaS SEO strategy, content marketing, and link building efforts, it should usually reside on the main domain. 

We also recommend the same principle to other content hubs that often end up on subdomains for engineering convenience, such as help centers, community forums, resource libraries, and changelogs.

Issue 2: The Marketing Site Renders Client-Side Instead of Server-Side

Most SaaS marketing sites are built by the same engineering team that builds the product, on the same JavaScript framework (typically React, Next.js, Vue, or Nuxt). That choice makes sense from a stack-consolidation and engineering-ownership perspective. 

The problem is that when the marketing site renders entirely on the client side, search engines and their crawling algorithms have to work much harder to see your content, which usually results in delayed indexing, partial content rendering, and product pages that struggle to rank for keywords they should easily own.

The technical issue is that search engine crawlers don’t render JavaScript the way browsers do. Google does have a second-pass rendering stage for JavaScript, but it runs on a delay (sometimes days or weeks after the initial crawl), and pages with heavier JavaScript or rendering errors often don’t make it through cleanly. Other search engines, including Bing and the crawlers behind AI search products like ChatGPT and Perplexity, render JavaScript less reliably or not at all. 

We see this most often on SaaS sites built on Nuxt, Angular, or custom React frameworks, where the team has defaulted to client-side rendering for the marketing pages. In these engagements, we typically find that a meaningful portion of web pages are missing from Google’s index, even though every page is internally linked and submitted in the XML sitemap. 

Once the marketing site is switched to a server-rendered or statically generated configuration, indexation usually recovers within weeks.

You can diagnose this on your own site in under a minute. Disable JavaScript in your browser’s developer tools (in Chrome, open DevTools, then use the command menu to search for “Disable JavaScript”) and reload any marketing page. If the main content, headings, or internal links disappear, search engines that don’t fully execute JavaScript are seeing the same empty page.

How to Fix?

For the marketing site, use server-side rendering (SSR) or static site generation (SSG). Both deliver fully rendered HTML to crawlers on the first request, eliminating rendering delays and reliability issues.

If you’re on Next.js, this means using getServerSideProps or getStaticProps (Pages Router) or server components and static export (App Router). On Nuxt, it means running in ssr: true mode rather than SPA mode. For custom React or Vue setups, it means adding a server-rendering layer or migrating to a framework that handles it.

The deeper structural fix is to separate the marketing site stack from the product app stack. The product app can remain a client-rendered SPA since users authenticate before they see it, and SEO doesn’t apply. The marketing site should be its own deployment, ideally on a static site generator or an SSR framework, with content management handled through a CMS that the marketing team can edit without filing engineering tickets.

If switching frameworks isn’t realistic in the near term, the interim fix is dynamic rendering or pre-rendering specifically for crawlers. Services like Prerender.io serve a pre-rendered HTML version to crawlers while serving the SPA to users, which solves the indexation problem without changing the underlying stack. This works as a bridge but isn’t a long-term substitute for proper SSR or SSG optimization.

Issue 3: Thin and Duplicate Pages Don’t Have a Clear Canonical or Noindex Policy

Most SaaS websites accumulate thin pages or duplicate content over time, but many don’t have a consistent canonical or indexation policy for handling them.

We’ve seen login-gated marketing pages appearing in search results, internal search pages getting indexed, and UTM-tagged URLs attracting external links that should have pointed to the canonical version.

Individually, these issues may look minor. Collectively, they create a search profile where the marketing site has far more indexable web pages than it should. This can dilute link authority across near-duplicate pages, waste crawl budget, and make the site appear lower-quality because of its thin and duplicate content footprint.

Over time, this can also lead to indexing and ranking issues, especially on larger SaaS sites with hundreds or thousands of blog posts, feature pages, integration pages, comparison pages, resource pages, and template-based URLs.

How to Fix?

Apply canonical and noindex rules consistently at the template level so that every URL of the same type receives the same treatment automatically.

• Internal search URLs: Add noindex tags at the template level and exclude these URLs from XML sitemaps. Also, verify your robots.txt file isn’t accidentally blocking important sections of the site while handling internal search URLs.
• Filter, sort, and parameter URLs: Add a <link rel=”canonical”> tag pointing back to the clean, unparameterized version of the page. For very heavy filter combinations, consider adding noindex to specific parameter groups.
• UTM-tagged URLs: Make sure UTM parameters canonicalize back to the clean URL. If these URLs are being linked externally, update the source links when possible so authority flows directly to the canonical page.
• Paginated pages: Use a self-referential canonical tag on each paginated URL rather than canonicalizing page 2, page 3, and beyond back to page 1. Keep paginated pages crawlable and indexable so search engines can reach the deeper content linked from them. Avoid blanket noindexing paginated URLs, since that can also prevent crawlers from following links to deeper content. rel=next and rel=prev tags are no longer required for Google, though leaving them in place generally doesn’t hurt.
• Login-gated marketing pages: Add a noindex directive or serve a 401 or 403 status code, depending on whether you want the URL discoverable in any form.

