In this article we're going to dive deeper into the world of keyword research and focus on keyword clustering. Keyword clustering is an effective SEO-approach, which is not yet widespread or for which the knowledge is scarce. We are going to explain wat this method entails and how you can apply it in your projects. We will do so by answering the following questions:
Okay, here we go.
Let's start at the beginning, what is keyword clustering? In one sentence, keyword clustering is a method for grouping keywords based on relevance and similarity.
Keyword clustering is a method for grouping keywords based on relevance and similarity. Either by comparison of language or other data. - KeyWI
Groups can consist of different keywords that are used by people to find the same information online. Sometimes these search terms are closely related by language, for example "pizza delivery" and "online pizza delivery". However, this is not necessarily the case. Another example "Search engine optimisation" and "SEO". What we are trying to show you is that you don't want to miss out on potential visitors, because they are using different keywords in their search. By applying keyword clustering you are able to find relevant and related keywords which you can use in your content. These keywords are also known as semantic keywords. Either they have similar meaning and intention or they enforce each other and therefore can be used in the same topic cluster. By doing so Google is able to better understand which contents and goals your pages have. It helps to maximise the opportunity to rank higher and be discovered more often. To be able to comprehend this approach, we will take a step back and explain two search engine algorithm changes.
In 2013 Google introduced a new algorithm, known as Hummingbird. The world of SEO changed and was never the same again. As a consequence of this update Google is able to understand longer and more complex search terms. Hummingbird was the algorithm that brought Semantic Search to life. The focus shifted from ranking on single keywords to creating relations between so called entities. Understanding entities and their relations is how Google is can comprehend content better and optimise their results for searchers. Besides elements as location, IP-address, user history and personal data was integrated in the search engine.
In 2015 the Rankbrain update followed, many SEO experts see this as the point of no return. The focus switched completely from single keywords to topic focus. Focussing on topics and entities resulted in more precise interpretation of search intentions. Where Hummingbird switched from single keywords to entities, Rankbrain went even further by making the connection to the search intent. Besides that, Rankbrain was able to answer never before questions by making use of the entity logic. The algorithm searched for relations between earlier seen entities and showed similar results for the most related search terms.
Semantic grouping, also known as semantic clustering, of search terms aids in gaining more organic traffic on your website. To rank higher on Google it is important to reflect the search and answer in your content. The content and keywords should also reflect its target audience and intentions. This is where search intentions become important, it is the 'why' when people search for something. This search intent is dividable in to four categories:
When someone is searching for information, the search intent is informational. Sounds logical, right? The questions or keywords that are focussed on extracting specific information like who, what, where, when and why are used often. Navigational intent is what we see when people use the search engine to navigate on the web. For example "Facebook log in", "Gmail" or "Support Spotify". This user already knows where he or she wants to navigate towards, but is choosing for the easy way through the search engine. The remaining two categories, commercial investigation and transactional intention, may overlap a bit. Commercial investigation is classified as someone is looking for a place to make a transaction but does not know where yet or not which transaction. Think about it, you would like to find a music streaming service that contains all music in the world. However, you have never heard of Spotify or Apple music before. So this person types in "music streaming service free", he or she is investigating something commercial which potentially will lead to a transaction. When someone is ready to buy a product they have the intention to make a transaction: transactional intention.
Search intent is an important factor when setting up your logical website structure or writing targeted content. Imagine, you want to buy flowers for your mom on mother's day, however you end up on an informational blog about the ten most popular gifts for mother's day containing flowers. Your intent was transactional, however you ended up on an informational page.. Try to understand how the market is searching and therefore how content should fit. This may sound like stating the obvious, however how do you learn the intent a keyword may have or even a cluster of keywords?
KeyWI is an ai driven keyword clustering tool, that can analyse and group thousands of keywords in minutes. The tool works in four simple steps, which will be explained next.
Als user of the tool there is actually only one thing you need to do, input the keywords in the tool. As an expert on the topic of your website or project, you know best which keywords to use. Input the keywords in KeyWI in a CSV file with search volumes when you're ready to start.
KeyWI will then use those keywords and gather search engine data for each one of them. If you upload 100 or 1.000 keywords, it's not a problem for KeyWI. The data KeyWI gathers is similar to the results a search engine user sees when using a keyword.
The search engine data is turned into relations, by a smart in-house developed algorithm. Each keyword is compared to each other and KeyWI determines if there is a relation. The relations are based on the results that a user sees in the search engine and not based on language similarities. That's how you can be sure the keywords with the same results end up together, just like Rankbrain looks for similar entities.
The last step for KeyWI is to group the keywords based on the found relations between all keywords. The keyword are grouped and thats how keyword clusters are born. Oh and in the meantime KeyWI predicts the search intent based on the same search engine data. Now you not only know which keywords belong together, but also which intentions keywords may have. You're able to better understand your target audience now.
So, now what.. You have keywords clusters and search intentions.. In the KeyWI environment the clusters, subclusters and intentions are visually displayed. This makes it easy to determine which content page to start on next and how to setup your data driven sitemap structure. Writing content can now be done with precision and according to search engine logic and with the correct intentions. A site structure is derivable from the visualisations first ring, which gives you the biggest clusters or main topics in the set. Sometimes it's good to iterate a few times, when you miss a main topic or see fragmented smaller clusters it might be so you are missing important keywords.
The tine to get started with keyword clustering is now. You'll be amazed what related keywords can do for your site traffic when adding them to your content. Rather not spend a lot of time on keyword clustering? Check out KeyWI, looking for a demo? Or looking for the pricing?
Any other questions, please let us know!