Keywords play a huge role in search engines ranking web pages, but how do you know which keywords go to which pages when faced with thousands of keywords?
Keyword clustering is a way to group related keywords, questions, and long-tail keywords together.
Once you’ve done that, you can start creating content and ranking for those keywords.
There are several advanced keyword clustering tools available; plus a few free tools. This article will outline how to use start clustering keywords.
TABLE OF CONTENTS 👇
What is keyword clustering?
Keyword clustering is the process of analyzing and grouping thousands of keywords into smaller clusters with a similar meaning or intent for better rankings in search engine results.
For example, the keywords “SEO content plan” and “SEO content creation strategy” share similar intent and would be grouped on a page called “SEO content strategy” along with another page called “SEO content marketing” that would link to and from a pillar page called “SEO content“.
|Pillar Page: SEO content|
|Cluster: SEO content strategy||Cluster: SEO content marketing|
SEO content strategy
creating a content strategy for SEO
SEO and content strategy
SEO content creation strategy
SEO content planning
SEO content plan
SEO content marketing
SEO and content marketing
what is content marketing in SEO
how content marketing helps SEO
content marketing optimization SEO
how to do content marketing in SEO
Why does keyword clustering matter?
You might already be doing keyword clustering during the keyword research stage of a website or content plan, and you already know some of the main benefits.
- Build topical authority.
- Better search engine rankings.
- Increase search traffic.
- Helps organize website structure.
- Better internal linking to improve the crawling of a website by search engine bots.
However, not all SEO experts agree that organizing your content will lead to better SEO performance.
From my own experience, I’ve had plenty of pages on many different websites performing well in terms of rankings and clicks without using a pillar page/cluster or hub/spoke model.
Terms to know about
Since search marketers like Brian Dean and Hubspot have taken it upon themselves to invent new SEO terms, here’s a quick glossary.
- A topic cluster is a model of building a library of content around a central idea; a Pillar Page.
- A Pillar Page is the same as a Hub.
- A Content Cluster is the same as a Spoke.
- A content cluster or hub is made from a group of keywords with very similar meaning or intent achieved by keyword clustering.
- The Parent Topic (in Ahrefs) is the keyword responsible for sending the most traffic to the top pages for a keyword, e.g., for the search query “led lights for garage,” the parent topic is “led lights.”
How to build a topic cluster with a keyword cluster tool
The keyword clustering process consists of 4 main steps.
Step 1: Create a list of keywords
You need a keyword dataset that includes the keyword and search volume as a bare minimum obtained from a keyword research tool or Google Search Console.
Frase users can now download all the keywords their website ranks for inside Frase Analytics.
If you’re working with no existing data, use either Ahrefs or Ubersuggest, depending on your budget.
You should have an extensive keyword list of at least 1000 keywords.
Step 2: Group keywords into clusters
The next step is to group keywords into clusters; doing this manually would be extremely time-consuming, hence why keyword clustering tools exist.
Step 3: Create cluster pages
Once your keywords are grouped, your content planning begins by creating cluster pages.
Create a content brief for your content writers; with Frase, of course.
Then write the copy for the pages, optimize it, add images and publish.
Step 4: Create a pillar page
The next step is to create your pillar page.
A pillar page is everything a reader needs to know about a topic.
We’ve created a pillar page content brief template that outlines precisely the content that your writer should include.
Then, the pillar page links out to cluster pages using internal links.
So, let’s look at some of the keyword grouping tools available that should save you hours of keyword research time.
Keyword Cupid looks at the first 5 to 10 pages of Google and figures out how closely related these words are to each other and then groups them into tightly themed clusters.
- Import your keyword data into a project.
- View the mindmap or download the spreadsheet.
I used Keyword Cupid twice last year using my own Google Search Console keywords.
As you can see, this report shows the page theme (cluster page), all the keywords in that cluster, and the total search volume for the cluster.
For example, it suggested the intent behind “local business advertising” and “advertise your business locally” were different.
Here’s a link to the full keyword clustering report.
There’s a free trial where you can import 500 keywords or pay $49.99 a month for 5,000 keyword credits.