Once these rules are in place, you can audit the indexation report in Google Search Console quarterly. Look for sudden growth in indexed URLs, especially when that growth is not tied to new content production. That usually means a new template, parameter group, or URL pattern is being indexed without the right canonical or noindex rule.

If your indexed URL count is growing faster than your content volume, there’s almost certainly a canonical or noindex policy issue worth investigating.

Issue 4: Pages Don’t Follow a Scalable URL Taxonomy

Most SaaS product sites already have CMS collections for features, integrations, comparison pages, use cases, industries, FAQ pages, and help docs. The issue we often see is that these page types are created in isolation.

For example, different teams produce feature pages, integration pages, comparison pages, or industry pages, and each team organizes its pages around its own campaign or internal workflow.

The site has organized page types, but no consistent structure for how commercial pages relate to one another.

For example, a SaaS company might have:

• A feature page targeting “[feature] software”
• A use case page targeting “[job to be done] software”
• An industry page targeting “[feature] software for [industry]”
• An integration page targeting “[partner] integration”
• A comparison page targeting “[competitor] alternative”

Without a clear taxonomy, these pages often overlap, compete, or remain disconnected from one another. The site ends up with multiple commercial pages targeting adjacent intent, but no clear parent-child structure, internal linking model, or rule for which page should rank for which query type.

This is where URL architecture becomes more than a formatting issue. A strong SaaS URL taxonomy is part of your overall site structure and defines how commercial intent is organized across the site.

When this isn’t defined, several issues show up over time:

• Feature, solution, and industry pages start targeting the same keywords.
• Integration pages sit isolated from relevant feature and use case pages.
• Comparison pages are launched as one-off landing pages rather than as part of a scalable competitor hub.
• Internal linking becomes manual instead of template-driven.
• Breadcrumbs don’t reinforce the commercial hierarchy.
• XML sitemaps group pages by CMS origin rather than by search intent.
• Reporting can’t easily show which commercial page type is driving rankings, qualified traffic, trials, demos, or pipeline in Google Analytics or your internal dashboards.

How to Fix?

Build a commercial URL taxonomy and a clear site structure before scaling new SaaS landing pages. 

This means defining the role of each page type, the query class it should target, the URL pattern it should use, and how it should link to related pages.

A simple taxonomy might look like this:

Once the taxonomy is defined, apply it across the site:

• Move existing pages into the correct URL structure when the SEO benefit outweighs the migration risk.
• Add 301 redirects from old URLs to new URLs.
• Update internal links so related features, use cases, industry, integration, and comparison pages reinforce each other.
• Add breadcrumb logic that matches the taxonomy.
• Segment XML sitemaps by commercial page type.
• Track rankings, traffic, conversion rates, and pipeline by page type, not just by individual URL.

For SaaS companies, this matters most once the site has more than a handful of product-led SEO pages. At that point, every new feature, integration, use case, industry, and comparison page either strengthens the site’s commercial search architecture or adds another disconnected URL that has to be manually managed later.

A strong taxonomy lets the site compound and supports a scalable, high-quality content strategy, consistent on-page SEO, and ongoing optimization across the site. Each new page fits into a known structure, links to related pages by default, appears in the right sitemap, inherits the right breadcrumbs, and has a clear role in the broader keyword strategy.

Issue 5: 404s, Redirect Chains, and Broken Internal Links

For SaaS sites that undergo frequent product changes, redesigns, and migrations, this is one of the clearest examples of a maintenance-related technical SEO issue. It may not require daily attention, but it does need regular cleanup.

The most common causes of these issues are when features are renamed, and the old feature pages start returning 404s, when blog posts are deleted but remain in the XML sitemap, or when site migrations leave behind redirect chains where one URL passes through two or three intermediate URLs before reaching the final destination. 

None of these issues is critical or usually breaks the site on its own. But over time, they create technical drag, leading to crawlers wasting time on dead URLs, internal links passing through unnecessary redirects, and users occasionally landing on pages that no longer exist, which hurts user experience and crawl efficiency.

