How to do keyword clustering

Disclaimer: This content was generated using AI.

Introduction

This is a complete guide to keyword clustering. In fact, this exact strategy helped me rank a single page for over 640 different keywords. And today, I’m going to show you exactly how to use keyword clustering to drive more organic traffic. Let’s get started.

What Is Keyword Clustering?

Keyword clustering is the process of grouping related keywords based on search intent and semantic relevance. By clustering keywords, you can create content that ranks for multiple related queries instead of targeting one keyword per page.

Here’s a simple example: Imagine you have these keywords:

• how to brew coffee
• best coffee brewing methods
• french press coffee tutorial
• coffee brewing techniques
• coffee making tips

Instead of creating five separate pages, keyword clustering allows you to group them into one comprehensive, high-ranking article.

Why Keyword Clustering Is Critical for SEO

Here’s why keyword clustering is so important:

• Avoids Keyword Cannibalization Instead of competing with yourself by creating multiple pages on similar topics, clustering helps you consolidate authority into a single, powerful page.
• Aligns with Google’s Algorithm Google’s RankBrain and BERT updates understand semantic relationships between keywords. If you rank for “best protein powder,” you’re also likely to rank for “top protein powder brands” and “protein powder reviews.”
• Saves Time and Resources Instead of writing 20 separate blog posts, you can create a few authoritative guides that target multiple related queries.

How to Perform Keyword Clustering

Follow this step-by-step approach to create effective keyword clusters:

Step 1: Collect Your Keywords

Start by compiling a comprehensive list of keywords.

I recommend using Answer Socrates to generate as many relevant keywords as possible.

For instance, if you have a fitness website, you might begin with “protein powder” and discover these related keywords:

Step 2: Categorize by Search Intent

Next, analyze the intent behind each keyword.

For example, these keywords indicate commercial intent (users looking to buy or compare products):

• best protein powder
• protein powder reviews
• top protein powder brands

Whereas these keywords indicate informational intent (users looking for knowledge or guidance):

• how to use protein powder
• when to take protein powder
• protein powder benefits

Group keywords based on their intent. This helps ensure your content aligns with user expectations.

Once categorized, export the data by clicking “Download CSV.”

Step 3: Build Topic and Semantic Clusters

Now comes the real power of keyword clustering. Instead of grouping keywords solely based on matching words, focus on both topic relevance and semantic meaning.

Here’s how to do it:

Each topic cluster should include:

• A primary keyword (usually the most searched term)
• Supporting keywords that are semantically related
• A consistent search intent
• A clear topical connection

Example: Protein Powder Cluster

Primary Keyword: best protein powder

Supporting Keywords (Direct Matches):

• protein powder reviews
• top protein powder brands
• which protein powder is best
• best protein powder 2024
• protein powder comparison

Supporting Keywords (Semantic Matches):

• protein powder for muscle gain
• bodybuilding protein supplements
• best protein for building muscle

Even though these terms don’t all contain the exact phrase “best protein powder,” Google recognizes their semantic connection and ranks them together.

Pro Tip: Want more semantically related keywords? Check Google’s “People also ask” and “Related searches” sections. These provide direct insight into how Google groups related topics.

Step 4: Assign Content to Each Cluster

Finally, match each keyword cluster with a specific content piece.

Key Reminder: Don’t try to stuff every keyword from your cluster into one article.

Instead, create a comprehensive piece of content that naturally integrates relevant terms while maintaining readability and SEO strength.

AI-Powered Keyword Clustering

By Oliver Palmer, from haystack.earth.

A few months ago, I started testing AI tools to develop my own keyword clustering system in Python. Initially, I used natural language processing (NLP) libraries that grouped keywords based on frequency and similarity.

While the mathematical logic was sound, the results often missed deeper relationships between keywords. For example, it might cluster "apple pie recipe" with "apple store" just because they share the word “apple,” while failing to connect "apple pie recipe" with "best dessert recipes." Sometimes this approach was useful, but other times, a more context-aware, human-like grouping was needed.

