It used to take me 15+ hours to do comprehensive keyword research for a new B2B client — research that formed the basis of a strategic content plan designed to drive revenue.
Now it takes me less than 30 minutes, and sometimes as little as 10. But this isn’t the case for most B2B SEOs.
Here’s what consultants in my network have told me recently when we’ve talked about how time consuming and painful it is to do keyword research for B2B:
“Keyword research is a rate limiting step. Even if I cluster them well, I still have to go through 30,000 rows to make sense of whether each one’s a good keyword or not. It takes 30-40 hours, start to end. It directly impacts my ability to take on more clients.”
“[Keyword research] is hugely time consuming and painful. Like, I spent the better part of this week doing keyword research for this one new client.”
“The 10-15 hours [on keyword research for a new client] isn’t time well spent. A business consultant would probably tell me not to do it any more.”
In this post, I’m going to outline how the current B2B keyword research process is broken, in part due to fundamental problems with conventional SEO tools like ahrefs and Semrush.
And I’m going to describe a better approach I’ve developed over the past 8 years of SEO and content consulting for B2B companies. A keyword research approach designed to get you to actionable, conversion-driving results in 30 minutes or less, so you can quickly develop strategic, data-driven content plans from scratch.
I have so much conviction in this new approach to B2B keyword research that I’m building a new startup around it: ContentSage.
If you’re interested in getting early access to the ContentSage, you can join the waitlist at contentsage.ai.
The current, busted B2B keyword research process
When you’re using keyword research to generate a content plan, there are basically two steps:
- Use one or more keyword tools to generate a huge list of potential keywords. (The relatively quick and easy part.)
- Sift through thousands of potential keywords to find the ones relevant to your business and your content strategy. (The time-consuming, pain-in-the-ass part.)
1. Generate a huge list of potential keywords (~1 hour)
Cast a wide net (<30 mins)
Today’s keyword research tools aren’t designed to help you quickly get to actionable results — they’re designed to help you quickly generate a ton of potential keywords with varying degrees of relevance. Full stop.
And that’s whether you’re using competitor website analysis, keyword suggestions (like “matching terms” or “related terms” from ahrefs), People Also Ask and auto-complete data sourced from Google, or other features.
The benefit of generating keywords from these tools is that you end up with a pretty comprehensive list of contenders — the chances are small that you’re going to miss some important keyword topic.
The (considerable) downside is that the keyword lists generated from these tools are so comprehensive exactly because they’re so long. By design, these sources cast a very wide net.
It’s easy for keyword lists generated from these common sources to reach thousands — if not tens of thousands — of rows.
For example, an analysis based on five competitors in the software test automation industry produces over 9K potential keywords. (Throughout the piece, I’ll use the example of the software test automation industry, the industry of one of my B2B clients.)
2. Sift through your (huge) keyword list for relevant results (5-40 hours)
This is where the bulk of the time-consuming and painful work of B2B keyword research happens.
Identifying the relevant keywords in a list thousands of rows long is a tedious process because you have to manually process every row to make sure you’re not missing any needles amidst all the hay(stacks).
Of course, sifting through a huge list of potential keywords is a good opportunity to learn about the search patterns within the industry at hand. But, wow, that’s an incredibly inefficient way to learn.
Consolidate semantically similar keywords with automated clustering
What happens in most big lists of potential keywords is that you’ll get a bunch of different variations on a keyword that all effectively mean the same thing — they all reflect the same search intent. As we say in SEO, they’re “semantically similar.” (Like all the keywords reflecting an intent for no-code test automation software in the example above.)
Since you’d only create a single piece of content to target all of these similar keywords, you’d ideally want to group (or “cluster”) them together. But manually locating all the semantically similar keywords in a list of thousands isn’t practical, let alone feasible.
Thankfully, there are now a number of tools that can automatically cluster semantically similar keywords. The better ones of these tools compare the first page of search results for each keyword in your list, and cluster the keywords that have similar search results. (Because similar search results reflect similar search intent.)
Your clustering results will vary, but for one client, consolidating semantically similar keywords into clusters reduced the size of my list of potential keywords by over 65 percent. Instead of having to manually process over 6,700 rows of keyword data, I only had to process ~2,300 rows. Still painful, but definitely less painful.
Sift for keywords relevant to your business
To some degree, each competitor in an industry tends to offer a different set of features. When you sift through a list of potential keywords, one type of noise you have to filter out is related to irrelevant features or benefits.
For example, my test automation client doesn’t offer API testing, but several of their competitors do. Lots of keywords around the topic of API testing appeared in my list after doing competitive analysis, so I need to filter those irrelevant keywords out.