KeywordInsights.ai is an excellent keyword clustering tool endorsed by many SEO professionals that position its service as “the smartest way to group keywords and classify search intent at scale.”
They have introduced a keyword discovery feature for monthly subscribers, for pay-as-you-go customers, you will need to bring your keywords.
- Create a project and select the options and default settings.
- Review and generate your report.
- Keyword Insights will notify you by email upon the report completion.
- Click the project tab and open up the Excel file or with Google Sheets (via the Drive link).
There are a few tabs where you can view the data, but I prefer the Cluster Data tab, which shows 3 columns:
- Cluster (the page to create).
- The column with keywords (all the keywords grouped into the cluster).
- Total Search Volume of all the keywords in the cluster.
Since I only uploaded 400 keywords, the report isn’t very different from using the Ahrefs parent topic for keyword clusters.
However, in the sample report based on 3,500 keywords, you can see over 50 pages you could create, with search volume per day between 780 and 86,300 per month.
Here’s a link to the full keyword clustering report.
A basic report for 3,500 keywords costs just over $55.
SEOScout’s free keyword grouping tool clusters related keywords into groups using n-gram word similarities, not SERP analysis.
Here’s how to use the tool.
- I exported 848 keyword ideas related to “SEO content” from Ahrefs.
- Import the CSV into Google Sheets.
- Copy and paste the keywords into SEO Scout.
- The results are displayed in your browser, which you can export and then import into Google Sheets.
Since there is no search volume data associated with each keyword, you’ll need to manually count the number of keywords per cluster or create a pivot table to determine the best clusters.
If you want to try SEO Scout, you can copy the keywords in this spreadsheet in the 2nd column of the first tab.
Charly Wargnier’s keyword cluster tool
Charly Wargnier is a UK-based developer who has created a keyword clustering tool where you can use data from Ahrefs, Semrush, or Google Search Console.
- First up, I exported 848 keyword ideas related to “SEO content” from Ahrefs.
- I dragged the CSV file into the tool and received the results instantly.
- Then exported the report and imported it into Google Sheets.
Keyword clustering reports aren’t easy to interpret; you’ll get a column for the keyword and cluster name while retaining the original data.
It would be helpful to see the number of keywords in a cluster but let’s not forget this tool is in beta mode.
If you want to try this keyword clustering app, you can copy the keywords in this spreadsheet in the 2nd column of the first tab.
Interview with Suganthan Mohanadasan of Keyword Insights about keyword clustering
We interviewed Suganthan Mohanadasan, CEO of KeywordInsights.ai, who answered some questions we had.
What is keyword clustering, what problem does it solve, and where in the SEO workflow does it come into play?
Keyword clustering involves grouping keywords that are similar to each other together. Essentially a “keyword cluster” is a group of keywords that mean the same topically and can be targeted together on a single page.
Where in the SEO workflow does keyword clustering come into play?
It’s helpful in many cases, such as identifying content gaps, forming new content strategies, and most importantly identifying keyword cannibalization.
Do you have any website examples that implemented what you consider a world-class keyword clustering strategy?
I really like how Forbes content is built around topic clusters. For example, if you visit their credit cards page. https://www.forbes.com/advisor/credit-cards/
You will see how they have the generic high-level topic supported by a bunch of spokes (supporting content) such as “best credit card,” “best reward credit cards,” and “best airline credit cards.”
Is there any minimum number of keywords required for keyword clustering to be worthwhile?
It depends on the topic you’re researching. We recommend using as many keywords as possible to get a full picture of the topic.
I also recommend doing some cleanup of the keywords by removing duplicates, branded terms, and any irrelevant queries from the list.
How do you measure cluster-level organic growth and revenue?
You can measure the organic growth and revenue by ranking your high competitive and generic terms highly on Google.
As I showed in the Forbes example, you can clearly see that they rank #1 for “credit cards,” and this is a place that used to be owned by Nerdwallet for a while. Not only do they rank their head terms, but they also rank well on their clusters.
It’s likely the revenue is coming from Ad revenue and commissions. So, it can be measured.
How do you tackle fractured/mixed intent inside a cluster?