The most common patterns we see are:

• 404s with internal or external links pointing to them: Pages get deleted, but the links pointing to them don’t get updated. If the old URL had backlinks from your link building efforts, rankings, or meaningful internal links, redirect it to the closest relevant live page instead of letting those signals die on a 404.
• Redirect chains: URL A redirects to URL B, which redirects to URL C. The cleaner setup is for URL A to redirect directly to URL C.
• Redirect loops: URL A redirects to URL B, and URL B redirects back to URL A. This makes the page inaccessible to users and crawlers.
• Internal links pointing to redirected URLs: The link technically works, but every click and crawl has to pass through a redirect. 
• 302 redirects used for permanent moves: If a page has permanently moved, use a 301 redirect instead of a 302 so search engines understand that the new URL should replace the old one.
• Soft 404s: These are pages that return a 200 status code but display “not found,” “page no longer exists,” or similarly thin content. The response code says the page exists and functions normally, but the content says otherwise.

How to Fix?

Use Screaming Frog, Ahrefs Site Audit, Semrush, or a similar crawler to identify 404s, redirect chains, redirect loops, soft 404s, and internal links pointing to redirected URLs.

Then clean up each issue based on the URL type:

• For 404s, decide whether to restore the page, redirect it to the closest relevant live equivalent, or leave it as a 404 if the page was intentionally removed and has no meaningful links or traffic.
• For pages that are permanently removed and should not come back, consider using a 410 Gone status code instead of a 404. This gives search engines a clearer signal that the URL has been intentionally removed.
• For redirect chains, update the first redirect to point directly to the final destination URL.
• For redirect loops, fix the redirect rule causing the loop.
• For internal links pointing to redirected URLs, update the source links so they point directly to the final URL.
• For 302 redirects, replace them with 301 redirects when the move is permanent.
• For soft 404s, either improve the page so it serves a real purpose, redirect it to a relevant live page, or return a proper 404 or 410 status code.

After the cleanup, remove dead URLs from XML sitemaps.

For SaaS businesses, especially fast-moving startups that undergo multiple redesigns, migrations, feature launches, and pricing changes, this cleanup should happen right after each change goes live, so issues are caught immediately rather than accumulating into broader crawl and internal linking problems.

Issue 6: Schema Markup Is Missing or Doesn’t Match the Page Type

Schema markup is structured data that tells search engines what type of content is on a page, beyond what they can infer from the HTML alone. For a SaaS company, a well-implemented schema can unlock rich results and rich snippets in the SERPs and feed into Google’s knowledge panel for the brand.

Most SaaS websites have either no schema markup at all or generic schema generated by the theme or website template they use (usually the case on WordPress websites). 

The implementation is inconsistent across page types, and the schema often doesn’t match the page’s actual content.

The most common patterns we see are:

• No schema markup at all. The site has no structured data on any page, which doesn’t break anything but means it misses out on rich results opportunities and provides less context to AI search products.
• Wrong schema type used. A blog post tagged with Product schema, or a feature page tagged with Article schema. The schema technically validates but doesn’t accurately represent the content.
• Required fields missing. Schema types have required and recommended fields. When required fields are missing, the schema may not be eligible for rich results, even when it validates as syntactically correct.
• Product or SoftwareApplication schema added to the homepage to surface star ratings. A common misuse of schema on SaaS sites is adding the Product or SoftwareApplication schema with aggregateRating fields to the homepage, often pulling those ratings from third-party review sites such as G2, Capterra, or TrustRadius. Google’s structured data guidelines explicitly prohibit this. The reviewed item must be the main subject of the page, the ratings must be backed by reviews visible to users on the same page, and the reviews can’t be self-serving. Sites that misuse review markup this way can lose rich result eligibility across the entire site, often without warning.

How to Fix?

Audit the existing schema, then implement appropriate schema types for each page type at the template level so that every page of the same type receives the same treatment automatically.

• Audit: Run the Rich Results Test and Schema Markup Validator on a representative page of each template (homepage, blog post, feature page, integration page, comparison page, pricing page, help doc). Note what schema exists, what’s missing, and what’s flagged with errors.
• Implement at the template level: Add schema to each page template so new pages inherit the correct markup automatically. Don’t add schema page by page.

The most common schema types for SaaS sites are:

• Organization site-wide: company name, logo, social profiles, contact information. Add to every page that describes the organization (homepage, about page, etc.) through a global template.
• Article or BlogPosting on blog posts: author, publish date, modified date, headline, image.
• SoftwareApplication on product or feature pages: name, application category, operating system, offers. Only include aggregateRating if the page itself displays legitimate first-party reviews. Don’t pull ratings from third-party sites like G2 or Capterra or use self-reported numbers.
• HowTo on procedural content like setup guides or tutorials.

