Keyword Clustering

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Keyword Clustering - The Ultimate Search Engine Optimization Solution

If your target is to gain more organic traffic, you have to think about SEO in terms of marketing. 

The market is keyword search which means what the searchers are actually looking for.

The product is the content which means what consumers (users) are consuming.

The fit is the optimization.

In order to increase you traffic you have to make your content relevant to the users’ need, which means it should answer all questions of user. The keyword mapping, planning and creation of your content should be according to the market. This is one of the best ways to grow your organic traffic.

Things to Keep In Mind

Why bother with keyword grouping?

When one web page can rank for multiple keywords then why don’t we focused on planning and optimizing content that targets dozens of similar and related keywords?

Why aiming only one keyword with one piece of content when you can target 30?

The effect of keyword grouping in order to gain more organic traffic is really ignored. In this article we are going to share with you our own method that we have developed for keyword clustering which enables you to do it yourself. Moreover you can increase the number of keywords your amazing content can rank for.

This below is the 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 would be foolish to focus on just one keyword, because you would lose 90% + of this opportunity.

Below are the examples of all keywords, some of which could potentially be targeted:

Let’s dig in 

Part 1: Keyword collection

First of all we need a collection of keywords from which we can make group, be we initiate the process of grouping keywords into clusters.

In this initial step our job is to find every possible keyword. In this process we will be getting many irrelevant keywords unintentionally. And it is pretty good to have a large number of keywords plus with the perk of filtering out the irrelevant keywords instead of having a small number of keywords to focus on.

Usually for any client project we collect from 1000 to 6000 keywords. But to be honest, sometimes we found more than 10,000 keywords, and sometimes (based on the example of local niche customers) we found less than 1000.

It is better to collect keywords from different sources like 8 to 12. Some of sources are:

  1. Brainstorming your own ideas and checking against them
  2. Third-party data tools ( Ahrefs, Moz SEMrush, AnswerThePublic, etc.)
  3. Your competitors
  4. Your existing data in Google Search Console/Google Analytics
  5. Autocomplete suggestions and “Searches related to” from Google
  6. Mashing up keyword combinations

There is no limitation of sources for collecting keywords, and there are now more keyword research tools than ever before. Our goal in this is to be so broad that we will never have to step back and “find more keywords” in the future – unless, of course, there is a new topic that we need to focus on.

At this point suppose that you have invested some time for collecting a long list of keywords, filtering them out by removing the duplicates. Now you have a semi dependable  search volume data.

Break it Down

What Are The Different Types Of SEO?

At Syndiket, we believe four types of SEO exist – and we have an acronym to represent those 4 types of SEO. The acronym is T.R.A.P. 

“T” stands for Technical, “R” stands for Relevancy, “A” stands for Authority, and “P” stands for popularity. Search engine optimization has many smaller divisions within the 4 types, but all of them can be placed into one of these 4 buckets.

Generally, technical SEO for local businesses carry the least importance for ranking. Technical SEO has a bare minimum that is required and this usually includes things like site speed, indexation issues, crawlability, and schema. Once the core technical parts are done, minimal upkeep is required.

Relevancy is one of trivium elements of SEO. It has equal importance with popularity signals and authority signals. Relevancy signals are based on algorithmic learning principles. Bots crawl the internet every time a searcher has a search. Each search is given a relevancy score and the URLs that pop up for a query. The higher the relevancy score you attain, the greater your aggregated rating becomes in Google’s eyes. Digital marketing is a strange thing in 2020, and ranking a website requires the website to be relevant on many fronts.

Google’s Co-creator, Larry Page, had a unique idea in 1998 which has led to the modern-day Google Empire. “Page Rank”, named after Larry Page himself, was the algorithm that established Google as a search engine giant. The algorithm ranked websites by authority. 

Every page of a website has authority and the sum of all pages has another authority metric. The authority metric is largely determined by how many people link to them (backlinks). The aggregate score of all pages pointing to a domain creates the domain score, which is what Syndiket calls “Domain Rating”, per Ahrefs metrics. The more a site is referenced, the more authority it has. But, the real improvement to the algorithm came when Google began to classify authority weight. 

If Tony Hawk endorsed Syndiket for skateboarding, it would carry a lot more authority than 5 random high school kids endorsing Syndiket. This differentiation in authority happened in 2012 with the Penguin update. Authority SEO is complicated but VERY important.

Popularity signals are especially strong for GMB or local SEO, but popularity and engagement are used for all rankings. The goal of this signal is for Google to verify its own algorithm. You can check off all the boxes, but if your content is something real people hate, Google has ways to measure that. Syndiket has proprietary methods of controlling CTR (click-through rate) but we also infuse CRO methods into our work to make sure people actually like the content. Social shares and likes are also included in this bucket.

Part 2: Term Analysis

You have a bulky list of 1000+ keywords; the next step is to turn this list into something useful.

We start with term analysis. What the does that mean?

We separate each keyword into distinct component words that include that keyword so that we can see which words are most common.

Take an example of the keyword “best natural protein powder” it has 4 words best, natural, protein, and powder. By separating these keywords into their components part we will be able to analyze more easily that which terms are being used commonly in our keyword dataset.

Sampling of 3 keywords:

  • best natural protein powder
  • how to make natural deodorant
  • most powerful natural anti inflammatory

By looking at the above sampling you will notice that the term natural is common in al three of these keywords. If this term is common throughout our long list of keywords, it will be highly important when we start clustering our keywords

To make this task easy you need a frequency counter. The best tool for this is  Write Words’ Word Frequency Counter.