At Keyword Insights, we developed a machine learning model that can accurately detect three types of content in the top 10 results of Google for a given keyword. We call this context.
We can detect how many of the top 10 results are articles, product pages, or other types of pages. So, when you cluster keywords, we also apply the context to your keywords and your clusters.
So when you see the intent is mixed.
Let’s say for a given cluster you see 5 articles and 5 product pages ranking; in this case, I would optimize both my article for the cluster and look at the terms that trigger the product pages and optimize that as well.
The idea is to try and capture both spots.
The beauty of our context is you can run this periodically and map the ‘intent shift’ over time, and optimize your strategy accordingly.
How do you evolve from keyword clusters to a hub/spoke content plan?
After we create clusters (based on Google results), we use natural language processing to find semantic relationships between the clusters. This creates hierarchical clustering.
We use keyword search volume to determine the ‘head cluster’ or the head term.
We know from the above Forbes example that ‘credit cards’ is a head term (highest search volume and generic keyword), so Keyword Insights will create a hub/spoke model based on this logic.
You can use this to map the site architecture and how content should be structured. For example, which keyword should be targeted as a hub and which keyword clusters should form the supporting content.
There are several benefits to organizing keywords into well-organized groups on your site using some of the data sources and keyword clustering tools we’ve outlined.
Will keyword clustering work for me? Well, it depends; try it.
Start a Frase.io trial and start creating content briefs and optimizing content for pillar and cluster pages.
A topic cluster model is a new approach that shifts your focus to broader topics instead of targeted long-tail keywords. By doing this, you organize your content so that your target audience get the answers they're looking for quickly.How do you create a topic cluster? ›
- Audit Your Existing Content. Even if you have ideas for new topic clusters, start with the content you already have. ...
- Identify Topics and Subtopics. ...
- Organize Your Subtopics by Intent. ...
- Conduct Keyword Research. ...
- Make the Links. ...
- Check Your Other SEO Factors. ...
- Review the Data.
- A pillar page on a core topic. This page should cover a wide range of user intents. ...
- “Cluster” or supporting pages that cover related topics in more depth. A cluster page tends to have a narrower focus on a specific user intent. ...
- Internal linking between all of the pages.
- Import The List Into Your Python Notebook. import pandas as pd import numpy as np serps_input = pd.read_csv('data/sej_serps_input.csv') serps_input. ...
- Filter Data For Page 1. ...
- Convert Ranking URLs To A String. ...
- Compare SERP Similarity.
A topic cluster model is a new approach that shifts your focus to broader topics instead of targeted long-tail keywords. By doing this, you organize your content so that your target audience get the answers they're looking for quickly.How to create cluster in SQL? ›
On the Table Designer menu, click Indexes/Keys. In the Indexes/Keys dialog box, click Add. Select the new index in the Selected Primary/Unique Key or Index text box. In the grid, select Create as Clustered, and choose Yes from the drop-down list to the right of the property.What are the 3 types of cluster system? ›
The types of clustered operating systems can be divided into three types: Asymmetric Clustering Systems, Symmetric Clustering Systems, and Parallel Cluster Systems.What are the three major steps in cluster analysis? ›
The hierarchical cluster analysis follows three basic steps: 1) calculate the distances, 2) link the clusters, and 3) choose a solution by selecting the right number of clusters.What is keyword clustering tool? ›
Keyword clustering is a practice search engine optimization (SEO) professionals use to segment target search terms into groups (clusters) relevant to each page of the website.How to create a cluster in Excel? ›
To cluster values, first select the Person column, go to the Add column tab in the ribbon, and then select the Cluster values option. In the Cluster values dialog box, confirm the column that you want to use to create the clusters from, and enter the new name of the column. For this case, name this new column Cluster.
- Think like a customer when you create your list. ...
- Select specific keywords to target specific customers. ...
- Select general keywords to reach more people. ...
- Group similar keywords into ad groups.
- Do Keyword Research. Keyword grouping usually starts with collecting a large database of related keywords for a certain niche that also includes long-tail keywords. ...
- Create High-Level Keyword Groups. ...
- Make Sub-Groups. ...
- Work Through Sub-Groups Until They Are Targeted & Small.
