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. But this isn’t the case for most B2B SEOs.
Here’s what consultants in my network have told me recently when we 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 tell you about ContentSage, a keyword research tool I’m developing based on insights from my 8+ years of SEO and content consulting for B2B companies.
Using AI and another new technological approach, ContentSage is designed to get you actionable, conversion-driving results in 30 minutes or less. So you can quickly develop strategic, data-driven content plans from scratch.
If you’re interested in getting early access to 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)
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.
That’s the case whether you’re using competitor gap 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 tools 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 this industry, that of one of my B2B clients.)
Part of the problem is that ahrefs, Semrush, and other keyword research tools don’t consider your intent when you submit a query. Instead, they treat user queries as simple strings of text rather than understanding (or caring about) their contextual meaning.
So, for example, searching ahrefs for the topic of “test automation” will return some relevant results I care about (“test automation software”) and a lot of irrelevant results I don’t care about (“covid test automation,” “lab test automation,” “remote test automation jobs.”)
And if you search for something that isn’t in the ahrefs database (“no code test automation software”), you’ll get zero results — even if there are many keywords in the database that reflect the intent of your search (like “codeless test automation tools”).
What I want as a user is for a keyword tool to tell me, “Ah, I understand you want to rank for the topic of ‘no-code test automation software,’ so here’s a list of keywords that people actually use when they want to find content for that topic on Google.”
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).
Even if you use a clustering tool to group all the semantically-similar keywords, cutting a list of 10K keywords down to 4K with clustering (a reasonable expectation) still means you have to process 4K keywords. Definitely less painful — but still painful.
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.
Sift for keywords relevant to your business
In B2B keyword research, there are so many common patterns of irrelevant noise to filter out. For example:
- Irrelevant features and topics. (In my client’s case, any keyword around “api testing.”)
- Keywords related to job searches. (“automation test jobs in greater los angeles area,” “test automation engineer hiring test.”)
- Keywords searched by people Googling the answers to test questions (“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 most 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.
10x faster strategic B2B keyword research with inverse keyword research and intelligent automation
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.
If you’d like to try ContentSage for your B2B keyword research, sign up for the waitlist at https://contentsage.ai/.
Inverse keyword research
The crux of ContentSage’s new approach is completely inverting the order of operations of old, busted B2B keyword research. Which is why I’m calling it “inverse keyword research.”
The old, busted method is to:
- Generate a huge list of potential keywords of varying degrees of relevance.
- Sift through the (noisy) list, thousands of rows long, to find the keywords relevant to your business and content strategy.
Inverse keyword research flips the order of operations so you can get to relevant results faster, skipping all the irrelevant noise:
- Quickly create a comprehensive list of the topics relevant to your business and content strategy using AI.
- Use keyword search functionality based on intent to identify the most relevant keywords for each one of those topics.
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
You don’t have to spend hours sifting through a long, noisy spreadsheet to learn about the terminology and search patterns of a particular industry. Because you can learn those things in minutes from AI.
A generative AI tool like ChatGPT is ideally suited to act as your expert industry partner. It can quickly generate comprehensive lists of topics that are relevant to your business and your content strategy — without you needing to know anything about the industry at hand.
Unlike human subject matter experts, AI models have the benefit of knowing all the actual words people use when they talk about billions of different topics.
In ContentSage, we’ve engineered an AI to help you quickly identify all the topics you’d want to cover for your industry, business, and product(s) in the bottom of the funnel and beyond. The only input you need to provide is the name of a noteworthy competitor in the industry or the industry’s main product category.
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.
2. Use keyword search based on intent to identify the most relevant keywords for your topics
Now that my industry-expert AI partner has helped me uncover a juicy list of bottom-of-funnel topics, 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.
What I don’t want or need is to know every single keyword that contains the words “software,” “testing,” and “tools,” 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 search intent of your query.
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
Inverse keyword research already saves a lot of time compared to old, busted processes and tools by skipping right to the relevant keywords you care about. No more sifting through thousands of rows in spreadsheets.
But what about the other parts of the old, busted keyword research process that eat up time (and tend to break your spirit)?
Thanks in part to generative AI, we’ve automated all the following 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).
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.
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 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 comprehensive 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 technology and automated processes in ContentSage, I can create a comprehensive, data-driven content plan for a B2B client in less than 30 minutes.
If this new approach to B2B keyword research sounds good to you, sign up for the ContentSage waitlist at https://contentsage.ai/.