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.

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

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

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

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

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

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

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

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

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

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

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

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

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

A Note on Methodology

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

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

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

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

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

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

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

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

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

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

So we also grouped them by business type:

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

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

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

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

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

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

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

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

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

ChatGPT May be Running Additional Queries Behind the Scenes

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

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

Response Caching

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

Training Data

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

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

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

Here are the two titles ChatGPT showed: 

The best video conferencing software in 2026 | Zapier

The best video conferencing software in 2025 | Zapier

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

The best video conferencing software for teams in 2026

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

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

Hallucinations

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

So where does all of this leave us? 

GEO ≠ SEO…

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

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

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

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

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

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

… But SEO is Necessary for GEO

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

Be Available for Training Data

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

Increase Your Topical Authority

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

That’s an actual content strategy.

How this Looks for a Real Client, Toro TMS

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

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

But, this can mean sacrificing traffic in order to get more leads, which is where a lot of marketers get hung up.

You’ve probably heard advice like “create quality content,” “optimize your website for search,” or “target the same keywords as your competitors.” But in our experience, most of that advice is either obvious (of course you should create high quality content) or ineffective (increasing traffic only helps with leads if those people are ready to buy).

We’ve ranked clients for thousands of keywords over the past 8+ years, and our data shows that the #1 factor in whether SEO generates leads is the type of keywords you target.

In this article, we get into the details of how to find and rank for keywords with high buying intent and common mistakes people make in the process. We also briefly cover how to effectively track leads and ROI, how long it takes to get results from SEO, how to speed up results, and how to drive leads while you’re waiting for those results.

Why High-Buying Intent Keywords Convert 20x Better (+ How to Find and Rank for Them)

Like we said above, the biggest mistake we see in B2B SEO is traffic chasing. Yes, a lot of SEOs give lip service to “intent”, for example labeling SEO keywords as “commercial” versus “informational” like Ahrefs does. Almost every single time we look at the keywords a client has already gone after before working with us, it’s a bunch of low-buying-intent top of funnel keywords, while leaving tons of really high intent bottom of funnel keywords on the table.

The argument for top of funnel keywords is that you need traffic above all else. That traffic is alluring. There’s this underlying assumption that if you generate enough traffic, surely it’ll lead to conversions and ultimately customers.

Right? Wrong.

The difference in conversion rates between bottom and top of funnel keywords is massive. Not 10% or 20%, but as much as 20x, meaning high buying intent keywords convert 20x better than top-of-funnel traffic-generating keywords.

Top-of-funnel searches are simply done by people who likely aren’t ready to buy your product. For example, a top of funnel keyword for an IT company might be ‘cybersecurity’, which has a search volume of 124k.

When you look at the top results on Google, they’re mostly informational posts on what cybersecurity is, which tells us this reader doesn’t know what cybersecurity means and wants to learn about it.

Sure, these readers might eventually be head of IT and in charge of hiring an IT security service, and they might remember that your company once wrote a nice article on the topic and look you up.

Meanwhile, there are people actively searching for solutions like yours. Continuing with the cybersecurity example, someone who is ready to pay for an IT security service or software already knows what cybersecurity is and just needs help implementing it. Those are the people you should be targeting.

This is especially important for B2B companies. In B2C, there are scenarios where top of funnel content leads to a purchase because purchases are often made more impulsively for stylistic reasons. For example, a B2C apparel shopper might search “summer fashion trends 2025,” learn about something new, and then within a day or two try to buy an item they like. In this case top of funnel content can directly lead to a purchase.

But B2B purchases take more consideration. They aren’t as flippant. For example, if you’re selling IT security software, your customer isn’t buying on a whim, and they’re certainly not buying because they saw a mention of your brand in a general purpose article on “IT security tips in 2025”. They’re buying only when and if they’re in the market for new security software.

Note this is true even when the B2B purchase is relatively “cheap”. Take project management software, for example. Tools like Trello or Asana aren’t expensive (around $10 per user per month) and can be purchased self-serve with a credit card.

But they’re operationally expensive because they require an entire team or company to change their habits (switch to a new project management tool). No organization makes that decision lightly. That’s why there’s a massive difference in conversion rates between traffic reading a general article on project management tips and traffic searching for “best project management software for startups.”

Lastly, in our experience, you can’t nurture someone from no buying intent to buying intent. No amount of drip emails to someone who isn’t in the market for new PM software or new IT security software is going to manufacture that buying intent. They’ll want it when they need it. In the meantime you could have spent that same time and energy creating content that ranked for keywords that do have buying intent.

Three Types of High Buying Intent Keywords for B2B Companies to Target

Based on our analysis of over 90 blog posts we wrote for B2B clients, there are three types of high buying intent keywords that consistently drive the most conversions.

And, all three of these keyword types significantly outperform top-of-funnel content keywords, which typically convert at just 0.2% in our data.

1. Category Keywords convert at 3.25% on average

These are search terms that directly correspond to the product or service category you’re in. Examples include “content marketing agency,” “CRM software,” or “accounting software for small business.”

These are usually the easiest ones to think of, but there are also typically more than you think. Read our article to learn more about how to find these.

In our data, category keywords convert at 3.2% on average.

2. Comparison and Alternatives Keywords convert at 6.94% on average

These terms indicate the searcher is actively comparing products or looking for alternatives to existing solutions. Examples include “Salesforce vs. HubSpot,” “Mailchimp alternatives,” or “best alternatives to Shopify.”

These keywords typically convert quite well on average at 8.43%, because searchers are actively evaluating solutions and are closest to making a purchase decision.

3. Jobs-to-be-Done (JTBD) Keywords convert up to 12.5%

These are queries that indicate the searcher has a specific problem your product solves. Examples include “how to manage sales leads,” “how to track content marketing ROI,” or “how to migrate from Salesforce to HubSpot.”

JTBD keywords convert at 2.44% on average in our data because they capture people who have identified a specific pain point, but may not yet know what category of solution they need. In other words, they’re just looking to solve a problem. They aren’t looking specifically to buy your product/service, which is why we see a lower average conversion rate.

However, they do have the possibility to convert at a much higher rate — up to 12.5% in our experience. This is because some JTBD keywords have extremely high buying intent while others have less.

For example, for our digital marketing client, JTBD keywords around collecting or gathering video testimonials (exactly what their software does) converted to free trials at 5–9%. But keywords around what to say in a testimonial video, i.e., no intent for buying software, converted at only 0.6%.

How to Find High-Converting Keywords That Most Teams Overlook

It’s usually pretty easy to find a handful of the most obvious category keywords (like “content marketing agency” if you’re a content marketing agency), but these often run out quickly. So how do you find more?

We suggest talking to your sales team and any other customer-facing teams. Many B2B marketing teams will review a few sales calls, read customer feedback, and look at sales battlecards, but they don’t systematically involve the sales team in their SEO strategy.

This is a missed opportunity. Your sales team knows exactly which pain points resonate most with prospects, which objections come up repeatedly, and which benefits actually close deals. This is where you can find JTBD keywords that actually have buying intent, additional category keywords, and competitor keywords you might have missed.

For example, a marketing automation company might target the obvious category keyword “marketing automation software.” But, after talking to sales, they might discover prospects frequently ask about “how to set up drip email campaigns” or “how to track email marketing ROI” — both high-intent JTBD keywords that indicate someone needs marketing automation.

Ask your sales team specific questions like:

• What pain points do prospects mention most often in sales calls?

• Which product benefits close the most deals?

• What objections do you hear repeatedly, and how do you overcome them?

• What language and terminology do prospects use when describing their problems?

• Which types of leads convert best and why?

• What are prospects typically using or doing when they come to us?

• What competitors do they mention most often?

This research should directly inform your keyword strategy and content creation.

Plus, it can also tell you whether your content is working and help you drive more qualified leads. Check in with sales regularly to learn what type of leads your content is bringing in, get specific about why certain leads are or are not closing, and use that information to fine tune your marketing strategy.

How to Rank for High Buying Intent Keywords

Most advice on writing SEO content talks about creating quality content and thoroughly meeting search intent — understanding what the searcher wants and giving them exactly that. This is great advice and we agree completely.

However, when it comes to B2B blog content, most marketers go wrong with this approach in two ways: they don’t write at the expertise level of their audience, and they don’t sell their product or service.

How to Write for B2B Readers (Beyond Just “Create Quality Content”)

With B2B, your reader has likely been in the industry for many years and knows the ins and outs of the job. In short, they’re experts on the topic. And yet, most content reads like a “Google research paper” — surface-level content that regurgitates what’s already available online without adding any new insights or expertise.

This usually happens because the writers aren’t experts themselves (whether internal marketing team members or freelancers), so they end up just researching what’s already published or reading through a few internal documents.

This approach fails for two reasons:

1. Google can only provide what everyone else has already said on the topic, not your company’s unique perspective or any details about your product/service.

2. Internal documents are often outdated or not detailed enough, so the content ends up reading like the writer knows less than the reader — because it’s true.

To solve this, we conduct detailed interviews with subject matter experts for each blog post (or at least each blog post where we’re covering new information). More importantly, these SMEs are from within our clients’ own companies, so we get the unique perspective they have to offer and can easily tie their product or service directly to the pain points hidden in the keyword we’re targeting.

In these interviews, we ask questions like:

• What do you think a potential customer is looking for when they search {keyword}?

• Why are they looking for that (i.e., what problem are they trying to solve)?

• Do you think there’s anyone who would be searching this term that wouldn’t be a good fit for your company? Why?

• What specific features or capabilities does our product have that solves this problem?

• What mistakes do you see prospects making when they try to solve this problem on their own?

• What questions do customers ask most often about this topic?

Further reading: How to Improve Your Content Writing Skills by Using an Interview Process (+ 6 Other Content Writing Tips)

Why You Need to Sell Your Product

If you follow our suggestion and target high buying intent keywords, you need to sell your product or service in order to satisfy intent.