- Step 1: Build a Keyword List.
- Step 2: Categorize Your Keywords.
- Step 3: Plan Your Keyword Strategy.
- Step 4: Optimize or Create Your Content.
- Step 5: Track Your Keyword Rankings.
The grid-based technique is fast and has low computational complexity. There are two types of grid-based clustering methods: STING and CLIQUE.How many topic clusters should you have? ›
The answer? As many as you can possibly think of! There is no limit to the amount of topics you can cover in a single cluster. And as long as they aren't too similar to each other and continue to provide value to the reader, they will continue to help your website compete in the search engines!What are the different types of clustering in SQL? ›
- SQL Server Basic Availability Groups.
- SQL Server Always On Availability Groups.
- SQL Server Failover Cluster Instances with Shared Storage.
Clusters in standard query language (SQL) databases are collections of multiple physical servers that are grouped together using a LAN connection to create a single database with high availability, less hardware failures, and reduced downtime.What is an example of a database cluster? ›
An example of this would be when a company has multiple data centers for a single website. With many servers across the globe, no single server is a “master.” Shared-nothing is also known as “database sharding.”What are the four clusters? ›
In the end, four personality clusters emerged on the researchers' new map. They were: average, reserved, self-centered, and role model.What are the two most popular form of clusters? ›
There are two different types of clustering, which are hierarchical and non-hierarchical methods. Non-hierarchical Clustering In this method, the dataset containing N objects is divided into M clusters. In business intelligence, the most widely used non-hierarchical clustering technique is K-means.
Context in source publication
content analysis on the definitions yielded six clusters representing: (1) competence, skills, abilities; (2) actions; (3) information and resources; (4) objective; (5) context; and (6) time as outlined in Table 2.
K-Means is probably the most well-known clustering algorithm. It's taught in a lot of introductory data science and machine learning classes. It's easy to understand and implement in code!What are the types of cluster building? ›
... the study 3 types of building cluster pattern are used: grid, staggered and irregular ( Figure 2).What are the clustering tools in R? ›
Clustering is a popular technique for segmenting data. The primary options for clustering in R are kmeans for K-means, pam in cluster for K-medoids and hclust for hierarchical clustering.What is step 4 in cluster analysis? ›
- Step 1: Confirm data is metric. ...
- Step 2: Scale the data. ...
- Step 3: Select Segmentation Variables. ...
- Step 4: Define similarity measure.
The recommended approach is a four-stage process: initial response, assessment, major feasibility study, and etiologic investigation. Each step provides opportunities for collecting data and making decisions.What is cluster analysis tool? ›
Cluster analysis is a data analysis technique that explores the naturally occurring groups within a data set known as clusters. Cluster analysis doesn't need to group data points into any predefined groups, which means that it is an unsupervised learning method.Which are some keywords tool? ›
- Google Keyword Planner.
- Google Trends.
- Keyword Tool.io.
- Term Explorer.
- Moz's Keyword Difficulty Tool.
- SE Ranking.
K-Means. K-Means clustering algorithm is easily the most popular and widely used algorithm for clustering tasks.Which tool is used for keywords planning? ›
Keyword Planner helps you research keywords for your Search campaigns. You can use this free tool to discover new keywords related to your business and see estimates of the searches they receive and the cost to target them.
Hierarchical clustering on this dataset can be performed using Excel's built-in Data Analysis Toolpak. Organize the data into a matrix with rows representing each customer and columns representing each product category.How do you prepare data for cluster analysis? ›
To perform a cluster analysis in R, generally, the data should be prepared as follows: Rows are observations (individuals) and columns are variables. Any missing value in the data must be removed or estimated. The data must be standardized (i.e., scaled) to make variables comparable.
- Audit Existing Content and Find Gaps. ...
- Develop and Manage Your Pillar Pages & Topic Clusters. ...
- Analyze Content Against Specific Keywords. ...
- Keep Track of Your Content.
Struct is the keyword used to define structure in C.Is there a keyword generator? ›
Google Keyword Planner as a Keyword Generator
Google's keyword tool is able to capture a huge volume of internet traffic and keyword variations, making it the go-to keyword generator for many.