For SaaS sites, schema is a low-effort, low-risk investment that complements high-quality content with meaningful upside in search visibility and click-through rates from rich snippets. The goal shouldn’t be to add schema everywhere for its own sake, but to ensure every page type has schema that matches its purpose, and that the markup actually reflects the content visible to users.

Issue 7: Chasing Perfect PageSpeed Scores

Core Web Vitals are rarely the main ranking bottleneck for SaaS websites.

Most modern SaaS sites already use decent hosting, CDN delivery, caching, compressed assets, and frontend frameworks that make pages fast enough to meet basic performance thresholds for site speed and load times. 

Because of that, we rarely see cases where moving a page from a “good” PageSpeed score to a perfect score materially changes rankings. The issue is that teams often focus on the overall PageSpeed score rather than the specific performance problem affecting user experience on the page.

The most common patterns we see are:

• Oversized HTML documents: Googlebot can read large HTTP responses, but that does not mean large HTML documents are good for performance. HTML documents over 2MB often point to bloated templates, inline base64 images, large JSON blobs in the page source, excessive inline CSS or JavaScript, or uncompressed HTML.
• Hero image and web font issues hurting LCP: Largest Contentful Paint is often the hero image or above-the-fold heading on a SaaS marketing page. If the hero image is too large, served in the wrong format, not preloaded, or paired with render-blocking web fonts, the page can feel slow even if the overall score looks acceptable.
• Cumulative Layout Shift from cookie banners, popups, and lazy-loaded content: CLS often comes from elements that load late and push existing content down. Cookie consent banners, popups, embedded widgets, and lazy-loaded images without dimensions are common causes.
• Render-blocking JavaScript and CSS: Scripts and stylesheets that block the initial render delay everything else. This often comes from third-party tags loaded synchronously, large CSS files without critical CSS extraction, and JavaScript that is not deferred or loaded asynchronously.
• INP issues from heavy JavaScript: Heavy JavaScript bundles, third-party scripts, chat widgets, and analytics libraries can make a page feel unresponsive even when LCP and CLS are fine.

How to Fix?

Treat Core Web Vitals as a threshold to pass, not a score to perfect. Start by auditing different page types, not just the homepage.

Then fix the issue based on what the audit shows:

• For oversized HTML documents: Compress HTML with gzip or Brotli, move large JSON blobs out of the initial HTML, replace inline base64 images with proper image URLs, and reduce unnecessary inline CSS and JavaScript.
• For LCP issues: Compress hero images, serve them in WebP or AVIF, set explicit width and height attributes, preload the LCP image, use font-display: swap, and preload critical fonts where needed.
• For CLS issues: Add explicit dimensions or aspect-ratio CSS to images and embeds, reserve space for cookie banners and embedded widgets, and avoid inserting content above existing content after the page loads.
• For INP issues: Break up long JavaScript tasks, reduce unused JavaScript, load third-party scripts with async or defer, audit tracking libraries, and move non-critical JavaScript out of the initial bundle.
• For render-blocking resources: Defer non-critical JavaScript, extract and inline critical CSS, reduce large stylesheet dependencies, and delay non-essential third-party scripts like chat widgets, heatmaps, and marketing tags until after the page is interactive.

For SaaS sites, page speed optimization should be targeted rather than exhaustive. Fix the templates and scripts that create real performance problems on important web pages. Don’t spend weeks chasing a perfect score when the site already passes Core Web Vitals, and the same engineering time could be spent on higher-impact structural SEO efforts.

Issue 8: XML Sitemaps and Robots.txt Don’t Match What Should Be Crawled and Indexed

XML sitemaps and robots.txt files are supposed to help search engines understand which URLs should be discovered, crawled, and indexed.

We often see XML sitemaps that include redirected URLs, 404 pages, noindexed pages, parameter URLs, staging URLs, thin CMS-generated pages, or old blog URLs that should no longer be submitted.

At the same time, robots.txt files sometimes block important sections of the site from being crawled, including blog directories, integration pages, JavaScript assets, CSS files, or documentation pages that should be accessible to search engines.