Many marketers and business owners shy away from this because they don’t want to be “too salesy” with their content marketing. They often have this belief that with content marketing you should ‘give and not ask’.

But, the searcher is literally looking for something to solve their problem. So, if you do solve that problem, the best way you can help them is by showing them exactly how you solve it.

Additionally, we believe you should spend the majority of the blog post going into detail about how you can help them. Again, B2B buyers want this level of detail. And, it makes sense that you only know that level of detail for your own product or service, so the other sections about the competitors have to be shorter.

Plus, they can see whose website they’re on, so it can seem disingenuous to try to sound completely unbiased. Include specific details related to the keyword, screenshots of your product in action, and use roughly half of the blog post real estate to explain your own solution.

How to Effectively Measure Leads and ROI From SEO

All of the advice so far can help you take SEO content from something that just generates traffic into a lead generation tool, but you will still need a way to track leads and ROI.

Organic traffic and search rankings can tell you if what you’re doing has potential, but it’s not the same as actually tracking conversions. In order to know whether your content is actually generating a positive ROI, you need to be able to directly tie individual leads to specific pages.

In this article, we go over how to set up conversion tracking in GA4 to track form submissions, demo requests, and other lead actions. But, there are other tools besides Google Analytics like WhatConverts that can help you track additional types of leads, like phone calls.

If you can track which leads actually become paying customers, even better. Every company will have a different process for how they do this.

Next, figure out the ROI or breakeven point. How much are you spending on content right now? How much is the average value of a lead in dollars? Use that to figure out how many leads per month you need to break even and start plotting those numbers on a graph.

It Takes 3–6 Months to Start Seeing Results from SEO

Our study on how long it takes to rank on the first page of Google shows that it takes an average of 3-6 months to start seeing meaningful rankings for new content. But that’s just rankings.

We typically see a few leads start to come in with page one rankings, but steady leads typically don’t come in until you’re ranking in the top three spots for your target keyword.

Figuring out your timeline for getting into the top three positions is harder to predict because it depends on the specific keyword, your industry, and your domain authority. We sometimes see slower results in industries with longer sales cycles (think switching over your entire CRM, which would require buy-in from multiple levels of management, all departments, significant financial investment, and significant disruption for all employees using it) or for highly competitive keywords.

However, on average our data shows that nearly half of the blog posts we create for clients rank in the top three positions within the first year.

How to Speed Up Results and Drive Leads in the Short Term

While the number one factor for getting results from SEO is time, you can speed up results with strategic link building. This means getting high-quality, relevant websites to link to your content through guest posting, editorial outreach, and relationship building. Quality backlinks signal to Google that your content is authoritative and can help you rank faster for competitive keywords.

While you’re waiting to get those results organically, you can also drive leads in the short term with:

• Paid ads: Run targeted ads on Google, LinkedIn, or other platforms to drive immediate traffic to your high-intent content. This approach allows you to start generating leads from your content investment while you wait for organic rankings to develop. Just like with organic search, we recommend taking an intentional, buying intent focused approach with PPC. You can learn more about our approach to PPC here.

• Disruption stories: If your company has a unique perspective that challenges conventional wisdom in your industry, write about it and share it across social media channels and industry forums. These contrarian takes often generate immediate attention and can drive qualified traffic to your site. Plus, they can attract buyers who may not be actively searching for your product/service because they don’t know it exists. You can learn more about disruption stories here.

  
  

How Does the Shift to ChatGPT and LLMs Affect Content and SEO Strategy?

Finally, the shift in everyone’s search habits from Google to AI tools like ChatGPT is on every marketer’s minds — how to show up in AI searches, how to get mentioned by Google’s AI overviews, how to use AI for writing and optimizing content— but the truth is, no one actually knows very much about how it works or how to hack the system.

We share our thoughts on AI in these articles:

• Pain Point LLMO: Why Product-Centric Content on Your Own Site Beats Reddit & PR

• How SEO Will Change in a World of AI Search

• Does Google SEO Affect LLM Optimization? We Analyzed 400+ Keywords to Find Out

• How Marketers Are Using AI for Writing [Survey]

To summarize these articles, we believe a lot of the advice out there right now is largely unhelpful (‘get mentioned on Reddit’ — nice, but how?) or unnecessary (‘add .txt files’). The best thing you can do is have a strong SEO presence and produce the type of content we recommend above.

When it comes to using AI to create content, we recommend caution because many of the writing tools out there are nowhere near good enough to produce the kind of content you need to get quality leads or AI mentions. (That’s why we’ve created our own tool, which you can read about here.)

How to Work With Us or Learn More

• Work with our agency: If you want to hire us to create and execute a search engine optimization strategy by identifying your best keywords, creating content that is laser-focused on ranking and driving conversions, and link building to improve your ranking positions, you can learn more about working with us here.

• Join our team: If you’re a content marketer or writer who wants to do content marketing in this way, we’d love to have you apply to join our team.

• Learn our methods in our content marketing course: Individuals looking to learn our agency’s 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 content formats. We include several details and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we, along with other members, give feedback. Learn more here.

  
  

Some have even coined this emerging field LLMO, which stands for LLM Optimization.

But while Google SEO has decades of tested strategies, LLMO — or AI SEO — is still in its early stages of development. Most of us have a general sense that if your name appears in many places online, AI tools are more likely to mention you.

But where exactly do you need to show up, how often, and in what context? For example, how closely does your brand name need to be positioned next to the target keyword you want to rank for?

To shed light on this, we analyzed whether our Google-focused SEO strategy has helped our clients get mentioned by AI search engines like ChatGPT and Perplexity.

Specifically, we wanted to know:

• Does ranking on the first page of Google for a specific keyword increase the chances of being mentioned when that keyword is entered into an AI chatbot (e.g., “product development software”)?

• Do domain rating and the number of referring domains affect the likelihood of getting mentioned by AI tools?

To answer these questions, we analyzed 400+ bottom-of-funnel keywords across 16 clients of varying sizes, industries (SaaS, services, etc.), and authority levels, all ranking on the first page of Google.

We focused on results from Chat GPT and Perplexity, the most popular AI search tools today, while also briefly covering Claude AI and Google’s AI Overview.

First, we’ll break down the results and share what we learned about how AI tools work, followed by how we set up and executed the study.

We show up 77% of the time in ChatGPT & Perplexity when we rank on the first page of Google

For this study, we took 400+ bottom-of-funnel keywords where our clients ranked on the first page of Google and entered those into ChatGPT and Perplexity.

We then recorded when our clients were mentioned in the AI’s response and when they weren’t. Here’s an example using ourselves (not one of the companies we studied):

Since we focused solely on high-buying-intent keywords — queries indicating the reader was ready to purchase — we weren’t interested in instances where our client was mentioned as a source but not by name in the response.

On average, we found a 72% correlation between our clients ranking on the first page of Google and being mentioned by AI tools. On Perplexity, this correlation is 77%, and on ChatGPT, it’s 67%.

Note: We consider the “the first page of Google” to be the top ten organic spots.

Next, we examined whether ranking in the top three spots on Google for a given term (rather than just the top ten) further increased the likelihood of being mentioned by AI search tools.

The answer is yes. The correlation rises to 72% for ChatGPT and up to 82% for Perplexity.

This means that when our clients rank in the top three positions for a keyword, they are mentioned by AI search engines 77% of the time on average when the user enters the exact same keyword.

It’s particularly interesting that not only is there a strong correlation between ranking in the top ten positions on Google and showing up in AI-powered search engines, but ranking even higher (i.e., in the top three positions on Google) further strengthens that correlation.

What’s causing this correlation? (And what we know about how AI tools work)

All of this begs the question: what’s going on behind the scenes that’s causing this correlation?

On the Google side, we know from decades of SEO research that Google determines the importance and authority of websites through backlinks.

But the key questions are:

• How many sites are linking to you?

• Who are they?

• And who is linking to them?

The more links backlinks you have, especially from authoritative sites, the more authority your site gains. While there are many other ranking factors, current SEO research continues to support the thesis that backlinks are still one of the most important ranking factors.

Another crucial factor is topics: What topics does your site cover? And what topics do the sites linking to you cover? Google evaluates this to ensure relevance when displaying results.

We’re grossly simplifying here, but these two factors (backlinks and topic relevance) help determine which pages Google ranks when a user searches for something.

But what about AI search engines? How do they decide what to recommend or say?

We know that ChatGPT’s web search feature relies on third-party search engines, including Bing:

“To provide relevant responses to your questions, ChatGPT searches based on your prompts and may share disassociated search queries with third-party search providers such as Bing.”

(Source: Open AI help doc)

And, Perplexity’s CEO, Aravind Srinivas, admitted in June of 2024:

“We don’t just rely on our own web crawlers, we rely on third-party web crawlers as well.” (Source)

We also asked Perplexity what it uses to crawl the web in multiple different ways and each time it was some version of what you see in the screenshot below:

We know both ChatGPT and Perplexity rely on third-party vendors to access current information. While this offers some insight — many marketers assume a strong correlation between search engines like Google and Bing, so ranking on one should help with the other — it’s not the whole picture.

Unlike traditional Google, which only indexes third-party sites, AI tools like ChatGPT also incorporate their training data to craft natural language responses.

This training data comes from a much broader range of sources.

[source]

So, how much do these tools rely on their own web crawlers, third-party search engines, or their training data?

We don’t have the full answer, and understandably, OpenAI, Perplexity, Anthropic, and others are somewhat cryptic about their inner workings.

However, the data shows that if our clients rank on Google, they appear in AI search tools for bottom-of-funnel queries up to 77% of the time.