It is really simple; you just have to paste your list of keywords and click submit, and Voila!

Ignore the preposition terms like “to”, “For” and “is” and copy and paste your list terms into a sheet.

You won’t always get the most value just by looking at individual words. Sometimes a phrase of two or three words gives you an understanding of what would not otherwise be. In this example, you see the words milk and almonds, but you find that this is actually part of the phrase almond milk.

To collect this information, use the Phrase Frequency Counter from WriteWords and repeat the process for phrases that have two, three, four and more terms in them. Paste all of this data into your spreadsheet too.

A two-word phrase that is more common than a one-word phrase indicates its importance. To calculate this, I use the COUNTA function in Google Sheets to show the number of words in a phrase:

=COUNTA(SPLIT(B2,” “))

Now we can look at the data of our keyword from another dimension: not only the number of words or phrases, but also the number of words in this phrase.

Finally, in order to give more weight to phrases that are repeated less often, but contain more words, I put the exponent of the number of words using the basic formula:

=(C4^2)*A4

In other words, take the number of words and raise it to a power, and then multiply by the frequency of its occurrence. All this gives more weight to the fact that a two-word phrase, which is less common, is still more important than a one-word phrase, which can be more common.

Since I don’t know how to properly raise it, I conduct several tests and continue to sort through the sheet to find the most important words and phrases on the sheet.

When you see this now, you can already begin to see the patterns, and you are already able to better understand your researchers.

In this sample dataset, we go through a list of 10,000+ keywords to understand what people are actually asking. For example, the phrases “what is the best” and “where can i buy” are phrases that we can fully understand.

I mark important words or phrases. I try to keep this number below 50 and to a maximum of around 75 otherwise, grouping will get risky in Part 5.

Part 3: Hot Words

Hot words are the words or phrases of our last session that we considered to be the most important. We have explained the hot words here in more detail.

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 customer (or doing this for yourself), there are usually 3 questions we want answered for each hot word:

  1. Which of these terms are the most important for your business? (0–10)
  2. Which of these terms are negative keywords (we want to ignore or avoid)?
  3. Any other feedback about qualified or high-intent keywords?

We make the list more reliable by removing any negative keywords or keywords that are not really important for the website.

Once we have our final list of hot words, we arrange them into broad topic groups like this:

The different colors are to keep it visually organized for when we group them.

Note that the word stems play an important role here.

For example, consider that all of these words below have the same meaning:

  • Blog
  • Blogger
  • Blogs
  • Bloggers
  • Blogging

So, when we group keywords to consider “blog” and “blogging” and “bloggers” as part of one cluster, we will need to use the word “blog” for all of them. The word “stems” is our best friend when forming a group. Synonyms can be arranged in a similar way, which basically means the same thing (and the intention of the same user) as two different ways of saying “build” and “make” or “search” and “see”. There are ways.

Part 4: Preparation for Keyword Grouping

Now we are going to prepare for the heroic 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 explains us whether this word stem or word is in that keyword or not. You have to set individually the term for each column to get your “YES” or “NO” answer. I then drag this formula from all of the rows to get the entire 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 Grouping

Now at this point you are all set for keyword clustering.

To do this phase right, you need:

  • Good intuition
  • Good judgment to make tradeoffs when breaking keywords apart into groups
  • A deep understanding of who you’re targeting, why they’re important to the business, user intent, and relevance

This part need experience, it is hard to train anyone to do it because it need practice and the person him/her self can master it.

 At the top of the sheet, I use the COUNTA function to show me how many times this word step has been found in our keyword set:

=COUNTA(C3:C10000)

This is important because, as a rule, it is best to start with the niche items that have the least match with other items. If you start too broadly, your keywords will overlap with other groups of keywords, and it will be harder for you to divide them into meaningful groups. Start with narrow and specific groups first.

To begin, you want to sort the sheet by word stem.

The word stems that occur only for a few times won’t have a large amount of overlap. So I start by arranging 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 to break up one group of keywords into another, we will have several different groups of keywords. If the groups you come across are too wide, you can reduce them even further to narrow the subgroups of keywords for more focused pieces of content. You can follow the same procedure for this broad set of keywords and make it a microcosm of the same process of dividing keywords into smaller groups based on the basics of the words.

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.

Here you go

You have successfully taken a list of thousands of keywords and grouped them into relevant keyword groups.

Now you can ultimately attain that “product/market fit” we talked about. It’s great.

You can take each keyword group and make a piece of optimized content around it, aiming dozens of keywords, exponentially raising your potential to acquire more organic traffic. 

All done. Now what?

Now you can start planning for the new content that you never knew you needed to create. On the other hand you can plot your keyword groups (and subgroups) to active pages on your website and add in keywords and optimizations to the header tags, body text, and so forth for all those long keywords you had ignored.

Keyword grouping is largely overlooked, underrated and ignored. This creates a huge new opportunity to adapt to words that were not there. Sometimes it’s just adding one phrase or a few sentences focusing a long-tail keyword here and there that will bring in that additional search traffic for your website. Do it dozens of times and you will continue to increase your organic traffic.

What do you think?

Leave a comment below and let me know your viewpoint on keyword clustering.

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Syndiket Marketing 
1033 Demonbreun St, Nashville, TN 37203
(615) 551-5257

Syndiket is a Nashville based digital marketing agency with a strong emphasis on SEO, PPC, & Web Design. Your clients are searching for you. Be there with Syndiket.

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