The most common issues we see are:

• Noindexed URLs in the XML sitemap: These pages are being submitted to Google even though the page itself says not to index them.
• Redirected URLs in the XML sitemap: The sitemap includes old URLs that now redirect elsewhere instead of the final canonical URLs.
• 404 or 410 URLs in the XML sitemap: Deleted pages remain in the sitemap long after they have been removed.
• Parameter or filtered URLs in the XML sitemap: URLs with filters, sorting parameters, tracking parameters, or internal search parameters get submitted even though they are not meant to rank.
• Important pages missing from the XML sitemap: Feature pages, integration pages, comparison pages, or new blog posts are live and internally linked, but not included in the sitemap.
• Robots.txt blocking important content: Blog directories, docs, integration pages, or page assets are blocked from crawling, even though they need to be accessible for proper rendering and indexation.
• Robots.txt used where noindex would be more appropriate: If a page is blocked in robots.txt, Google may not be able to crawl the page and see the noindex directive. For pages that need to be removed from the index, a page-level noindex is usually the cleaner solution.

How to Fix?

Audit your XML sitemap and robots.txt file at least quarterly, and after any redesign, migration, CMS change, or major template launch.

Your XML sitemap should only include URLs that are:

• Canonical
• Indexable
• 200-status
• Important enough to be discovered and crawled
• Not blocked by robots.txt
• Not parameterized or duplicated versions of another page

Remove any URLs that are noindexed, redirected, broken, canonicalized to another URL, blocked by robots.txt, or not meant to rank.

Then review the robots.txt file to ensure it is not blocking important pages or assets. In most cases, SaaS sites should avoid blocking major content sections like /blog/, /features/, /integrations/, /compare/, /solutions/, /docs/, or /help/ unless there is a clear reason. 

While reviewing robots.txt, also confirm the sitemap URL is declared at the bottom of the file (e.g., Sitemap: https://www.yoursite.com/sitemap.xml) so crawlers can find it even when they don’t check Google Search Console.

Also, avoid using robots.txt as your primary indexation control. Robots.txt controls crawling, not indexing. If a page should not appear in search results, use a noindex directive instead, and make sure the page is crawlable long enough for search engines to see that directive.

For SaaS websites, the sitemap should list the URLs you want search engines to crawl and index, and robots.txt should only block areas that genuinely should not be crawled. 

Issue 9: Staging, Preview, or App URLs Are Indexable

For SaaS companies, this issue usually becomes more important as the engineering and marketing stack gets more complex. The more environments, preview systems, CMS collections, and app surfaces the company has, the easier it is for non-production URLs to leak into search.

There may be a production marketing site, a staging site, CMS preview URLs, Vercel or Netlify deployment URLs, a documentation subdomain, a help center, a public product app, partner landing pages, and old campaign pages that were never fully removed. Over time, some of these URLs get discovered and indexed by search engines.

The issue is not just that these pages look messy in Google but that they can create duplicate versions of important marketing pages, expose thin or unfinished content, and make indexation harder to manage.

The most common patterns we see are:

• Staging subdomains indexed in Google: URLs like staging.company.com, dev.company.com, or test.company.com appear in search results.
• Preview deployment URLs indexed: Vercel, Netlify, Webflow, or CMS preview URLs get indexed alongside the production page.
• Duplicate marketing pages on alternate hosts: The same homepage, feature page, or blog post exists on both the production domain and a staging or preview URL.
• Public app URLs showing in search results: Product app URLs, login-gated pages, onboarding screens, or public user-generated pages get indexed even though they are not part of the marketing site’s SEO strategy.
• Old campaign or landing page environments still live: Temporary pages created for launches, ads, partner campaigns, or sales enablement stay accessible long after the campaign ends.
• Sitemaps include non-production URLs: XML sitemaps accidentally include staging, preview, or alternate-domain URLs instead of only canonical production URLs.

How to Fix?

Start by auditing what Google has indexed across your main domain and common subdomains.

Search for indexed URLs across different environments, like:

• site:staging.company.com
• site:dev.company.com
• site:company.vercel.app
• site:company.netlify.app
• site:app.company.com
• site:preview.company.com

Then decide how each environment should be handled.

For staging and preview environments, the cleanest fix is to require authentication so these URLs aren’t publicly crawlable in the first place. If authentication is not possible, add a noindex directive at the template level and make sure the pages are not included in XML sitemaps.

For duplicate production content on preview or deployment URLs, add canonical tags pointing to the production URL, but don’t rely on them alone in staging environments. If the environment should not be public, block access or noindex it.

For app URLs, decide whether the content has search value. Most authenticated app pages should not be indexable. Public app pages, user profiles, dashboards, reports, or shared assets should have a clear indexation policy based on whether they are useful landing pages or thin or duplicate content.