This correlation suggests that, despite the unknowns behind AI’s processes, ranking on Google still plays a significant role in LLMs mentioning you. This is encouraging for brands aiming to boost traffic and leads through traditional SEO and content marketing.

Individual clients saw up to 93% correlation between Google rankings & AI results

Interestingly, when analyzing results at an individual company level, we observed a wide range of correlations, from 0% to as high as 93%.

This raises a key question: Why are there variations? What causes these differences between companies? And can we use this information to determine how a company can optimize its SEO efforts to also get noticed by AI tools like ChatGPT?

We think the answer is yes.

Specifically, we noticed some interesting patterns when analyzing:

1. A client with a brand-new site and brand ranking only on Perplexity.

2. A correlation between domain rating and LLM mentions.

A brand-new site seen only by Perplexity and not Google

For example, Client 6 wasn’t mentioned at all by ChatGPT but had a 60% correlation on Perplexity. Why? Client 6 is a young company with a brand-new website and a domain rating of less than 1 when we started working with them in early 2024. It’s likely they weren’t included in ChatGPT’s training data at all.

However, Perplexity operates more like a search engine than an all-purpose AI tool like ChatGPT (you wouldn’t use Perplexity to write a blog post with AI, for instance). It’s possible that perplexity relies less on training data and is more sensitive to what’s ranking in search engines like Google.

When we conducted this study, Client 6 had 11 first-page Google rankings, and we saw their brand name mentioned by Perplexity for 60% of those queries. This suggests that our SEO work and their Google ranking directly influenced their chances of being mentioned by Perplexity.

Domain Rating (DR) and how it affects AI search results

Backlinks play a pivotal role in Google rankings, which is why various SEO tools have developed metrics to measure the quantity and quality of those backlinks. These metrics are referred to as domain authority (Moz), authority score (Semrush) and domain rating (Ahrefs).

In simple terms, the more reputable websites that link to your site, the stronger your website’s authority.

Since LLMs like ChatGPT generate their responses based on common word patterns in conversations, it’s reasonable to assume that they will prioritize the most popular — and thus most linked-to — brands or websites in product discussions or recommendations.

So, we wondered: Does a higher DR correlate with getting mentioned by AI tools?

We used Ahrefs’ domain rating (DR) for this and found that, yes, there is a correlation!

The data above, though sparse, clearly shows a preference for high DR sites being mentioned by ChatGPT and Perplexity. This is significant because higher DR correlates with better SEO rankings, and for years, marketing teams and SEO agencies have focused on improving domain rating through strategies like link building.

If increasing DR helps brands get mentioned by LLMs, we’re in familiar territory.

That said, it’s important to note that this doesn’t mean that AI tools are literally counting backlinks or measuring their strength. More likely, AI tools prioritize websites or brands with strong authority, which might be reflected in their training data or because they base web searches on Google and Bing.

Regardless, this data suggests that continuing to improve DR by creating high-quality content that other websites want to link to is a solid strategy for “ranking” well in LLMs.

Having hundreds of backlinks isn’t the same as having strong website authority

Before moving on, we wanted to dig deeper into the correlation between AI results and backlinks or domain authority.

In addition to analyzing the relationship between DR and AI search results, we also explored how the number of referring domains (i.e., how many unique websites link to yours) correlates with AI mentions.

The slight correlation still exists, but it’s much weaker than the one observed with DR.

This suggests that the raw “number of backlinks” (or referring domains) isn’t the main factor influencing which sites LLMs mention. While the number of referring domains contributes to Ahref’s DR metric, it also factors in the DR of those domains.

It seems LLMs may prioritize site or brand authority similarly, which should reassure brands that have heavily invested in traditional content and SEO strategies.

Setting up the study

For this study, we analyzed 400+ bottom-of-funnel keywords from 16 clients who ranked in Google’s top 10 positions for each search term. The keywords included all three categories of high-buying-intent keywords we typically target: category keywords, jobs-to-be-done, and competitor keywords (i.e., competitor vs competitor, competitor alternatives).

We evaluated results from four tools: ChatGPT, Perplexity, Claude AI, and Google’s own AI Overview (more on the last two in the next sections).

Each keyword was entered into the AI tool the same way it would be entered into Google.

Note: We understand that most users chat with LLMs in a more organic way than just entering keywords (e.g., “I need help finding a project management software” vs. “best project management software”). However, we found that that created too many variables. So, for the sake of a controlled study, we simply copied the keyword as-is.

Then, we took note of:

• When our client was mentioned. This was marked down as a “Yes”

• When other software or services were mentioned, but not our client. This was marked down as a “No”.

• When the tool didn’t mention any solutions. For example, sometimes the AI only offered information on the topic (e.g., a definition of project management software) or offered to create the software for you. This was marked down as “N/A”.

We then calculated the percentage of “Yeses” by dividing the total number of “Yeses” by the combined total of “Yeses” and “Nos.”

As mentioned, all the keywords in this study are bottom-of-funnel queries, meaning the goal is to ultimately sell your product. So, we purposely excluded “N/A” values because no amount of online presence, let alone ranking on the first page of Google, will help if the AI tool doesn’t think the user is looking for a solution. (More on this in a later post.)

As we cover in detail above, we also analyzed results separately for keywords in any position on the first page versus those where we ranked in the top three positions. Additionally, we examined results for individual clients and each tool.

A note on Google’s AI Overview

Marketers and brands have been focused on showing up on AI tools like ChatGPT and Perplexity, but they’re also eager to know how to show up on Google’s AI Overview.

However, this AI Overview isn’t offered for every keyword or for every user. In fact, our study showed that 40% of the time, there was no AI Overview for bottom-of-funnel keywords. Plus, it’s also worth noting that users can choose to turn this feature off.

This means that for most high-buying-intent queries, the reader has to rely on non-AI results (e.g., ads, organic, and sources across the web).

Finally, just because you’re ranking on the first page or even in the top three positions on Google’s SERP doesn’t guarantee you’ll be mentioned by Google’s AI.

Our data shows that if you’re ranking on the first page, there’s only a 31% chance you’ll be mentioned in Google’s AI Overview. And that chance only rises to 43% if you’re in the top three positions.

What’s encouraging is that, in our experience, you can still get plenty of traffic (and potentially leads), if you produce content that ranks well — even when an AI Overview appears above your content.

And, as mentioned earlier, Google’s AI Overview only appears roughly half of the time. So, writing high-quality content that fulfills search intent remains the best way to reach your target audience on Google.

A note on Claude AI

Claude AI is better known as a writing tool than a search engine replacement, and it even admits that it doesn’t search the web — instead, it works off of older training material.

However, we included it in the study for the sake of interest and to confirm that not all tools should be used for search.

And that’s evident from our data. Out of all the tools we tested, Claude had the weakest correlation to Google results by a long shot.

It also had the most “N/A” values of all the tools, as it preferred to create the software rather than list options.

Note: We entered the keyword into Claude just like we would into Google for consistency’s sake, but if we had followed up with Claude and specified that we wanted it a list, it likely would’ve provided one.

So, we wouldn’t spend much effort trying to crack AI tools that weren’t designed for search, as they simply aren’t effective for that purpose.

Instead, we recommend focusing on showing up for tools like ChatGPT and Perplexity. And, as this study showed, continuing with a strong SEO strategy can get you 77% of the way there.

How to work with us or learn more

• Work with our agency: If you’d like to discuss how we can work together to create a content strategy that generates leads and possibly increase your chances of getting mentioned by LLMs, feel free to reach out here.

• Join our team: If you’re a content marketer or writer interested in this approach to content marketing, we’d love for you to apply to join our team.

• Learn our methods in our content marketing course: Individuals looking to learn our agency’s 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 content formats. We include several details and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we, along with other members, give feedback. Learn more here.

Category keywords make up one of the three main buckets that we think of for high buying-intent, or “bottom of the funnel”, keywords. The other two being jobs-to-be-done and comparison and alternatives keywords. 

The defining characteristic of category keywords are their extremely high buying intent — users are actively looking to buy your type of product or service. They don’t need to be educated on the problem or led through a buyer’s journey; they’re ready to buy now. 

Due to their high buying-intent, category keywords tend to convert at much higher rates than most SEO keywords. In our study of 90+ rankings across dozens of clients’ accounts, category keywords had an average conversion rate of 4.85%, whereas most blog posts that target more informational (“top-of-funnel”) keywords typically convert at 0.5% or less (many don’t convert traffic to product leads at all). This is nearly 10x the difference. 

However, from talking to and seeing the analytics of over a hundred companies over the years, we’ve noticed that the vast majority of companies only target one or two category keywords with their homepage or product pages.

This gross underutilization of category keywords is problematic for several reasons:

• Companies typically only focus on one or two category keywords and think that’s it, when in reality there are usually dozens of high converting terms to target.
  
  
• Companies often rely exclusively on their homepage or product/service pages to target these keywords. However, search engine results pages (SERPs) typically favor comprehensive blog posts. As a result, these pages rarely achieve high rankings.
  
  
• Companies don’t thoughtfully prioritize the order in which they target these keywords, which often leads to slower results.

All of this leads to brands leaving tons of conversions on the table in their SEO and content strategy. 

In this post, we’ll cover how we solve these issues and have approached targeting hundreds of category keywords for dozens of clients. Specifically, we’ll cover:

• Types of category keywords and their average conversion rates
  
  
• Effective strategies for discovering a wide range of category keywords
  
  
• Best practices for targeting these keywords
  
  
• A framework for prioritizing keywords to optimize your SEO efforts

Types of Category Keywords

At Grow and Convert, we think of category keywords as falling into one of three main types: 

1. Main category keywords
2. Side category keywords
3. Category keywords with a layer of specificity 

Understanding these distinctions will help you identify  which are the most valuable for your company in terms of conversion potential, so you can focus your efforts.

