How To Automate SEO Keywords Clustering

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There are many SEO benefits that apply directly and indirectly to the growth of a business or company. If you want to stay competitive it’s important to use the strategic advantages of search engine optimization. It improves your digital marketing efforts.

In general, SEO targets unpaid organic search traffic rather than paid traffic as a marketing strategy. It allows for increasing the quality and quantity of website visitors in the long term. At the same time, you will not use additional funds. Therefore, you can get more customers by making content that ranks well on Google.

Of course, websites that rank high on search engines like Google, Yahoo, and Bing are typically considered to be higher quality and more trustworthy than sites that are in lower-ranking positions. And users trust search engines to filter the results for them. Earlier keywords clustering was done manually and took quite a lot of time. But now you can get the benefits of implementing ML in this area and automate almost all processes. Therefore, learn more about how to beat the first places in the search engines for your business and get much more than you have now.

What Role Does Keywords Clustering Play in SEO Strategy?

To increase your organic traffic, your content should mirror the reality of what users are actually searching for. Keywords can give you this information and help your content match market demands. Keywords clustering streamlines the SEO content plan and improves productivity. This is one of the best ways to grow your organic traffic and become rewarded with a full, scalable content plan that recalls the main goals. Also most importantly, every part of your content can be seamlessly optimized.

Keywords Clustering for Organic Traffic

Keywords clustering is an advanced SEO strategy and can give you the edge you need to win in competitive verticals. The more keyword research and clusters you can create early on, the more it pays back in Google ranks and seamless marketing strategy. Through Search Engine Results Page analysis, you will understand your customer on another level. SERP helps to determine if and how you can rank for a key phrase and whether the effort is worth the reward. Thereby you create content that will target your focus words. Also, it directs what you need to work towards in order to secure that page-one rank.

The main benefit is objective planning for content. You can use keyword clustering to create a clear plan of action for SEO content for every single page on your website. Also, it gives an opportunity to jot down suitable content ideas for the future. It helps you for long-term scalability since you have meta tags to target over time that can be scaled indefinitely. Your team can work from one document detailing which target words live where, which content needs to be created in order to achieve a rank, and also, how that content can be repurposed for use across the marketing landscape. You can think of keyword clustering as the preparatory work that takes place before you execute SEO.

One web page can rank for multiple search terms. So why aren’t we hyper-focused on planning and optimizing content that targets dozens of similar and related query terms? Why target only one target word with one piece of content when you can target more? The impact of SEO keyword clustering to acquire more organic traffic is not only underrated, but it is also largely ignored. And accordingly, opportunities to earn more are lost.

Keywords Clustering: Methods

The clustering method is not one specific algorithm. It is a general task, the main goal of which is to group a set of objects in such a way that they are more similar to each other in the same group than to objects in other groups (clusters). The appropriate method and parameter settings (including the distance function to use, the density threshold, or the number of clusters expected) depend on the individual data set and the intended use of the results. Each clustering method includes tests and failed attempts. It is often necessary to change the data preprocessing and model parameters until the result achieves the desired properties.

There are several different methods for keyword clustering, you will learn about the most popular of them below.

Manual Clustering

Manual clustering is the oldest and easiest way to cluster words. This involves manually reviewing a list of keywords and grouping them together based on their similarities. This is time-consuming, but it allows for a more nuanced and personalized approach. For large-scale data, this method will not be rational, since machine learning can give a better result in a shorter time.

Topic modeling

Topic modeling is a natural language processing technique that helps to identify clusters of related keywords. It involves analyzing large amounts of text to identify underlying topics and grouping keywords together based on their association with these topics. Topic modeling can scan a set of data, detect word and phrase patterns within them, and automatically cluster word groups and similar expressions that best characterize all that data.

Agglomerative clustering

One of the most widely used clustering algorithms is agglomerative clustering. The main operation is to merge keywords into the most suitable clusters. The USE (Universe Sentence Encoder) model converts required data into digital values – vectors (embeddings). Then they group by the similarity of meaning. Next, keywords that are closest to each other are added to the nearest cluster. If one does not already exist the new one is created. The given data always cluster with respect to the threshold.

Further, the data divided into clusters are analyzed in relation to the set goals. If the result does not satisfy the task, the parameters of the threshold or other components are changed until the required result is achieved. There is also the technique that measures cohesion within clusters – cosine similarity. Its low complexity consists in detecting only the non-zero coordinates that need to be considered and depends only on their angle. Thanks to this, we can be sure of the similarity of keywords that are important to us.

K-means algorithm

The K-means algorithm allows you to set the number of clusters yourself. This is used when you want to find purely nearest values relative to given clusters. Each data point takes its position relative to semantic content. Then, we compute the centroid (functionally the center) of each cluster and reassign each data point to the cluster with the closest centroid. We repeat this process until the cluster assignments for each data point are no longer changing.

K-means algorithm

Each of these methods has its strengths and weaknesses, and the best approach will depend on the specific needs and goals of the project.

How It Works: Our Case

The Amazinum team had the following task: Group keywords from a set of keywords by semantic similarity.


  • At first, we used the Semrush service to get a set of keywords that are connected to some topic. 
  • Then thanks to Universal Sentence Encoder, we encoded each keyword into its corresponding numerical vector (embedding) and stored it in a data frame for convenience using pandas
  • We apply agglomerative clustering to the resulting embeddings while specifying the basic threshold from which we will start. Let it be .65 by default.
  • After the keywords are grouped, we select such a threshold that will be optimal for the customer’s goals, namely to balance the number of words in the cluster to their similarity to each other.