For old campaign pages, either keep them live and indexable if they still serve a purpose, redirect them to the closest relevant page, or noindex or remove them if they are no longer useful.

After cleanup, remove non-production URLs from XML sitemaps, and use the Removals tool only when you need to temporarily hide sensitive or urgently problematic URLs until the permanent fix takes effect.

Improve Your Technical SEO With Grow and Convert

Across hundreds of B2B SaaS engagements, we’ve found that technical SEO lays the foundation for the rest of the work to compound. When the structural decisions are right, every piece of content you publish, every internal link you add, and every keyword you target builds on a stable base. When they’re wrong, the rest of the work fights against a site that isn’t built to rank.

That’s why we treat technical SEO as a finite set of decisions to make well, not an ongoing retainer-level discipline. We make recommendations and fix the structural issues once, and audit the maintenance items quarterly. Then,  we put our time and budget into the work that actually moves the pipeline, like keyword research, content production, internal linking, and external link building tied to keywords that drive trials, demos, and revenue.

For more on the broader SaaS SEO strategy that technical work supports, see our articles on B2B SaaS SEO and Pain Point SEO.

• Our Agency: If you want to hire us to execute a conversion-focused SEO strategy built around driving demo requests, trial signups, and pipeline, not just traffic, you can learn more about working with us here.
• Join Our Team: If you’re a content marketer, writer, or SEO who’d love to do SEO and content marketing in this way, we’d love to have you apply to join our team.
• Our Course and Community: Individuals looking to learn our agency’s SEO and content strategy and become better marketers, consultants, or business owners can join our private course and community, taught via case studies and presented in both written and video formats.

This made some waves. It was syndicated or summarized by outlets like Search Engine Land, which spurred more articles from smaller brands like this agency who straight up called this a “black hat SEO” tactic (eye roll).

We also heard about it directly from clients who sent us the article or told us other advisors told them these product category list posts weren’t helping. 

We saw Lily’s article when it came out and discussed it in depth internally. Lily Ray is a well-respected voice in the SEO community, including us, but in this case, our evidence strongly suggests her takeaway is wrong.

Across dozens of clients and hundreds of articles, we see no evidence of Google cracking down on self-promotional listicles. We also think the evidence in her article itself doesn’t support this thesis, as we explain below. 

What we think is actually happening is simpler: Google is cracking down on bad content. AI slop or human slop. Bad content absolutely hurts your SEO long term, whether it comes in the form of self-promotional listicles or anything else. And frankly, this isn’t new. It’s been true for decades.

In our client data, we’re seeing self-promotional listicles be just as effective now as they were years ago when we first started talking about the importance of this kind of bottom-of-funnel content. The key is that, like all content, they need to be written well. They need to be honest, and they need to be specific and detailed enough to be genuinely useful. 

We discuss all of this with data and examples below. 

Issue #1: Product List Posts Don’t Have to Be ‘Self-Promotional Listicles’

Before we get into the data, there’s an important detail that dictates whether your category list post is a misleading “self-promotional listicle” or not: force ranking your list and calling yourself the best. 

This is how Lily defines a “self-promotional listicle”:  “ranking the best companies or products in their niche and placing themselves in the #1 spot. The common thread is that the company publishing the blog post ranks themselves, and/or their own products, in the top position.”

Even worse is if you then pretend like the list is based on some objective analysis where you evaluated all the options and ended up “objectively” concluding your product is the best. 

We absolutely agree with Lily that this type of content is misleading. It’s also unnecessary.  

We routinely get all the benefits of ranking for these valuable “best X software” type keywords without positioning our clients’ articles as a ranked list of the best products. Searchers of these keywords are just looking for a list of good options, with clear explanations of which product is good for which situations. That doesn’t need to come with a ranking. 

In fact, we think not force ranking your list better fits the search intent of these keywords because not every product is good for all situations. You don’t know the specifics of every reader’s situation, so it’s presumptuous to call any product “the best”.

Discussing Your Product First Doesn’t Have to Mean Saying It’s The Best

Now, as a marketer, you should discuss your product first, simply because eyeballs drop the further down the page you go. You’re just giving yourself less exposure if you put yourself last. 

But as our examples below show, you can do that honestly and transparently. Many of our introductions include some version of “We’ll start with a deep dive into our own product and how we designed it to fulfill the criteria above.”

That’s simply telling the reader: “We’re proud of our product. Here are the choices we made when building it, who it’s a fit for, and why. And if it’s not right for you, here are other major players in the space.” What’s misleading about that? 