Main Category Keywords

Main category keywords are the most straightforward. These keywords directly describe the core product or service your business offers. For instance, if you sell accounting software, your main category keywords would be  “accounting software” and variants like “best accounting software”. 

Keep in mind that while synonyms may appear interchangeable to us, Google displays different search results for each variant.

Note that there are many synonyms that seem to mean the same thing to a human, that Google ranks different results for. Therefore, you’ll need to target each separately to rank for both.

For example, people might also search for accounting software using terms like“bookkeeping software” or “financial management software.” 

If Google is ranking the same web pages for both your main category keyword and one of its synonyms, it means you don’t need to create a unique page for each keyword. Google is viewing them as having the same intent. 

However, if the SERPs for each keyword show different web pages ranking, it suggests the intent behind the keywords differs, meaning Google won’t rank the same pages for both. In this case, you’ll need to create separate pages to cater to each specific intent and rank for each.

Finally, note that a single company, brand, or even product may have multiple category keywords to target, not due to synonyms, but because the product or service has distinct features or use cases that correspond to different categories. 

For example, we’ve had web analytics software clients whose products serve as “heat mapping tools”, “website visitor tracking software”, “visitor recording software” and “funnel analysis software” — all valid category keywords. 

Note that these are different from side category keywords (discussed below) because these companies have multiple main offerings, whereas side category keywords are not part of the main offering. 

Our recommendation: Take the time to list every possible category keyword related to all features and use cases of your product(s). Cast a wide net and use keyword research tools to verify search volume and prioritize the order in which you target these keywords (more on that below). 

Average Conversion Rate for Main Category Keywords: 4.85%

In an analysis of 95 different articles across various clients (mentioned above), we found that main category keywords convert at a rate of 4.85% on average, which is significantly higher than the average blog post.

We find that typical blog posts focusing on high-volume, top-of-funnel keywords like “what is accounting” and “project management trends” tend to convert at a rate of 1% or less. We’ve even observed instances where clients’ blog posts, before implementing a conversion-focused strategy, converted at extremely low rates, like 0.03% to 0.19%, with some generating no conversions at all over extended periods.

Category Keywords with a Layer of Specificity

Category keywords with a layer of specificity target a particular niche within your broader category. Using our accounting software example, a layer of specificity could be “accounting software for freelancers” or “cloud-based accounting software.”

In our experience, these layers of specificity attract a more qualified audience because they target searchers who are looking for a solution tailored to their specific pain points. As a result, they provide an amazing conversion opportunity if your product has features that cater to those pain points. 

For example, say someone is searching for “accounting software for small businesses.” Searching for “accounting software” by itself shows high buying intent. But the addition of “small business” indicates a person motivated to solve specific pain points. So, if your accounting software has features that cater to small businesses, and you can create content that addresses those pain points — and show how your product solves them — you’re in a great position to convert the searcher into a customer.  

Because of this, in our past client data, we’ve seen that category keywords with a layer of specificity have the potential to convert at a much higher rate than broader category keywords. For instance, we wrote a post for a client targeting the keyword “marketing asset management software” which had a conversion rate of 15.2%. This was significantly higher than the average conversion rate of 4.85% for posts targeting general category keywords. 

Finally, we’ve noticed that these longer-tail category keywords often have less competition since most businesses focus on higher-volume keywords. So they can be a good option for getting fast results.  

Average Conversion Rate: 2.96%

Category keywords with a layer of specificity convert at a rate of 2.96% on average, but can convert as high as 15+%. 

Side Category Keywords

Side category keywords represent features or use cases that are closely related to your main offering, but not the primary focus. For example, that same accounting software might have a feature for invoicing so side category keywords would include “invoicing software” and “online invoicing tools”.

A common objection to targeting side keywords is that companies feel it narrowly defines their business and limits their perceived offerings (“We aren’t just invoicing software!”). They worry that focusing on a specific feature, like “email sequencing,” will make customers think that’s all they offer (“But we’re an entire marketing automation platform!”).

In our experience, this issue is easily addressed within the content. Specifically, the content should, of course, speak to the intent of the software (e.g. covering invoicing software needs and how you provide those features) while also showing how the targeted feature fits within the  larger product or service ecosystem, and explaining  the benefits of your entire product suite (e.g., why it’s convenient to have invoicing and accounting in the same software).

This way, you can use people’s demand for specific features or point solutions to expose them to your offering, even if it’s more comprehensive than what they searched for. Not everyone will need your larger solution, of course, but as we share below, these still convert decently well.  

Another objection we sometimes hear is that companies are hesitant to target certain side category keywords because that area isn’t their strong suit and they question whether or not it will actually convert customers on its own. For example, a company with an email marketing platform might have simple CRM capabilities, but they know they can’t replace a dedicated CRM platform like HubSpot.

This is a fair objection and one that warrants consideration. If you truly feel you aren’t strong in a specific feature category, then it’s fair to decide not to target it, even if there are keywords with good search volume. If your product isn’t strong in those categories, the chances of those customers being good long-term paying customers are low. Ultimately, it comes down to opportunity cost and prioritization. If you have other more valuable keywords to target, go after those first. (More on this later.) 

Average Conversion Rate: 1.94%

In the same study mentioned above, side category keywords convert at a rate of 1.94% on average. Although this is the lowest converting type of category keyword on average, it’s still about 2x the conversion rate of top-of-funnel blog posts. 

How to Find Category Keywords

The key to finding lots of different category keywords is understanding how your customers talk about the products or services you provide (main category keywords) and which aspects of your solutions get them most engaged (side category keywords, layers of specificity). You need firsthand data on how they perceive and talk about your products, how they describe their problems, and what language they use when searching for solutions.

The best approach is to have direct conversations with your customer-facing teams. We’ve talked about this extensively before, including: 

• What questions we ask to understand customers’ pain points
  
  
• How to conduct interviews even if you don’t have customers
  
  
• An example of how we surveyed G&C readers and what we learned

This step gives you a list of pain points. Then, you correlate those with  SEO keywords from keyword research tools (like Ahrefs, Semrush, or any others). These tools provide data on search volume, keyword difficulty, and can suggest variations and synonyms that you may not have considered.

You can also look at Google’s autofill suggestions that appear when you start typing a query, the “people also ask” sections, and related searches. These features can reveal common search patterns and variations that you can incorporate into your strategy.

Finally, you can utilize online communities and forums like Reddit to see how people phrase their pain points and describe solutions like yours.

By combining customer insights with a variety of keyword research methods, you can develop a comprehensive and targeted list of category keywords that are much more likely to resonate with your ideal audience and drive conversions.

How to Prioritize Which Category Keywords to Target First

Most SEO folks prioritize keywords based on search volume alone — the higher the volume, the better. Instead, we recommend starting with the category keywords that are most closely aligned with your product’s strengths because these will likely have the highest conversion rates.  

Then, consider your current domain authority and resources. It might be more strategic to initially target less competitive keywords and gradually build up to more challenging ones. Tools like Ahrefs and Clearscope can help assess keyword difficulty and provide insights into search volume and ranking potential.

For example, let’s say you offer a CRM platform known for being easy to use and budget-friendly. Rather than immediately targeting “CRM software,” you might start with terms like “affordable CRM platform” which align with your key differentiators and are less competitive. 

On the other hand, if it’s a particularly valuable keyword, it’s totally reasonable to decide to target it early on because it will likely take longer to rank. We typically start with a mix of both. 

Finally, you’ll need to monitor the performance of your targeted keywords and track conversion rates. This allows you to adjust your strategy so you can focus on keywords that deliver the best results and iterate on those that underperform. 

Targeting Category Keywords with Blog Posts vs Product or Homepages

Most companies target category keywords through their homepage and product pages and save their blog for top-of-funnel content. However, in our experience, blog posts often outperform product pages for category keywords because they: 

• Satisfy the searcher’s intent more thoroughly, which leads to higher rankings for a larger variety of keywords
  
  
• Allow for a stronger sales pitch, which leads to higher conversion rates

In many cases, the top-ranking results for category keywords are list posts or articles that thoroughly explore how a specific product addresses a pain point. This is because when someone searches for a category keyword, they’re often in the research phase and are looking for a range of options and how to choose the best one for their needs.

Unlike product pages which focus only on your solution, detailed blog content can discuss the benefits and use cases of the product or service, compare options, teach readers how to evaluate those options, and naturally lead readers to your offering. This comprehensive approach better serves searchers looking to understand the full landscape before making a decision. It also allows you to craft a more in-depth sales pitch around the specific pain points of that reader. 

Plus, because you have to deeply satisfy intent in order to rank, your homepage can only target a limited number of keywords effectively. In contrast, you can create numerous blog posts that each focus on a specific variation of the category keyword. 

For example, you could have one blog post targeting “accounting software for small businesses,” another targeting “accounting software for freelancers,” and yet another targeting “accounting software with time tracking.” 

This strategy allows you to cast a wider net and capture a larger share of organic traffic, while still maintaining a laser focus on satisfying the intent of each specific keyword. 

All of this helps you rank higher for more keywords and convert more of your readers to paying customers. 

Learn More About Our Agency

• SEO and Content Marketing Service: If you want to hire us to execute a content-focused SEO strategy built around driving lead generation and sales, not just traffic, you can learn more about working with us here.
  
  
• Paid Search Service: We started PPC services at Grow and Convert after seeing thousands of dollars of wasted spend in our client’s Google Ads accounts (full story). Specifically, we noticed that many paid search agencies focus their strategy around maximizing clicks and traffic and rely heavily on Google’s automation tools. In contrast, we applied our well honed bottom of funnel strategy from SEO to paid search, where we take an active, manual approach to ensuring every campaign maximizes lead generation instead of click generation.
  
  
• Join Our Content Marketing Team: If you’re a content marketer or writer and would love to do content marketing in this way, we’d love to have you apply to join our team.
  