There are also various other common patterns of keywords in B2B that will be irrelevant to your business. For example: keywords related to job searches and from people Googling the answers to test questions (e.g., “which of these is an automated vulnerability scanner you can use during penetration testing?”).
This extensive noise is a direct result of the bias of most keyword tools towards “more output is better (even at the expense of relevance).”
Sift for keywords relevant to your content strategy
Driving revenue for the business means taking a strategic approach, prioritizing keywords that are more likely to drive conversions.
Experienced SEOs and content marketers know it’s easiest to drive conversions with content targeting keywords closer to the “bottom of the funnel” — the intent of these keywords indicates someone far along in the buyer journey, close-ish to making a purchase decision.
Often, a keyword’s search intent is obvious because it fits into one of several patterns, like “best [category] software” or “[top competitor] alternatives.”
But sometimes it’s not so clear. In these cases, the tried-and-true solution is to Google the keyword and see what kind of results Google provides.
Checking Google for a single keyword’s intent takes maybe 30 seconds on average, but I’ll have to do it tens or hundreds of times in the course of processing a big list of potential keywords. That’s more time-consuming, rote work to add to the slog of processing a list of keywords.
SEO tools like ahrefs have started incorporating search intent labels into their keyword research results, but the four intent labels the SEO tools use — informational, commercial, transactional, navigational — are so broad as to be practically useless.
Take “informational” intent, for instance. Both “what is test automation” and “how to automate testing” are keywords with informational intent, but the former is a top-of-funnel query and the latter is a middle-of-funnel query. To drive conversions, I care more about the latter query, “how to automate testing,” because I can use the associated content to show how my client’s test automation solution can solve the searcher’s problem.
As a B2B marketer, I want to quickly identify the part-of-funnel intent of each keyword in my list of potentials so I can simply filter to the keywords that align with my content strategy.
A 10x faster, strategic keyword research process
Now that we’ve identified exactly what’s wrong with the current B2B keyword research process, we can talk about what’s required to fix it.
You can implement a few of the following methods on your own using currently-available tools, like keyword clustering apps and AI. But the full velocity and effectiveness generated by these approaches only happens when they’re all integrated together in a cohesive system.
That’s why we’re building ContentSage.
If you’d like to try ContentSage for your B2B keyword research, sign up for the waitlist at https://contentsage.ai/.
Note: ContentSage is currently in prototype form, so it’s functional — but not pretty. I’ll add more screenshots to this post once our designer has had some time with the tool.
The inverse keyword research method
We’ve established that the functionality of keyword research tools is at odds with the goals of B2B keyword research:
- Keyword research is intended to identify keywords relevant to your business and content strategy.
- Outputs from keyword research tools like ahrefs and Semrush don’t consider relevance — they favor quantity over quality, leaving you to sift for the signal in the noise.
So if we were to design a keyword research process from scratch that focused exclusively on getting relevant results, faster, what would that look like?
The crux of this new approach is completely inverting the order of operations of old, busted B2B keyword research. (Which is why I’m tentatively calling this “inverse keyword research.” Let me know if you can think of something more catchy. 🤷♂️)
The old, busted method is to:
- Generate a huge list of potential keywords of varying degrees of relevance. (~1 hour)
- Sift through the (noisy) list, thousands of rows long, to find the keywords relevant to your business and content strategy. (5-40 hours)
Inverse keyword research flips the order of operations and takes less than 30 minutes:
- Quickly create a comprehensive list of the topics relevant to your business and content strategy using AI.
- Identify the most relevant keywords for each one of those topics using keyword search functionality based on intent.
Instead of searching for needles in a haystack, you’re the owner of a needle-making machine.
1. Generate a list of highly-relevant topics using AI
In the old, busted world, it’s difficult to uncover all the relevant keyword opportunities until you sift through a long list generated by, for example, competitor analysis.
Even a subject matter expert in the industry isn’t going to know all of the terminology and search patterns that people use on Google to find relevant information about your industry and its solutions. They’re too close to the company’s perspective, not to that of a prospective customer on Google.
Instead of spending hours sifting through a long, noisy spreadsheet to learn about the terminology and search patterns of an industry, you can learn those things in minutes from AI.
Generative AI tools like ChatGPT are ideally suited to act as your expert industry partner. They can quickly generate comprehensive lists of topics that are relevant to your business and your content strategy.
For example, here are the results of a prompt we’ve built into ContentSage to give me a list of product categories in which my client is regularly mentioned.
In just seconds, I’ve surfaced all the topics and terminology (and then some) it took me hours to uncover in the old, busted process. I simply disregard the topics irrelevant to my client’s business, like API testing.
