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 collectionBefore 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
Part 2: Term analysisNow 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
=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)*A4In other words, take the number of terms and raise it to a power, and then multiply that by the frequency of its occurrence. All this does is give more weighting to the fact that a two-word phrase that occurs less frequently is still more important than a one-word phrase that might occur more frequently. As I never know just the right power to raise it to, I test several and keep re-sorting the sheet to try to find the most important terms and phrases in the sheet.
When you look at this now, you can already see patterns start to emerge and you're already beginning to understand your searchers better. 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. For example, “what is the best” and “where can i buy” are phrases we can absolutely understand searchers using. I mark off the important terms or phrases. I try to keep this number to under 50 and to a maximum of around 75; otherwise, grouping will get hairy in Part 5.
Part 3: Hot wordsWhat are hot words? Hot words are the terms or phrases from that last section that we have deemed to be the most important. We've explained hot words in greater depth here. Why are hot words important? 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.
When developing your hot words list, we identify the highest frequency and most relevant terms from a large range of keywords used by several of your highest-performing competitors to generate their traffic, and these become “hot words.”When working with a client (or doing this for yourself), there are generally 3 questions we want answered for each hot word:
- Which of these terms are the most important for your business? (0–10)
- Which of these terms are negative keywords (we want to ignore or avoid)?
- Any other feedback about qualified or high-intent keywords?
Part 4: Preparation for keyword groupingNow we're going to get ourselves set up for our Herculean task of clustering. To start, copy your list of hot words and transpose them horizontally across a row. List your keywords in the first column.
Now, the real magic begins. After much research and noodling around, I discovered the function in Google Sheets that tells us whether a stem or term is in a keyword or not. It uses RegEx:
=IF(RegExMatch(A5,"health"),"YES","NO")This simply tells us whether this word stem or word is in that keyword or not. You have to individually set the term for each column to get your “YES” or “NO” answer. I then drag this formula down to all of the rows to get all of the YES/NO answers. Google Sheets often takes a minute or so to process all of this data. Next, we have to “hard code” these formulas so we can remove the NOs and be left with only a YES if that terms exists in that keyword. Copy all of the data and “Paste values only.” Now, use “Find and replace” to remove all of the NOs. What you're left with is nothing short of a work of art. You now have the most powerful way to group your keywords. Let the grouping begin!
Part 5: Keyword groupingAt this point, you're now set up for keyword clustering success. This part is half art, half science. No wait, I take that back. To do this part right, you need:
- A deep understanding of who you're targeting, why they're important to the business, user intent, and relevance
- Good judgment to make tradeoffs when breaking keywords apart into groups
- Good intuition
=COUNTA(C3:C10000)This is important because as a general rule, it's best to start with the most niche topics that have the least overlap with other topics. If you start too broadly, your keywords will overlap with other keyword groups and you'll have a hard time segmenting them into meaningful groups. Start with the most narrow and specific groups first. To begin, you want to sort the sheet by word stem. The word stems that occur only a handful of times won’t have a large amount of overlap. So I start by sorting the sheet by that column, and copying and pasting those keywords into their own new tab. Now you have your first keyword group! Here's a first group example: the “matcha” group. This can be its own project in its own right: for instance, if a website was all about matcha tea and there were other tangentially related keywords. As we continue breaking apart one keyword group and then another, by the end we're left with many different keyword groups. If the groups you've arrived at are too broad, you can subdivide them even more into narrower keyword subgroups for more focused content pieces. You can follow the same process for this broad keyword group, and make it a microcosm of the same process of dividing the keywords into smaller groups based on word stems. We can create an overview of the groups to see the volume and topical opportunities from a high level. We want to not only consider search volume, but ideally also intent, competitiveness, and so forth. Voilà! You've successfully taken a list of thousands of keywords and grouped them into relevant keyword groups. Wait, why did we do all of this hard work again? Now you can finally attain that “product/market fit” we talked about. It’s magical. 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. Boo yah!