  
• Our Content Marketing Course: Individuals looking to learn our agency’s 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 content formats. We include several details and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we, along with other members, give feedback. Learn more here.
  
  
• Continue Learning: Find additional content on our blog and YouTube channel. 

• If you target too many keywords with a single page, it’s unlikely that you’ll rank high for any keywords because it’ll be hard to satisfy search intent for all of them.

• There are instances where you don’t need two different pages for similar keywords — one page can rank for both. So, creating two different ones is extra, unnecessary work.

The conclusion we’ve come to (based on 7+ years of helping clients rank on the first page for high converting keywords) is that you should only target one keyword per page and not worry about the exact number of times you’re using any given keyword. 

But, we’ve also found that this leads to a whole host of other questions such as…

• If two keywords are almost identical (e.g., accounting software vs accounting platform), how do I decide whether to create separate pages or target both with one page?

• Can I target the same keywords with multiple pages?

• Why type of content should I create to target various keywords (e.g., blog vs product page)?

• How many keywords should I track?

… and many more.

So in this guide, we’ll explain why we default to one keyword per page, share real client examples, and answer additional questions in a detailed FAQ section. 

Why We Only Target One Keyword per Page (+ How to Do It)

The main reason for only targeting one keyword per page is that Google is known to favor pages that deeply satisfy intent. In other words, Google is more likely to rank a blog post that is solely focused on the given keyword over a page that briefly covers lots of different topics.

For example, let’s look at the results for “types of pizza stone”:  

The first three results are reviews of different types of pizza stones, while the fourth includes a comparison of baking steel. Although we can’t know for sure why it ranks lower, one strong possibility is that it may struggle to reach the top 3 spots because it addresses both steel and ceramic alongside stones, whereas the top three results are solely focused on different pizza stones. 

While we can’t be certain, our experience with ranking clients for over 1,000 SEO keywords and updating old articles to improve rankings shows that these subtle differences in intent matter. An update to an article to fix a slight intent mismatch has historically improved rankings for us.  

But knowing the true intent behind a keyword can be tricky. 

For instance, the results above for ‘types of pizza stones’ are all detailed, first-hand reviews of specific pizza stones. But, it would also be reasonable to conclude that a blog post about types of pizza stones could simply be a summary of the different materials, shapes, and sizes that pizza stones come in. So, how do you determine which one searchers actually want? 

In our experience, the best way to determine the true intent behind a keyword is to conduct a detailed SERP analysis. 

Google’s algorithm has already done the work of determining intent by testing different types of content to see what readers click on and read after entering a specific keyword. So, you just need to look closely at what’s already ranking to figure what the reader is looking for. 

Then, you should shape an entire page (whether that’s a blog post, landing page, or something else) around addressing just that intent. In our experience, that maximizes your chance of ranking for that keyword. In contrast, if you try to rank for multiple keywords with that one page, unless their intents are all extremely similar, you risk diluting your page trying to fulfill multiple different search intents. 

Here are some more resources for how to write SEO content that ranks: 

• 40+ Content Writing Samples That Companies Can Mimic

• “Mirage Content” Is the Reason Your Company Blog Isn’t Generating Leads

• Content Creation Process: How to Produce Unique Content

How Many Times Should You Use Your Target Keyword on a Page? 

If you target one keyword per page, as described above, it generally doesn’t matter how many times you use that exact keyword throughout the content. 

In fact, we don’t track the number of times the target keyword is used when writing our articles. 

Don’t get us wrong; we do use on-page optimization tools (like Clearscope). However, the frequency of the target keyword in the content is not something we’ve ever worried about or counted. Despite this, we’ve helped a wide variety of clients (e.g., B2B, B2C, ecommerce, services) rank number one for hundreds of different keywords. 

To help illustrate this point, we went back through some of the articles we’ve written that are ranking #1 for their target keyword and counted how many times the full keyword was used. Below are two examples from two wildly different clients — one selling a low priced self-serve SaaS product to small businesses and the other offering a healthcare service to consumers for thousands of dollars. 

Note: Keyword density, which we mention below, is the number of times a keyword is used, divided by total word count. Most SEO and marketing advice will tell you to use the keyword once or twice for every hundred words, which is a keyword density of 1%–2%.

Example #1: 

• Target keyword: time clock app for multiple employees
• Number of times the keyword was used: 2
• Word count: 2906
• Keyword density: 0.0006%

Example #2: 

• Target keyword: feeling cold after a head injury
• Number of times the keyword was used: 1
• Word count: 4851
• Keyword density: 0.0002%

And these are just two examples. We were quickly able to find several different articles that are ranking number one for their target keyword with a keyword density of less than one percent.

Note: We talk about what to do when two keywords are almost identical below.

Why We Don’t Add Secondary Keywords (at Least Not Initially) To Content

A lot of SEO advice suggests targeting one primary keyword per page (as we do) but also recommends including 5–50 secondary keywords in your content. 

Secondary keywords are those that aren’t the primary keyword you’re targeting, but you include on a page to:

• Try to rank for multiple keywords with one page because the keywords are closely related to the primary keyword.

• Help rank for the primary keyword by adding a thoroughness in covering its intent.

But as we argued above, in our experience, simply adding a keyword to a page isn’t enough to rank for that keyword (even if you add it multiple times), and using secondary keywords to rank for the primary keyword isn’t the most effective method for getting high rankings. 

Let’s look at each of these in detail. 

#1. You Won’t Likely Rank for Multiple Keywords Just by Sprinkling Them In

Many people believe that inserting a specific keyword a certain number of times on a page will increase the chance of ranking for it. But this is an outdated method from the early days of SEO when Google’s algorithm was less sophisticated.

In our experience, this approach no longer works — Google tends to rank content that fully satisfies the intent of the target keyword. Simply adding secondary keywords to content focused on a different intent (i.e., the primary keyword’s intent) is unlikely to satisfy the intent of the secondary keyword, making it insufficient for ranking well. 

Note: Sprinkling in secondary keywords might help you rank somewhere on Google, but it’s rarely sufficient to reach the first page, let alone the top three positions, which are essential for achieving results. 

There is one caveat: If two keywords have the same intent, i.e., the SERP is nearly identical, you can rank for two keywords with one page. Below, we talk about how to determine if you can do this or if you need separate pages. 

Keep in mind that you don’t need to worry about this initially. After publishing, we check if we naturally rank for any closely related keywords. If we do, we’ll start tracking our position for those keywords as well. 

For the most promising secondary keywords, we’ll add relevant text and optimize the content for those keywords in addition to the primary one.

You can learn about how to recover lost rankings for secondary keywords here:
How to Find and Use Secondary Keywords to Increase Conversions: A Case Study 

#2. Using Secondary Keywords to Help You Rank for Your Primary Keyword is Unnecessary 

Another common belief about including secondary keywords is that they help address the intent of the primary keyword more thoroughly, which can improve your ranking. 

While this approach seems reasonable, we’ve found it easier to just look at the SERP to determine what needs to be covered. You don’t need to go through the extra effort of identifying all these closely related keywords that may or may not align with the intent. 

How We Decide Whether or Not to Create Separate Posts for Close Keyword Variations

One of the most common concerns about focusing on just one keyword per page is how to handle situations where two keywords are closely related. Should you still create separate pages targeting each one or is this a special case where you can target both with one page? 

In short, if the intent is the same for both keywords, you can target both with one page. If not, create separate pages. An obvious question is, “How do you determine if the intent is the same for two keywords?”

The first page of the SERP will indicate intent. If Google ranks the same web pages for both keywords on the first page, you can rank for both with one page. If the first page results are different for each keyword, you’ll need different pages. 

But even that isn’t always clear cut. 

Often, the first-page results for two keywords will overlap partially but not entirely. For example, maybe three out of ten results on page 1 are the same. In this case, it’s largely a judgment call. Look carefully at the ranking pages that aren’t the same to see where the intent differs. Then determine whether you can cover both variations and their overlap with one page or if it makes more sense to create two separate pages. 

Note that it’s not the end of the world if you make the wrong decision. For instance, if you choose to publish just one page and it doesn’t rank high enough for both keywords, you can always add a second page later. Conversely, if you create two pages but one of them just ranks for both, that’s fine too. 

Many SEOs really shy away from publishing two similar pages for closely related keywords because they’re concerned about keyword cannibalization (i.e., the idea that if multiple pages on your site compete for the same SERP position, Google may rank them both lower because it can’t determine which one to prioritize).

We don’t worry about this for two reasons: 

• If the intent is different, which is often the case, this won’t happen — even if the keywords are very similar.
  

• If Google determines that both posts are relevant for the given keyword, we typically see both achieve top rankings (see the example below).

We rarely encounter a case where Google won’t rank us because we have multiple similar posts. In fact, after discussing it with our team, we couldn’t find a single example of this happening in our client work.  

FAQs

Can I target the same keyword(s) with multiple pages? 

In short, we don’t recommend it. 

If you’re thoroughly satisfying intent as we suggest, the pages would essentially repeat the same information, providing no additional benefit. 

Can a secondary keyword in one page be the primary keyword of another page? 

Yes, if the intent is different (see our discussion above for more details). 

Should I target keywords with a web page or blog post?

The decision to create a product page or a blog post will depend on the intent behind the keyword. 

In our experience, blog posts provide a great (but often underutilized) opportunity to go more in-depth into the topic than a product page. This level of detail often satisfies intent more thoroughly and allows for a more compelling sales pitch which generates more leads. 

We’ve written about this topic in more detail here:
Landing Pages vs. Blog Posts: What’s better for ranking and conversion? 

How many keywords should I track for an effective SEO strategy? 

When starting out with SEO, many people think that you have to track the rankings for all the keywords that your web pages show up for or that you have to track a certain number of keywords to have an effective SEO strategy. 

