Part 1: Keyword collection Before we start grouping keywords into clusters, we first need our dataset of keywords from which to group from. If this term is occurring very frequently throughout our long list of keywords, it’ll be highly important when we start grouping our keywords. Now we can look at our keyword data with a second dimension: not only the number of times a term or phrase occurs, but also how many words are in that phrase. Finally, to give more weighting to phrases that recur less frequently but have more terms in them, I put an exponent on the number of terms with a basic formula: In this example dataset, we are going from a list of 10k+ keywords to an analysis of terms and phrases to understand what people are really asking. We explain: This exercise provides us with a handful of the most relevant and important terms and phrases for traffic and relevancy, which can then be used to create the best content strategies — content that will rank highly and, in turn, help us reap traffic rewards for your site. We narrow down the list, removing any negative keywords or keywords that are not really important for the website. Once we have our final list of hot words, we organize them into broad topic groups like this: The different colors have no meaning, but just help to keep it visually organized for when we group them. You've successfully taken a list of thousands of keywords and grouped them into relevant keyword groups. You can take each keyword group and create a piece of optimized content around it, targeting dozens of keywords, exponentially raising your potential to acquire more organic traffic.
If your goal is to grow your organic traffic, you have to think about SEO in terms of “product/market fit.”
Keyword research is the “market” (what users are actually searching for) and content is the “product” (what users are consuming). The “fit” is optimization.
To grow your organic traffic, you need your content to mirror the reality of what users are actually searching for. Your content planning and creation, keyword mapping, and optimization should all align with the market. This is one of the best ways to grow your organic traffic.
Why bother with keyword grouping?
One web page can rank for multiple keywords. So why aren’t we hyper-focused on planning and optimizing content that targets dozens of similar and related keywords?
Why target only one keyword with one piece of content when you can target 20?
The impact of keyword clustering to acquire more organic traffic is not only underrated, it is largely ignored. In this guide, I’ll share with you our proprietary process we’ve pioneered for keyword grouping so you can not only do it yourself, but you can maximize the number of keywords your amazing content can rank for.
Here’s a real-world example of a handful of the top keywords that this piece of content is ranking for. The full list is over 1,000 keywords.
Why should you care?
It’d be foolish to focus on only one keyword, as you’d lose out on 90%+ of the opportunity.
Here’s one of my favorite examples of all of the keywords that one piece of content could potentially target:
Let’s dive in!
Part 1: Keyword collection
Before we start grouping keywords into clusters, we first need our dataset of keywords from which to group from.
In essence, our job in this initial phase is to find every possible keyword. In the process of doing so, we’ll also be inadvertently getting many irrelevant keywords (thank you, Keyword Planner). However, it’s better to have many relevant and long-tail keywords (and the ability to filter out the irrelevant ones) than to only have a limited pool of keywords to target.
For any client project, I typically say that we’ll collect anywhere from 1,000 to 6,000 keywords. But truth be told, we’ve sometimes found 10,000+ keywords, and sometimes (in the instance of a local, niche client), we’ve found less than 1,000.
I recommend collecting keywords from about 8–12 different sources. These sources are:
- Your competitors
- Third-party data tools (Moz, Ahrefs, SEMrush, AnswerThePublic, etc.)
- Your existing data in Google Search Console/Google Analytics
- Brainstorming your own ideas and checking against them
- Mashing up keyword combinations
- Autocomplete suggestions and “Searches related to” from Google
There’s no shortage of sources for keyword collection, and more keyword research tools exist now than ever did before. Our goal here is to be so extensive that we never have to go back and “find more keywords” in the future — unless, of course, there’s a new topic we are targeting.
The prequel to this guide will expand upon keyword collection in depth. For now, let’s assume that you’ve spent a few hours collecting a long list of keywords, you have removed the duplicates, and you have semi-reliable search volume data.
Part 2: Term analysis
Now that you have an unmanageable list of 1,000+ keywords, let’s turn it into something useful.
We begin with term analysis. What the heck does that mean?
We break each keyword apart into its component terms that comprise the keyword, so we can see which terms are the most frequently occurring.
For example, the keyword: “best natural protein powder” is comprised of 4 terms: “best,” “natural,” “protein,” and “powder.” Once we break apart all of the keywords into their component parts, we can more readily analyze and understand which terms (as subcomponents of the keywords) are recurring the most in our keyword dataset.
Here’s a sampling of 3 keywords:
- best natural protein powder
- most powerful natural anti inflammatory
- how to make natural deodorant
Take a closer look, and you’ll notice that the term “natural” occurs in all three of these keywords. If this term is occurring very frequently throughout our long list of keywords, it’ll be highly important when we start grouping our keywords.
You will need a word frequency counter to give you this insight. The ultimate free tool for this is Write Words’ Word Frequency Counter. It’s magical.
Paste in your list of keywords, click submit, and you’ll get something like this:
Copy and paste your list of recurring terms into a spreadsheet. You can obviously remove prepositions and terms like “is,” “for,” and “to.”
You don’t always get the most value by just looking at individual terms. Sometimes a two-word or three-word phrase gives you insights you wouldn’t have otherwise. In this example, you see the terms “milk” and “almond” appearing, but it turns out that this is actually part of the phrase “almond milk.”
To gather these insights, use the Phrase Frequency Counter from WriteWords and repeat the process for phrases that have two, three, four, five, and six terms in them. Paste all of this data into your spreadsheet too.
A two-word phrase that occurs more frequently than a one-word phrase is an indicator of its significance. To account for this, I use the COUNTA function in Google Sheets to show me the number of terms in a phrase:
=COUNTA(SPLIT(B2," "))
Now we can look at our keyword data with a second dimension: not only the number of times a term or phrase occurs, but also how many words are in that phrase.
Finally, to give more weighting to phrases that recur less frequently but have more terms in them, I put an exponent on the number of terms with a basic formula:
=(C4^2)*A4
In other words, take the number of terms and…
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