There are a few things to consider when choosing a clustering algorithm, including the type of data, the number of clusters, and the computational cost. Additionally, it is important to consider the nature of the clusters, as some algorithms are better suited for certain types of clusters.Which two types of clustering are there in clustered system? ›
The clustered systems are a combination of hardware clusters and software clusters. The hardware clusters help in sharing of high performance disks between the systems.How many types of cluster analysis are there? ›
Broadly, there are 6 types of clustering algorithms in Machine learning. They are as follows - centroid-based, density-based, distribution-based, hierarchical, constraint-based, and fuzzy clustering.What are three ways I could combine my keywords into search strings? ›
The three most commonly used operators are AND, OR, NOT. These are known as Boolean operators. They can be used to broaden or narrow a search and to exclude unwanted search terms and concepts. You can type these operators in between your search terms (Fig.What is keyword mapping? ›
What Is Keyword Mapping? Keyword mapping is the process of assigning a keyword to each page. Pages and keywords are also arranged in groups of keyword clusters to create a topical website structure. Google ranks based on specific URLs and each URL needs to be optimized and relevant for the target keywords to rank.
- Upload Your Keywords. Your first step is to upload keywords to STAT. ...
- Segment Your Keywords Into Relevant Groups. The next step is to take your keyword list and segment them into relevant groups. ...
- Re-upload Your Keywords With Associated Tags. ...
- Analyze Rankings By Category.
- K-means. A popular unsupervised learning algorithm for clustering is k-means. ...
- Hierarchical Clustering. ...
- DBSCAN. ...
- Latent Semantic Analysis (LSA) ...
- Latent Dirichlet Allocation (LDA) ...
- Neural network based clustering.
To use it, copy and paste a list of keywords into the search field, and hit “Get Started”. You'll also see the same Keywords Results Page you see when you use the “Find new keywords” tool. No matter which tool you ultimately used, you end up in the same place: The Keywords Result page.How do you write a clustering algorithm? ›
- Step-1: Select the number K to decide the number of clusters.
- Step-2: Select random K points or centroids. ...
- Step-3: Assign each data point to their closest centroid, which will form the predefined K clusters.
- Step-4: Calculate the variance and place a new centroid of each cluster.
Topic cluster example
One example is an ecommerce category page that organizes products into subcategories and links out to those pages. Another example would be a content hub page that breaks down various types of life insurance and links out to blog posts or guides about those subtopics.
Topic clusters help search engines better understand the hierarchy of your website. As such, they may help search engines see your site as an authority on a specific subject. Basically, a topic cluster is just another way of laying out your website's architecture.What is a good example of clustering? ›
- Identifying Fake News. Fake news is not a new phenomenon, but it is one that is becoming prolific. ...
- Spam filter. ...
- Marketing and Sales. ...
- Classifying network traffic. ...
- Identifying fraudulent or criminal activity. ...
- Document analysis. ...
- Fantasy Football and Sports.
Topic clusters are a part of an SEO strategy that organizes the structure of your page by topics and not just keywords. Content clusters are used in the pillar page. The pillar page is the central page that gives a broad overview of the main topic and displays all the relevant content you provide.How do I create a topic cluster in HubSpot? ›
- Log in to your HubSpot account (if you don't have one, get started here).
- Navigate to Marketing > Planning and Strategy > SEO.
- Select Add a topic and input your topic.
- Click on your topic and select Create Topic.
- Step 1 – Understand the content structure and user intent. ...
- Step 2 – Define the topic cluster with Google Search Console. ...
- Step 3 – Do keyword research and find new opportunities. ...
- Step 4 – Split and update the content. ...
- Step 5 – URL and navigation. ...
- Step 6 – Internal links.
A pillar page is a website page that provides a comprehensive overview on a topic. A topic cluster is a collection of interlinked articles and website pages centered around one umbrella topic.How do you create a cluster in tableau? ›
Drag Cluster from the Analytics pane into the view, and drop it on in the target area in the view: You can also double-click Cluster to find clusters in the view. When you drop or double-click Cluster: Tableau creates a Clusters group on Color, and colors the marks in your view by cluster.