For our clients, we track the keywords that we’re specifically targeting (i.e., the primary keywords). We’ll sometimes track secondary keywords if we find that we’re ranking well for them already. 

As for how many keywords to target, we target 3–5 keywords per month for each client. We’ve found this is a good pace that allows us to produce quality content that deeply matches intent and get results in a timely manner. 

What tools do I need for SEO? 

You don’t need any tools to implement an effective SEO strategy. However, here are a few of the tools we find useful in our own process: 

• Ahrefs for keyword research, search volume, keyword difficulty, and rank tracking.

• Clearscope for keyword research, search volume, keyword difficulty, and knowing which terms to include to help meet intent.

• Keyword Insights to see at a glance how similar the SERPs are for two different keywords.

How do I find secondary keywords?

To find secondary keywords, you can either:  

• Use a tool: Most SEO tools have a keyword finder tool where you can enter a keyword and it will generate tons of related keywords.
  

• Find them by looking at the SERP: For example, you can look at the ‘People also ask’ section, the autofill suggestions for the search bar, and, most importantly, web pages that are already ranking to see what they’re covering (e.g., headers, titles, common topics).

What are primary vs secondary keywords?

Primary keywords are the main keyword that you’re targeting with any web page.

Secondary keywords are any keywords that you add in addition to your primary keywords in order to: 

• Try to get additional rankings
• Rank more easily for your primary keyword

We discuss in detail, above, why we don’t think it’s necessary to include secondary keywords for either of these reasons. 

What are SEO keywords? 

Keywords are terms or phrases that users type into search engines like Google. SEO refers to the strategies marketers use to get their content rank organically (i.e., without paying) for these keywords.  

Learn More About Our Agency

• SEO and Content Marketing Service: If you want to hire us to execute a content-focused SEO strategy built around driving lead generation and sales, not just traffic, you can learn more about working with us here.
  

• Paid Search Service: We started PPC services at Grow and Convert after seeing thousands of dollars of wasted spend in our client’s Google Ads accounts (full story). Specifically, we noticed that many paid search agencies focus their strategy around maximizing clicks and traffic and rely heavily on Google’s automation tools. In contrast, we applied our well honed bottom of funnel strategy from SEO to paid search, where we take an active, manual approach to ensuring every campaign maximizes lead generation instead of click generation.
  

• Join Our Content Marketing Team: If you’re a content marketer or writer and would love to do content marketing in this way, we’d love to have you apply to join our team.
  

• Our Content Marketing Course: Individuals looking to learn our agency’s 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 content formats. We include several details and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we, along with other members, give feedback. Learn more here.
  

• Continue Learning: Find additional content on our blog and YouTube channel.

• Personalized email campaigns
• Retargeting ads
• Demos
• Limited time offers or discounts, etc.

While these are all useful and have their place, there are two issues with labeling these ‘marketing strategies’:

1. They are tactics not strategies. A strategy is a long term plan for how you’re going to accomplish something. A tactic is just one (of many) specific actions you can take to progress on that plan. For example, offering a limited time discount is one way of closing deals, but there are many others.
   
   
2. They are tactics to close leads, not bring them in. The job of marketing is to bring in qualified leads. It’s the sales team’s job to close the deal (with a good product demo, personalized follow-up emails, well-timed discounts, testimonials, etc.)

So, if these commonly shared tips aren’t BOFU marketing strategies, what are? 

A BOFU marketing strategy is about choosing which channels to use and how you’re going to use them to get more leads from people who are ready to buy now. 

Below, we share our experience on which channels have worked the best for BOFU marketing. Namely, we dive into organic search, paid search, and paid social and why they’ve worked well in BOFU marketing strategies we’ve executed for a variety of clients. Then, we also cover how to measure the success of your BOFU marketing strategy. 

Paid and Organic Search: Best Channels for Driving Leads

In our experience, search engine marketing (both paid and organic) is the number one best channel for BOFU marketing because unlike basically every other marketing channel, targeting someone through search means you know what the reader is looking for at that moment and can show up for keywords that indicate they’re actively searching for a solution like yours. 

While this is well accepted for paid search, most companies don’t think of organic (SEO) as a bottom of funnel strategy. Let’s discuss each in turn.

Organic Search (SEO) Strategy 

When it comes to SEO, there is a culture of using it as a top of funnel (TOFU) channel. Content and SEO teams seem to prioritize traffic above all else and pick keywords based on what will get them the most traffic the fastest. 

The typical thinking behind how a traffic-based strategy will lead to new customers usually rests on capturing emails from this traffic and dripping emails to your readers until they are ready to buy. 

But our data suggests there are two flaws with this approach. 

First, it ignores the fact that BOFU SEO keywords exist. 

BOFU keywords include any keywords that show the reader has buying intent now — i.e., that they’re looking for a tool or service to solve an existing problem. For example, ‘best route tracking software’, ‘best way to collect online payments’, ‘top software development services’, ‘automatic payroll solutions’, etc. 

Not only do BOFU keywords exist, but they also convert at a much higher rate than typical high volume TOFU keywords. For example, here are the conversion rates for 60+ BOFU and TOFU keywords for our client Geekbot: 

As you can see, BOFU keywords don’t convert just a little bit better — they convert significantly better, multiple orders of magnitude. This difference is so great, in practice it’s the difference between the entire company thinking “SEO isn’t a customer acquisition channel” to “SEO is definitely a customer acquisition channel”. We’ve seen this mental shift happen to many clients over the years.  

The natural next argument here is: while BOFU conversion rates might be better, don’t TOFU posts have so much more traffic that they end up with more conversions overall? The answer is no. We consistently find the opposite to be true. Continuing the example with Geekbot, the difference in conversion rates more than makes up for the difference in traffic.

Note in the second graph, the BOFU pieces generated over 1300 product signups when the TOFU pieces generated only 400 despite the heavy traffic disparity in favor of the TOFU pieces in the graph above. 

The second flaw with the TOFU approach is that the math of collecting emails and dripping or nurturing those until they convert doesn’t usually work out. 

Reason being, the conversion rate of BOFU content to a product call-to-action (CTA) can be as high as email collection in a lot of cases. For example, in this study of over 95 keywords we ranked clients for, BOFU keywords converted between 1%–5%, with many pieces converting at 10%+: 

These are the same conversion rates you often see for ebook and white paper downloads. Except with these BOFU conversions, visitors are immediately converting to the product CTA. They’re starting their trial or they’ve requested a demo. 

In contrast, for a TOFU conversion to a white paper or ebook, you still need to wait for another conversion to the product CTA after capturing their email. That means you need two conversions to happen. 

Mathematically, that means you’re multiplying two small percentages together to get a really small total conversion percentage. For example, say 5% download the whitepaper, then 2% of those to eventually request a demo, which results in a net 0.1% conversion rate. 

You can read more about this in our article comparing lead nurturing to direct conversions. 

Finally, there is also a logical fallacy in the “capture email and nurture to a lead” approach: 

It assumes you can manufacture buying intent, which in our experience is difficult. If someone gave you their email to join a webinar on IT trends, it doesn’t mean sending them emails with the right messaging is all of a sudden going to make them switch IT security platforms (a big decision for most companies requiring multiple stakeholders and tons of employee time to execute). 

They’ll need new software whenever they need it, and while yes, emailing them will help keep you top of mind, we’ve actually measured these conversion rates and it’s tiny when you multiply multiple conversion rates together to get a final visitor-to-lead rate.  

To help you get started with developing a good, BOFU organic strategy, check out these articles: 

• How to Find & Rank for Bottom of the Funnel Keywords
• What’s the Average Conversion Rate of Different SEO Keywords?

Paid Search

While many marketers don’t prioritize BOFU keywords enough in organic search, as we’ve argued up to now, with paid search, the culture is different. There is a focus on finding BOFU, “high intent” keywords because you have the added pressure of paying for every click. 

Most marketers aren’t going to intentionally spend money on clicks that may or may not lead to a paying customer sometime in the future — they need to clearly see that those clicks are turning into leads now in order to continue spending money on them. 

Despite this focus on BOFU, high intent keywords in paid search, we’ve nonetheless seen a few common issues with the way ad campaigns are set up and run that result in ad spend being used on TOFU and non-relevant topics that don’t drive leads. Namely:  

• Using cost-per-click (CPC) and number of clicks as the main metrics of success instead of leads. The thinking behind this type of strategy is similar to traffic-chasing, TOFU SEO strategies: if you can get really low CPC, you’ll be able to cast a wider net and therefore catch more leads. However, what we’ve seen is campaigns going after low CPC keywords that have almost no buying intent and therefore little to no chance of converting. Cheap clicks aren’t worth it if they never lead to customers.
   
• Using too much automation. Google offers many features for automating your ad campaigns that promise to optimize ad performance (e.g., dynamic ads, smart campaigns, broad match). However, in our experience, these tools inevitably make decisions that humans wouldn’t. We’ll use broad match (Google’s tool for finding extensive lists of related keywords that your ads will appear for) as an example. Let’s say a car rental agency starts with the keyword “car rentals”. Broad match search terms might include “car rentals near me” or “van rentals”, which both have buying intent and could be good options. However, it might also include “car lease deals” or even “bike rentals”. Neither of these keywords have buying intent for renting a car so the likelihood of either of them generating leads is quite low. We’ve seen this happen countless times. 

• Using account structures that are too broad. The most common mistakes here are including too many keywords per ad group, using too general of ad text, or pairing the wrong landing page with the ad. All of these issues can lead to low clicks and high bounce rates even if each individual keyword is BOFU and has the potential to drive leads. 

To dig deeper into each of these points and see specific examples of how we address these issues with our approach to paid search, check out our PPC service story here. 

Paid Social: A Good Option for B2C 

While we believe search engine marketing is the best BOFU channel for B2B businesses, we also wanted to mention paid social because it can be a good BOFU channel for B2C eCommerce businesses. 