Now I’ve got a juicy list of bottom-of-funnel topics to research. In ContentSage, we’ve engineered prompts that let you quickly identify all the topics a B2B marketer would want to cover for their industry in the bottom of the funnel and beyond.
2. Identify the most relevant keywords for your topics using keyword search based on intent
With the help of my expert AI partner, I now have a good set of topics relevant to my (client’s) business and content strategy.
The next step is to determine the organic search opportunities, if any, around those topics.
Let’s take the “software testing tools” topic, for example, which has pretty clear bottom-of-funnel intent. I only want to uncover keywords that reflect the specific intent of “software testing tools,” because that’ll help me get in front of the prospective customers searching for this type of solution.
What I don’t want or need is to know every single keyword that contains the words “software,” “testing,” and “tools,” because that’ll include (according to ahrefs) noise like “debugging tools in software testing,” which isn’t relevant to my client’s business. (Debugging is what happens after software testing is done.) So, using most keyword tools — which don’t understand the intent of queries — is out.
Instead, ContentSage evaluates every query you enter based on its intent, so it can return keyword results based on relevance — the top results are the keywords most aligned with the specific intent of your query.
Put another way, they’re the keywords Google thinks are most relevant to your query. (We’re using the Moz API to retrieve keyword data, including search volume and difficulty.)
Indeed, ContentSage’s top result for a search on “software testing tools” is a cluster of 46 semantically-similar keywords about testing tools. If I can rank well for one of these keywords, I have a good chance of ranking for most or all of them.
Aside from quickly getting highly-relevant results, another benefit of using this intent-based approach in ContentSage is that you can use natural language queries. It’s not like most keyword research tools that will return empty results if your query doesn’t exactly match something in the keyword database.
If Google can interpret the intent of your query, ContentSage can interpret the intent of your query.
I could search on something like “list of good software testing tools” (which is not a term that appears in the ahrefs database) and ContentSage returns the “testing tools” keyword cluster as the top result, again.
Intelligent automation
I’m already saving a lot of time inverting the keyword research process to make identifying relevant topics the first step and then producing keyword results based on relevance, not quantity. I get relevant results quickly, and there’s no more sifting through thousands of rows in spreadsheets.
And just like in ahrefs or another keyword research tool, in ContentSage I can quickly filter and sort keyword results based on volume, difficulty, and intent. And — uniquely – by relevance and content type.
But what about the other parts of the old, busted keyword research process that eat up time (and tend to break your spirit)? For example, dealing with semantically-similar keywords or repeatedly checking the SERPs to check (a) the intent of a potential keyword or (b) the type of content that’s most likely to rank.
Thanks in part to generative AI, we’ve automated all those steps in ContentSage so you can quickly assess the relevance and value of each keyword without leaving the tool.
Automatically cluster semantically similar keywords
All keyword results returned in ContentSage are automatically clustered into semantically similar groups.
Beyond reducing the time it takes to manually process search results, clustering gives you a better idea of the relative search (volume) opportunity between different topics (compared to looking at single, unclustered keywords).
Automatically identify (actionable) search intent
No more having to check the SERPs, because all the information you need is right there in the tool.
Using generative AI, ContentSage automatically identifies the primary search intent of any keyword cluster it outputs (and secondary intent, if any).
But we don’t use the typical four SEO intent labels (informational, commercial, transactional, navigational), since some of them aren’t actionable.
Instead, ContentSage uses intent labels that are relevant to a strategic B2B marketer, like “Commercial” (bottom funnel), “How to” (mid funnel), and “Overview” (top funnel).
Automatically identify the type of content most likely to rank
For searches on B2B software solutions (like “software testing tools”), it’s not always clear which is more likely to rank — a blog listicle or a feature page — until you check the SERP.
Using generative AI, ContentSage evaluates the SERPs for you so it can indicate for each keyword cluster it outputs the specific kind of content you should create to improve your chances of ranking success.
Less time, less pain, more relevant keyword results
It used to take me 15+ hours to do keyword research for a new client.
Now I spend a fraction of the time and I have a list of things I’m very happy I don’t have to do any more:
- I don’t spend hours (or any time at all) sifting through spreadsheets to find relevant keywords amidst a bunch of irrelevant results.
- I don’t have to jump in and out of Google to verify the intent or intended content type for potential keywords.
- I don’t have to switch between tools to cluster semantically similar keywords.
With the inverse keyword research approach and automated processes in ContentSage, I can create a comprehensive, data-driven content plan for a B2B client in less than 30 minutes. (And sometimes less than 10.)
If this new approach to B2B keyword research sounds good to you, sign up for the ContentSage waitlist at https://contentsage.ai/.