We’ve ranked for hundreds (maybe even a thousand or more) keywords like this over the years for 100+ clients. We never position them as force-ranked lists, and our articles rank for the keywords and convert extremely well (examples below).

Issue #2: There’s No Evidence That List Posts Are Being Dinged by Google

The core evidence in Lily’s article for a possible Google “crackdown” on list posts is showing declining traffic graphs of SaaS company blogs and noting that those blogs happen to have hundreds of “self-promotional listicles”.

But the self-promotional listicles only make up a tiny fraction of the total content on those blogs. She never isolates and examines the rankings and traffic of just those listicles. 

Here’s a table summarizing the traffic decline, but importantly, the number of self-promotional listicles versus total posts of every example in her article: 

Look at the last column. In all of the examples, the self-promotional listicles make up less than 10% of the total posts; for most, less than 5%. For some, less than 1%. So if the overall blog traffic is declining, how do we know it’s because of these listicles? 

We don’t.

To Lily’s credit, she never directly says any of this data proves anything is wrong with self-promotional listicles. It’s just insinuated. But the insinuation is strong. 

I mean, just look at the title and subtitle:

It’s not ambiguous what this is implying.

And as I mentioned in the intro, even though her post is implying and not proving, it got picked up by a bunch of other outlets. Many of those outlets went further than her to conclude outrageous things like saying these listicles are “black hat SEO”. The insinuation is strong enough to get people to jump to that conclusion. That matters because people will start changing their marketing strategy based on these insinuations. 

So what data would we need to see to really prove this? 

Well, to start, if Google is indeed cracking down on self-promotional listicles, you should see a decline in their rankings. Otherwise, what does a “crackdown” by Google even mean if those pieces aren’t being pushed down or off the SERP? 

We have that data from dozens of clients, and we’re simply not seeing any kind of widespread rankings hit on these articles. We’ll show a few examples to illustrate.

Our Data: Multiple Examples of Self-Promotional Listicles Doing Just Fine

Here are multiple examples of actual rankings of product category list posts from many of our clients. The data is from Ahrefs rank tracker, which we use internally. It’s never perfect, but directionally good enough. I’m showing a graph of the ranking position over the past year or so, including the late Jan 2026 period where Lily is saying all of the declines in her examples occurred. 

B2B tour operator software

This one has been ranking number one since the spring of 2025. You can see that over the course of a year, it’s been sitting at #1 almost the entire time. 

It’s also performing well on AI visibility, being cited and getting our client a brand mention for various variations of that term: 

Trucking management software

Also published in the spring of last year (about a year before I’m writing this) and after the first month, it consistently hovered in the top 10 for the entire year. No signs of any cracking down. 

Retainer management software

Steady for Nov 2025 to now. Not a sign of a dip in late January 2026. If anything, it seemed to move up a couple of spots then. 

AQM software 

Totally steady. I included this to show an example of natural ranking fluctuation, which you can see in April 2026 here. Google briefly removes the blog post, but after a few weeks, it moves right back to its #1 spot. This is normal fluctuation. 

Best private school for autism

Here’s a non-SaaS example. Also a natural ranking fluctuation in October 2025, but it goes right back to #1. No sign of any crackdown in late January. 

Best concussion clinics

Here’s one in healthcare, a field where Google is notoriously strict. This one has a bunch of other URLs Google tests for this keyword, and an entire month in Oct 2025 where the blog post fell off the SERP. But once again, it came right back. No late January dip. 

There are more…

These are just a few examples. We have tons of others. We debated how to show this data and whether we needed some large statistical analysis of hundreds of these posts in case people wondered if we cherry-picked the few that happened to maintain their rankings. We ultimately decided it wasn’t worth the effort or the downside of exposing a massive amount of client content strategy just to prove the point.

We think these six examples across different industries are enough to show that no widespread crackdown on these types of posts appears to be happening. You can absolutely still achieve strong, stable rankings for valuable category keywords without seeing signs of decline.

If you don’t trust our data, that’s fine; we urge you to just look at your own. Either you’ve never published product category list posts, in which case, why are you even reading this far? Or you have, which means you can look at your own rankings; you don’t have to trust us. 

What If It’s Just AI-Generated Content That’s Getting Dinged?

One of the most notable parts of Lily’s article is at the end, when she notes a couple of other common characteristics of the blogs she examined. One key pattern is that she ran them through an AI writing detector, and it said all had a 100% confidence that the text was AI-generated.

That’s a massive red flag. Folks have published studies showing ample evidence that Google is cracking down hard on sites with a heavy amount of low-quality AI-generated text. 