Whereas B2B buying decisions are prompted by need (e.g., your inventory software is no longer being supported) and often include a significant process change (e.g., the entire sales team needs to learn how to use a new CRM), B2C buying decisions are much more emotional, spontaneous, and visual. 

For example, most of us don’t wait until our current shoes are unusable before buying another pair. We buy shoes (or clothes, etc.) just because we see some we like and could use. 

So for B2C businesses, it’s worth looking into paid social as a BOFU channel. We’d just recommend applying the same level of rigor to measuring leads and cost per lead as you do in search (see below). We’ve observed ad agencies engaging in similar practices in paid social, where they create campaigns that don’t generate many sales but argue that they’re beneficial for “brand awareness” — so exercise caution.

(Social media marketing isn’t where our expertise lies, however, it’s worth looking into further if you have a B2C business.)

How to Measure the Success of BOFU Marketing: Count Leads Not Traffic

When you’re filling the top of the funnel, it makes sense to track traffic. But as you move down the funnel, it’s no longer about pure volume but rather about the quality of those visitors from a sales perspective — i.e., leads. 

That’s why we believe the main metric of success should almost always be leads, not traffic. 

And, more specifically, it’s important to track leads by post (or by ad). This level of granular attribution is necessary for knowing which channels and pages are working. For example, in our Pain Point SEO post, we use the image below to show how, more often than not, the majority of your leads (and traffic) come from just 2–5 posts. 

Knowing this, you can direct more of your time and resources toward targeting similar topics, improving the rankings for high performing posts, and ensuring you keep high rankings for the posts that are working. (This is especially important for paid search and paid social since you’re paying for each click.)

We’ve written a detailed guide on how to directly attribute conversions to individual posts using GA4, which you can check out here: How to Set Up and Track GA4 Conversions. 

You can also watch our video about it here: 

When it comes to organic search, we carefully track rankings because we’ve found that rankings are directly correlated to leads. Leads often start coming in when you’re ranking on the first page for high buying-intent keywords and they really start flowing when you’re ranking in the top three positions. We use Ahrefs, but you can use any rank tracking tool that you prefer. 

FAQS

What is bottom of funnel marketing? 

Bottom of the funnel (BOFU) marketing deals with choosing which channels to use and how you plan to use those channels to attract potential buyers. 

Is it more effective to target the bottom of the funnel or the top of the funnel first? 

This is entirely dependent on your goals. For most businesses, the end goal is to increase conversions. In that case, it makes the most sense to start at the bottom of the funnel because those people are already prepared to make a purchase decision without you having to be responsible for every stage of the marketing funnel. 

Note: There are also many mid-funnel (MOFU) opportunities with higher conversion rates than TOFU marketing efforts.

How effective is a full funnel approach?

While the idea of starting at the top of the sales funnel and nurturing your target audience along until they’re at the decision stage is a long standing one, there are consumers and businesses out there that are ready to buy now. So why not target these people first? Not only is starting at the bottom of the funnel more efficient, it also results in more leads overall. For example, we’ve seen BOFU SEO posts convert at a much higher rate than TOFU posts — 10x higher. 

Then, as you run out of bottom of funnel marketing tactics and strategies to use, you can start working your way up the funnel. However, in our experience, there are way more bottom of the funnel keywords and opportunities than most businesses realize, so you can spend quite a bit of time and resources focusing entirely on ready buyers. 

Learn More About Our Agency

• SEO and Content Marketing Service: If you want to hire us to execute a content-focused SEO strategy built around driving lead generation and sales, not just traffic, you can learn more about working with us here.

• Paid Search Service: We started PPC services at Grow and Convert after seeing thousands of dollars of wasted spend in our client’s Google Ads accounts (full story). Specifically, we noticed that many paid search agencies focus their strategy around maximizing clicks and traffic and rely heavily on Google’s automation tools. In contrast, we applied our well honed bottom of funnel strategy from SEO to paid search, where we take an active, manual approach to ensuring every campaign maximizes lead generation instead of click generation.

• Join Our Content Marketing Team: If you’re a content marketer or writer and would love to do content marketing in this way, we’d love to have you apply to join our team.

• Our Content Marketing Course: Individuals looking to learn our agency’s 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 content formats. We include several details and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we, along with other members, give feedback. Learn more here.

• Continue Learning: Find additional content on our blog and YouTube channel. 

From what we’ve noticed, most marketers’ process for SERP analysis looks something like this: 

• Determine high level intent (i.e., is it commercial, informational, transactional, or navigational) 
  
  
• Determine how difficult it will be to rank (e.g., use a tool to check keyword difficulty, consider the domain authority of the top ranking content)
  
  
• Find gaps in the existing content (e.g., are they missing key topics)
  
  
• Find new keywords to target

While this is a great place to start, in our experience, it doesn’t dig deep enough to uncover the information you need in order to rank (which is the main goal of SERP analysis). 

SERP analysis is first and foremost about determining the intent behind the search because Google’s algorithm is designed to surface content that fulfills intent. So, the more you align with intent, the better your chances of ranking high.

But the simple high level “intent” categories most SEOs stop at — e.g., is the keyword commercial or informational — is not enough. Those simple categories don’t give you detailed enough answers to questions such as:

• What knowledge level are these searchers? (For example, are these high school students writing a paper or an industry expert searching for thought leadership content?)
  
  
• Do the top ranking articles offer video content? If not, would the searcher be interested in video content, and could this be an opportunity for you to provide more value? 
  
  
• Is ranking for this specific keyword actually going to help you accomplish your goals? (Yes, SERP analysis helps you figure this out — more on this below.)

These are the types of questions you need to be able to answer in order to rank well on Google — especially for keywords that will drive customer acquisition, not just hollow traffic. 

Below, we walk you through our step-by-step process for conducting a SERP analysis. But first, we’ll talk about why it’s important to differentiate between the two stages of SERP analysis:  keyword research vs creating content.  

The Two Stages of SERP Analysis: Keyword Research vs. Creating Content

We’ve noticed that most people just refer to SERP analysis as one thing but in practice there’s actually two different stages of SERP analysis:

• Keyword research to determine if a keyword is worth targeting.
  
  
• Content creation to determine what you’ll need in order to rank (i.e., topics that need to be covered, what level of expertise to write for, etc.). Note: Nine times out of ten a new post needs to be created in order to rank because it’s often impossible to fully match intent with content that was created for a different purpose. 

Although there will be some overlap, how you conduct your SERP analysis actually differs between those two stages. 

For example, you’ll look at the top ten titles of the SERP during keyword research and when outlining your content. However, during keyword research, you’ll simply be trying to determine the underlying intent of the keyword and whether or not that intent will help you reach your goals (i.e. if the keyword has buying intent). Whereas, during the outline stage, you’ll take a closer look to determine what type of post you’ll need to write (e.g., list of options, step-by-step guide), what angle is the most compelling for readers, how your title can stand out, and more. 

In general, the keyword research stage will simply include an abbreviated SERP analysis (think, a minute or two per keyword), whereas you’ll need to spend a lot more time analyzing the SERP and the details inside each of the top-ranking pages, for example, when you create SEO content (think, an hour or more).

With that in mind, here are the steps we use to analyze the SERP for any given keyword.  

5 Steps for Conducting a SERP Analysis

1. Read the Titles of the Top Results

As we covered in the example above, this first step will look different depending on whether you’re doing keyword research or creating content. 

As you’re brainstorming different keywords to target, you’ll need to verify that your intent is in line with Google’s intent. 

For example, one of our clients, Cognitive FX, specializes in treating post concussion syndrome, which is typically abbreviated ‘PCS’. So, we thought ‘pcs diagnosis’ would be a great keyword to target for them. However, if you look at Google’s results for that keyword, you’ll actually find posts on pelvic congestion syndrome. 

So, this clearly isn’t a great keyword for them as a good fraction of searchers (or most, in this case) aren’t looking for help with a previous concussion. 

That’s obviously an extreme example of a totally different definition than intended. But there are more nuanced examples where scanning the titles can tell you if a keyword has the intent you want to accomplish your goal. 

Scanning Titles to Determine Buying Intent and Whether a Query Fits Your Goals

We’ve emphasized the importance of buying intent in your content and SEO strategy a lot at Grow and Convert. In short, our data across dozens of clients clearly shows that going after high buying-intent, bottom-of-funnel keywords produces far greater ROI in terms of customer acquisition than more common top-of-funnel content (see our Pain Point SEO article or scaling content case study for more information). 

The number one way we determine the level of buying intent is to scan titles. 

For example, say you’re an accounting software company utilizing SEO to acquire new users. Many marketers would think about targeting “accounting best practices,” since it’s a common SEO piece for a company like that. 

But just scanning the titles reveals a bunch of issues with that keyword: 

Right off the bat, you see a variety of mixed intent here. There’s a typical list of tips from QuickBooks, followed by a discussion of GAAP compliance, then a podcast on accounting. This tells you that the searchers themselves aren’t that clear on what they want. 

You can also see from these results that there doesn’t seem to be much buying intent. So if your goal of SEO in this case was to get more signups for your accounting software, this likely isn’t the keyword to target. 

In contrast, take the query “best accounting software”:

Here the intent is extremely clear — almost every ranking piece is a list of accounting software. You know exactly what you need to produce to rank and you know that there’s high buying-intent (as the goal is to buy or try an accounting software). 

Finally, here’s a common example between the two extremes: 

Let’s say your accounting software has a nice feature around automatically uploading receipts to create expense reports for employees. 

You can scan the titles of queries like “automatically upload receipts” to see if the searchers of that phrase are looking for the features of your product: 

In this case, they probably are. You see Zoho, an accounting suite, ranking with their autoscan receipts help page, you also see a Google Play store page for a receipt scanner app ranking. 

