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Serpstat updates 10 min read

Rank Your Page For a Whole Group Instead Of a Single Keyword With Our New Keyword Clustering Tool

Elena K.
Editorial Head at Serpstat
After several months in the beta stage with limited access, we're so excited to announce that our Keyword Clustering and Text Analytics tools are finally publically available.

In this post, I'll tell you about our Keyword Clustering tool. What is it? Why do you need it? How does it works? Keep reading to find the answers to all these questions.

This post is all about Keyword Clustering. To learn more about Text Analytics tool, read this post ;)
We've created the Serpstat Website SEO Checker which allows you to find out the key metrics of any site in just a few clicks.
To examine a domain, you don't need to open Serpstat in a new tab - all necessary data is available with one click directly on the site!
Click on the icon and you'll get multiple SEO metrics about a domain or page: visibility, traffic, keywords, top competitors in the region, etc.
Read the post to learn about the benefits of the updated Serpstat checker.

What is keyword clustering?

Keyword clustering is the process of grouping semantically related keywords in such a way that keywords in the same group (called a cluster) are relevant to a particular website page. Let's say you have a list of keywords: iphone 6s, iphone 7, bmx, mountain bike, road bikes and macbook. Here is what you'll get after clusterization:

Cluster #1: {iphone 6s, iphone 7} are relevant to website.com/phones/apple

Cluster #2: {bmx, mountain bike, road bikes} for website.com/cycling/bikes

Cluster #3: {macbook} that fits website.com/laptops/apple

Why do you need keyword clustering?
  • Collecting the right keywords for specific pages;
  • Grouping of semantically related keywords;
  • Reliable automatic analysis of a set of keywords;
  • Creating a site's SEO structure;

How others keyword clustering tool works?

The most fundamental drawback of the majority of existing keyword clustering tools is that the clusters are formed based on the cluster's center — the keyword with the highest search volume. They find similar keywords which share SERPs results with it.

Here are some problems such method cases:
resulting clusters may either contain keywords without a strong semantic similarity;
too many clusters that could have been merged into larger clusters appear;
parsing search volume data takes time.

How our keyword clustering tool works?

Unlike many competitors' solutions, Serpstat employs intelligent hierarchical clustering where clusters are combined in a supercluster. This being said, no preliminary data collecting like keyword search volumes required, you only need to upload a list of keywords and choose the region and clustering parameters.

Advantages of our method:
We don't set a cluster center;
Serpstat considers all keywords analyzing their connections in SERPs;
We use intelligent hierarchical clustering: the keywords are grouped into clusters, clusters are merged into higher-level groups called superclusters, and finally, superclusters are combined into even higher-level groups called protoclusters;
Serpstat checks the connection strength of all analyzed keywords according to the settings you chose.
Connection strength — is the number of identical URLs in keywords' top-30 search results. Thus the highest number of mutual results is 30 URLs.

What keyword clustering methods Serpstat provides?

In fact, there are only two of them: Linkage strength and Type of

1) There are two types of linkage strengthWeak and Strong.

By setting "Weak" parameter the keywords with several mutual URLs in Top-30 search results will be combined into the cluster.

While "Strong" sets more URLs in common as a condition for keywords merging into a single cluster.

2) There are two types of grouping to choose from: Soft and Hard

"Soft" parameter tells the system that a cluster can be created if at least one pair of keywords has less or more common URLs in Top-30 search results (depending on the previous Weak/Strong choice).

Hard one requires all keywords in a cluster to have less or more common URLs in Top-30 search results (the requirement for the number of common keywords is defined in the previous step where you selected Weak or Strong clustering).

The resulting clusters contain synonymous keywords with a high semantic similarity. At the same time, this clustering method produces lots of clusters as the keywords can be merged into a cluster only if they are closely related.
After clustering is finished, some keywords can fall into the Unsorted directory. These are keywords that have no semantic similarity to the topic of the analyzed keyword set and should be removed from the dataset.

An alternative solution here is to create separate pages for these keywords or move them to one of the created clusters if you believe they belong there.

What clustering method to choose?

Weak+Soft? Weak+Hard? Strong+Soft? Or maybe Strong+Hard?

The default is Weak+Soft. But you can choose any pairing according to your needs. The decision should be based on the semantic similarity of the objects from your dataset.

If the keywords are initially closely related, for example, sneakers of different brands, you should choose Strong+Hard or Strong+Soft so that only the closest synonyms are combined into a cluster. As a result, you'll get lots of clusters to use for separate pages or specific categories.

In the case of various products and services, for example, you're collecting keywords for a multi-product store or medical center with a full range of health-care services, it's worth selecting Weak+Soft. The choice of Strong+Soft will produce more clusters and a possibility to get more topic-specific clusters.
To learn more on how our clustering tool works watch this video →

What about the price?

No additional payments, Keyword clustering tool is included to your tariff plan price:

  • Users with Plan A have no access to keyword clustering tool.
  • Plan B — 4000 keywords.
  • Plan С — 12 000 keywords.
  • Plan D — 25 000 keywords.
Note: As Keyword clustering is currently available as a Beta version, you can upload no more than 2 000 keywords within one project.

How to use Keyword Clustering tool?

Launching the keyword clustering

1. Go to the "Tools" section and open Grouping and Text Analytics tool.
2. Click on "Create a project" button.

3. Enter the project name and domain (if you want to run a text analytics for a particular domain).

4. Paste the list of keywords or import them from csv or txt files.

5. Then choose search engine and region.

6. Finally, choose linkage strength and type of grouping and click "Finish."

Make a cup of coffee and sit back, grouping takes some time :)
7. After a while, you'll get something like this:
Where 3 stands for cluster, which includes the keywords on the right,
2 — supercluster, and 1 — protocluster.
Protocluster — is a set of superclusters. Generally, protocluster is made up of superclusters related to a specific category of objects.

Supercluster — is a set of clusters. It combines keywords with a high semantic similarity score, but slightly less similar than keywords in a cluster.

Analyzing the report:

On the right you'll see all your keywords grouped into clusters with some additional info:
1. Every keyword has its connection strength. It shows how related to the cluster's subject this keyword is (from 0 to 1).

2. Homogeneity shows the strength of connection between keywords.

3. If you add a domain while creating a project, we'll display the page which suits the cluster's subject the most. If you don't specify the domain, you can add URL manually by clicking Add URL.

Let's pass to some additional options, click on drop-down menu:

Click on "Search keywords" to find the desired keyword within the cluster.

2. To delete the cluster click on "Delete subgroup" button.

3. If you want to delete some keywords from the cluster, tick them and click on "Delete keywords."

4. By clicking on "Toggle metatop" you'll see the list of major competitors in SERP for keywords from this cluster. The higher a page's rank in the meta-top, the more relevant it is to the cluster's topic.

That's finally it ;)
P.S. Our math analyst is already working on more detailed keyword clustering tool overview. Don't hesitate to ask, if you have any questions. We'll do our best to answer all of them ;)

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