That alone could explain the blog-wide traffic declines in her examples. 

But I also want to highlight qualitative differences in writing quality between her examples and the posts we write for clients. This could also be hurting long-term ranking ability, whether or not a human or AI wrote them. 

Most ‘Self-Promotional Listicles’ Are Just Bad Writing (Examples)

Lily shares a few examples of specific posts in her article, which show clear examples of how not to write these bottom-of-funnel product category posts. 

First, there’s a post on top-rated test management tools. 

Look at the opening sentence: 

This is corporate marketing gobbledegook that doesn’t say anything useful. It also has questionable claims and unnecessary insertions of “2025”:

• No evidence or reasoning is given to support the claim that software delivery cycles started accelerating in 2025. It’s almost certainly not true. 
• In fact, test management tools have been a thing for a long time. Why are we even talking about 2025?
• Is “software delivery cycles accelerating” really the reason people need these tools? Software testing tools have been around for decades. 

The first two H2s continue this largely non-informative, corporate jargon writing:

Issues: 

• No one Googling “top test management tools” needs to be told what they are.
• Even more inane is asking “Why use them?” The searcher is already looking for them; they know why they’ll use them. 
• Once again, nothing magical happened in 2025. They’re just blatantly forcing the date into it because someone must have told them “it helps SEO”.
• So much corporate jargon: “organizations are adopting…to meet growing demands.” No one talks like this. 

This “Key Features” section is similar to something we’ve included in our bottom-of-funnel pieces for a long time: help the reader understand what they should look for when evaluating options.

But the above list is not useful. The descriptions are too short and generic. You can contrast this with how we do it in our examples below. 

Overall, this is just bad content, and bad content hurting your SEO long term is a tale as old as time.

This kind of writing exists throughout the examples Lily shares in her post. We won’t critique all of them. You can go find those articles if you want to read more. 

Our Examples: How We Write These Bottom-of-Funnel List Posts

Here’s an example of one of our bottom-of-funnel list posts, in this case targeting the keyword “trucking management software” (full article if you want to read it).

Here’s the full intro and the start of our client’s section:

A few important things to note in how we position this piece in the intro: 

1. We never say this is a ranked list
2. We never say Toro’s software is “the best” 
3. We never say we’ve “tested” all the others
4. We never allude to any sense that this is an objective third-party evaluation

Instead, we just describe what we know about the space (trucking management software, TMS) and steer the discussion to the needs of Toro’s target customer: bulk haulers, a subset of truckers. 

You can see this directness in the first H2. It doesn’t say “Toro is the best!” It says it’s software for bulk haulers, which is both true and notable. Most trucking management software is generic, so our discussion of what bulk haulers need is genuinely valuable to the reader. Their needs are different, and that discussion doesn’t exist online.

If you are a bulk hauler looking for a TMS, this is genuinely helpful content, even though it’s clearly selling Toro. 

See how the “self-promotion” isn’t, by itself, a negative? It’s if the company lies or pretends to be objective, or pretends they “tested” all the options, that it comes across as cringey (at best) or dishonest (at worst).  

In fact, for these bottom-of-funnel queries (like “trucking management software”), it’s our strong belief that the searcher is looking to be sold to. When you’re researching products, in particular complex or expensive options like many B2B purchases are, you want to understand the features, benefits, and differentiators of each option at a deep level. So if one of the brands has an article that basically lays out:

1. Here’s our view on what matters in this space
2. Here’s how we designed our product to align with that view 

That’s genuinely useful. It doesn’t matter that the content lives on the brand’s site. The reader knows where you are. They know the brand is selling their product, and that’s fine; they’re expecting a sales pitch and will use that pitch to help their decision-making. 

You can see this in action later in the piece when we introduce Toro:

We list what features you need as a bulk hauler, with real explanations, not just AI-sounding generic descriptions (see the second bullet as an example), and say how Toro meets those criteria. 

Then, throughout that section, we go into extreme detail about exact features and how they help bulk haulers:

This level of detail helps AI search in particular because AI recommendations rely on matching detailed pain points with solutions.

This is genuinely useful content for the target audience without any of the unnecessary pretending that you won some contest and ended up first.

We think content like this is aligned with Google’s long-term goals (provide truly valuable content to users for each keyword), and we’ve written content for these bottom-of-funnel category keywords in this way for 8+ years. All of them have survived countless Google updates.

And even though this piece isn’t a listicle, it wouldn’t feel complete without a little self-promotion. So if you want to talk about publishing honest, valuable content targeting high-converting keywords for your brand, you can reach out here. 

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.