It looks like a decent percentage of users are looking for apps that let them automatically upload receipts. There’s clear buying intent here, so if your app does this too, this could be a good keyword to target. 

Tip: Be sure to use incognito mode so that the results aren’t affected by your search history. 

Using Titles to Determine Nuances of Intent

Finally, titles can also help you figure out what you’ll need in the body of the content in order to rank. Knowing what Google wants within the content can also help you decide if it’s a good keyword for your company to target.

For example, let’s consider the SERP for ‘tree care techniques’. 

(The SERP has been visually condensed for better viewability but is accurate at the time of writing.)

The top organically ranking posts include slight variations of the keyword but half of the titles include some mention of pruning techniques. This tells you that your title should include both the keyword phrase and some mention of pruning. For example, “Tree Care Techniques and Tips: Pruning, Watering, and More”. 

But if we compare this to a slight variation of the keyword, ‘tree care tips’, you’ll see that the intent for the latter is more generalized. 

This reader wants an overview of what needs to be considered when it comes to tree care rather than specific techniques for pruning. 

So if pruning is a key strength of your landscaping service or your brand likes to talk about pruning for whatever reason, the former keyword (‘tree care techniques’) would be better to go after versus the latter (‘tree care tips’), even though at face value it doesn’t seem like the intent is that different. 

As you can see, just one word can change the intent behind a keyphrase and therefore the angle you need to take. 

2. Read Through the Top Results and Take Detailed Notes

Although the titles themselves give you an indication of intent, when you’re ready to dive deeper into a few of the keywords you’ve shortlisted, it helps to start reading or scanning the content inside the top few web page results. 

For example, in the screenshot below, the title is “Choosing the Right Ad Network for You (Checklist)”. However, both advertisers and publishers use ad networks.

If you only serve advertisers, you’ll need to make sure there’s intent for ad networks for advertisers not publishers, which you can’t determine just by reading the title. Instead, you’ll need to click in and scan through the content to see which audience they’re writing for.  

In our work for clients, we typically read through the top 10 results. Here are some of the things we look for and take note of: 

• What is done well (style of writing, topics that are covered, thoroughness of examples, etc.)
  
  
• What needs improvement
  
  
• Common themes/topics that all the top results cover
  
  
• Themes/topics that the top results don’t cover that’d be helpful
  
  
• What the top results are saying about certain topics
  
  
• How the information is organized (e.g., are high-level definitions covered at the beginning of the content or in a frequently asked questions section?)

With each of these points, you have to decide if you agree with how they’ve structured their content or if you want to take a different approach. The overall goal here is to create high-quality content that satisfies user intent.

3. Look for Other Google SERP Features (e.g., People also ask, Local Packs) 

It’s fairly obvious that if Google decides to include shopping results (i.e., links to products that stores are selling now), you can safely assume that there’s buying-intent behind the keyword. However, there’s more to learn from the additional SERP features that Google’s algorithm decides to include. 

Here are the most common Google features and what they can tell you: 

AI (AI Overview + From Sources Across the Web)

Google uses AI to summarizes the results on the internet in two ways: 

#1: From Sources Across the Web

#2: AI Overview (Beta)

These features can be useful because they tell you what everyone else is saying without you having to read through every article. (However, we still recommend reading through the top results.) Essentially, Google is giving you a summary of what they think the intent behind the keyword is. 

If you can match this intent better than any of the existing results, your chances of ranking well will be pretty good. 

One caveat is that you still need unique content. Yes, if you cover all of the topics mentioned in the summary, you’ll be off to a good start. But, you can’t just write what everyone else is saying and expect to stand out — let alone rank. 

How you make unique content will depend on the type of content you’re trying to create and its purpose. 

If your goal is to write SEO content that drives leads for your business, here are a couple of articles to help get you started: 

• Why your content needs “originality nuggets” to be effective
  
  
• “Mirage content” is the reason your company blog isn’t generating leads

Featured Snippets

Featured snippets typically show up for definitions or lists of steps, categories, strategies, etc. They tell you one key aspect of the search intent, but not the entire intent. 

For example, the snippet in the screenshot above was actually pulled from one of the frequently asked questions we included at the end of that post.

The bulk of the article was an in-depth summary of the study we did to compare the average SEO conversion rates of different types of keywords. 

So, you’ll likely need to clearly state relevant definitions or include a condensed version of lists when applicable (typically under a dedicated heading) in order to get a featured snippet. However, the entire article rarely needs to be centered around the featured snippet. 

People Also Ask + People Also Search

These two features are great for brainstorming new keywords to target. As we saw in the previous section, even one word change can change the intent behind the keyword. So, ‘People also ask’ and ‘People also search for’ can help you find variations that people are actually searching for. 

This is where most marketers stop — they discover a few new search terms and move on. But these features are also helpful when creating your content.  

For example, in the screenshots below, you can see several mentions of free software. This tells us that at least some of the searchers are looking for free software:

Knowledge Panels

Google’s knowledge panel is what sometimes shows up on the right-hand of the search results. It’s similar to the AI features in that it summarizes what people search for and talk about in relation to the keyword. 

When you see this, you’ll know right away that the keyword likely has informational intent. 

This is a great way to find new keywords. 

While you may be able to cover all of the necessary topics with one blog post, chances are that you’ll get more organic traffic and better success by covering each topic with a different piece of content. 

From there, you can utilize internal linking where relevant. You could also have one summary post that very briefly mentions each topic and links out to your other posts.

Google Ads

When it comes to ranking organically, we ignore Google ads altogether. 

Since businesses are paying for these spots, taking them into consideration won’t help you rank organically. And, they can be misleading during keyword research. 

During keyword research, many marketers see Google ads showing up for a given keyword and think it must be valuable because their competitors are paying for a spot. However, you don’t know how good the click through rate for those ads are or if they’re driving high quality leads. 

You can’t even be sure that there’s buying intent — let alone buying intent for your specific product/service. 

We cover this topic in more detail here and here. 

The only thing you can learn from Google ads is what messaging other companies are using.

4. Take Note of Other Types of Media That Show Up

Sometimes Google will include the top results from one of their other search results tabs (i.e., images, news, videos, shopping, etc.). 

This gives you a clue as to what the high level intent is (i.e., commercial, informational, etc.) and what type of content the searcher is looking for (e.g., images, video, news article). 

In order to determine how important it is to include video, for example, you’ll want to look at where on the first page the videos are ranking (e.g., are they at the top of the SERP or near the end). If videos aren’t the first result, you may be able to rank with a detailed blog post that uses helpful images instead of a video.

5. Look at the Backlink Profile & Domain Rating of the Top Results

This step is more relevant during keyword research to help you prioritize which keywords to target first and less relevant when you go to create the content. 

There are various SERP analysis tools you can use to check keyword difficulty (KD), backlink profiles, and the domain ratings of the top ranking websites including Ahrefs, Moz, and Semrush. 

If you don’t have access to these keyword research tools, you take a pretty good guess as to how hard it will be to rank by asking a few questions: 

• Who’s ranking? Are they a big name in the industry? Have they been around for a long time? 
  
  
• Are there any paid ads? 
  
  
• How well do the top results match intent? 

This last question is the most important and is where small businesses with low domain authority can outrank high performers. 

For example, we helped our client Cognitive FX — a clinic that specializes in post concussion treatment — outrank websites such as Mayo Clinic and WebMD:

Final Thoughts

Most keywords have multiple layers of intent. For example, a searcher may want to know what the ‘best accounting software’ is, but they also want to know what actually makes it the best. 

Your job is to identify and rank all the layers of intent for a given target keyword and then address each layer accordingly. 

Continuing with the accounting example, it might look something like this: 

1. Intent: Compare accounting software options — you’ll want to include a list of options.
   
   
2. Intent: Learn how to compare options — you’ll want to include a list of key features and factors to consider in order to choose the best option.
   
   
3. Intent: Cheap options — you’ll want to rank the list in order of cheapest to most expensive.

To a certain degree, you’ll need to copy what everyone else on the SERP is doing (if the searcher wants to know the definition of ‘SEO marketing’, you’re only going to rank if you include that definition).

However, creating great SEO content that actually ranks well is about marrying everything you gleaned from your SERP analysis with the idea of creating helpful and original content. 

You should use your SERP analysis to identify intent at a deep level and then provide a more helpful answer than any of the existing pieces of content. 

Frequently Asked Questions

What is SERP analysis? 

SERP (search engine results page) analysis is the process of reading through organic search results on the first page of Google for a particular keyword in order to determine intent and what your content will need in order to rank well. 

What are the steps for conducting a SERP analysis?

Here are the five overarching steps for how to do a SERP analysis: 

1. Read the titles of the top results.
   
   
2. Look for other Google SERP features: People also ask, related searches, rich snippets, local packs, etc. 
   
   
3. Take note of other types of media (e.g., images, video, shopping).
   
   
4. Read through the top results.
   
   
5. Look at the backlink profile and domain rating of the top results.

What tools do you need for SERP analysis? 

Although you can conduct a SERP analysis simply by entering a keyword into Google’s search engine, it can be helpful to have a SEO tools, such as:

• Ahrefs (to track your SERP rankings).
• Clearscope (to help find relevant keywords to include in your content).
• Semrush (to determine keyword difficulty).

How to Work With Us or Learn More

• Work with our agency: If you want to hire us to create and execute an SEO strategy by identifying your best keywords, creating content that is laser-focused on ranking and driving conversions, and link building to improve your ranking positions, you can learn more about working with us here.
  
  
• Join our team: If you’re a content marketer or writer who wants to do content marketing in this way, we’d love to have you apply to join our team.
  
  
• Learn our methods in our content marketing course: Individuals looking to learn our agency’s 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 content formats. We include several details and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we, along with other members, give feedback. Learn more here.