Tools: Python, Semrush, pandas, Universal Sentence Encoder, Agglomerative Clustering.

In our work, we also entered our indicator that shows how profitable it is to advance on a certain keyword in the reward-difficulty ratio. Under the reward was the expected modeled traffic  (= clicks throw ranks x impressions). The difficulty is a general assessment of the domain in the context of SEO optimization. The difficulty factor is calculated based on the ratings of our competitors. This showed us which keyword to optimize with the least amount of effort, leading to the best result. This works well for searching the URL whose ranking is not very high and updating its existing content (keywords). This will increase the efficiency of the page and, accordingly, positively affect its ranking, bringing maximum results.

amazinum case
  • MT – modelled traffic
  • CPC_k – cpc for chosen keyword
  • AS_t – authority score of target domain
  • AS_c – authority score of competitor domain on 1-shifting position

Semantic Core and Semantic Relevance

The semantic core is one of the main elements of the SEO strategy and the basis of any web resource. This is a set of words and their forms that correspond to the topic. They are selected in such a way as to most accurately characterize the type of site’s activity. A properly composed semantic core will help create a logical site structure, and organize a menu and internal communication between pages. Thanks to this, a high level of relevance of pages will achieve and, accordingly, the ranks will also increase.

Before you start creating content and optimizing it for search engines, you need to make a final list of keywords for the target web page. For example, there may be 20, 50, or even 100 keywords or more after you’ve completed the keyword research and clustering phase. You should select the top keywords from the cluster based on monthly search volume and reward-difficulty ratio. The keyword with the highest indicators will become your primary keyword. The other keywords will be the secondary terms you’ll target with on-page SEO. They also can be semantic relevant equivalents, because it has positive influences overall. It is important to cover the semantics by adding synonyms, and alternative keywords for both high and low-frequency queries. 

A good example of this search engine practice is HubSpot, which is a business that ranks for hundreds of thousands of keywords on Google that are related to digital marketing. The use of many different frequency keywords allows the site to stay in the first position in many topics, tangent to the main one. And thanks to good SEO, the website attracts millions of visitors per month. So, without a properly composed semantic core, site promotion will be an ineffective waste of time and money. It is because the site simply will not get good visibility in search engine results.

Semantic Core and Semantic Relevance for SEO optimization

ML in Creating Semantic Core

Many companies use machine learning (ML) to build their semantic core, which is the foundation of their content optimization strategies. And of course, the biggest of them are Google and Amazon.

Google uses machine learning to build its semantic core, which is the foundation of its search algorithm. The most important function is understanding the intent behind user queries and to match the right content to relevant users. By analyzing the words and phrases used in a query, as well as the context in which it was made, Google can provide more relevant search results. ML also helps to recognize patterns in user behavior and search queries. This affects personalized search results.

Amazon also uses ML to build a semantic core, which is the foundation of its product recommendations, search results, and other personalized features. With natural language processing (NLP) Amazon can understand the context of customer queries and match them with the most relevant products. This includes analyzing factors such as customer reviews, product descriptions, and customer questions. This helps to understand what customers are looking for and to provide personalized recommendations based on their browsing and purchase history.

These are just a few examples of companies that use ML to build their semantic core. As ML technology continues to advance, we can expect to see more and more companies leveraging it to improve their search and content optimization strategies.

Why Is It Worth Implementing?

Among a large number of advantages, it is worth identifying the most important ones:

Unlike paid marketing strategies that stop working as soon as you stop paying for ads, SEO continues to work day in and day out to bring more traffic to your website

Optimized Content

You can produce optimized content that targets every stage of the buying cycle. This strategy helps to acquire customers at every stage and keeps them for the long term. With SEO, you can publish optimized content that will have a positive influence on the buyer’s journey: the awareness stage, consideration stage, decision stage, and purchase stage will be better realized.

Increased Conversion Rate

SEO tends to increase conversion rates. If we are talking about different stages of the buyer’s journey and different types of content for each, most likely a significant part of visitors will already be familiar with the site’s activities if they have already come across your other page before. Therefore, it doesn’t take as much effort to convince people to close a sale because your products, services, or brand are top-of-mind.

User Experience

Businesses that focus on SEO often have websites with better user experiences than other competitors. That’s because good SEO-focused content aims to satisfy users while also being optimized for search engines to rank high for their target keywords. The practice of mixing good search engine optimization with people-first content and consideration of user experience is becoming more important each day for success.

Brand Recognition

Sales and conversions aside, SEO increases brand awareness. As people see your company’s brand name come up more in the search engines while looking up information, the more your business will become attributed to those topics. Therefore, search engine optimization can lead to more brand recognition and direct searches for your business online related to your industry. 

Confidence In Your Business

SEO also enhances the credibility of your business. Having a high-ranking website on Google, Yahoo, and Bing can increase the perceived value. This is why SEO is a well-worth investment for any business that’s trying to succeed on the Internet.


SEO is a great way to capture more market share for your business. Companies that use SEO are in a top priority than their competitors and have a higher rank in search engines. This puts SEO-focused brands at the top of the minds of more potential customers.

Identifying keywords is an important part of SEO strategy and means understanding how Google ranks us for them. Information about which keywords have the most requests allows us to determine where exactly we should put more effort. At the same time, of course, we conform it to global business goals. Keywords clustering makes it easy to create a semantic core, which is a fundament. At the output, we get the main keywords that we should focus on. It can help improve your overall SEO strategy for higher rankings and traffic.

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Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

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