I also experimented with ChatGPT for clustering, but it struggled with large CSVs—leading to error messages or exceeding usage limits.

My current method uses a script that processes a CSV of keyword and search volume data, divides it into smaller chunks, and feeds it into Anthropic’s API with a structured prompt. The output is an organized CSV with keyword clusters.

Here’s the prompt I use:

“You are an expert SEO analyst. Group these keywords and their monthly search volumes into clear categories based on search intent and topic.”

Rules for Clustering:

• Use clear, concise category names.
• Group by core topic and search intent.
• Keep clusters focused but not too granular.
• Maintain natural search language.
• Consider search volume trends.
• Group similar keyword modifiers together.

At first, the results were subpar—partly due to using a lower-end AI model to save a few cents per batch and partly because my prompt lacked clarity. By pasting the keyword clusters into Claude and iterating on the prompt, I significantly improved the output.

After multiple refinements, the results became far more contextual, as if an SEO expert had grouped the keywords based on meaning rather than just word similarity.

So far, I’ve had the best success using the Sonnet 3.5 model, clustering 100 keywords in under a minute at an average cost of $0.04 per batch.

If you're interested in the script, feel free to connect with me on LinkedIn. I'd also love to hear from others experimenting with AI-driven keyword clustering, especially those combining traditional NLP with LLM models.

Keyword Clustering for E-commerce SEO

By Tryggvi from nordicaseo.com, an E-commerce SEO Expert.

Keyword clustering plays a fundamental role in my e-commerce SEO strategy. It’s the most efficient way to group keywords correctly, allowing for better content planning and intent organization.

With the right clustering tool, you can:

• Create topical maps to structure site content.
• Develop long-term content plans that align with user intent.
• Identify overlapping content opportunities to improve rankings.

Another major advantage? You can quickly pinpoint the primary and secondary keywords for each page, optimizing content with a clear keyword hierarchy.

This approach allows for strategic content structuring, including:

• Well-defined heading structures.
• Strong internal linking strategies.
• A big-picture view of your site’s content plan—both for the present and future.

By implementing keyword clustering, scaling your site’s authority in new categories becomes much more manageable. You can confidently optimize pages with the right keywords while ensuring you answer user queries in the most relevant way.

Advanced Keyword Clustering Strategies

Looking to refine your keyword clustering process? Here are some advanced techniques to improve accuracy and effectiveness.

Leverage Natural Language Processing (NLP)

Using tools like IBM Watson or Google’s Natural Language API can help uncover deeper semantic connections between keywords, ensuring clusters align with true user intent.

Analyze Competitor Content

Examine top-ranking pages that appear for multiple keywords in your cluster. Identify common topics, structural patterns, and content strategies that contribute to their rankings.

Build Topic Hierarchies

Structuring keyword clusters into hierarchical topics improves site architecture and internal linking, helping search engines better understand your content.

Example:

• Main Topic: Protein Supplements
• Subtopic: Types of Protein Powder
• Cluster: Whey Protein Powder
• Supporting Keywords: Benefits, Dosage, Best Timing

Common Keyword Clustering Mistakes

Even experienced SEOs make these mistakes. Here’s how to avoid them:

Mistake #1: Grouping Unrelated Keywords

Just because two keywords share a term doesn’t mean they belong together.

Example:

• Protein powder for weight loss
• Protein powder for muscle gain

While both mention “protein powder,” they serve different intents and require separate content strategies.

Mistake #2: Ignoring Search Intent

Mixing keywords with different intents leads to weak content that doesn’t fully satisfy search queries.

Example of poor clustering:

• Buy protein powder (transactional)
• What is protein powder (informational)

Each of these requires a distinct approach—one for an e-commerce page and one for an informational guide.

Mistake #3: Overloading Clusters

If a cluster contains more than 15-20 tightly related keywords, it may be too broad. Large clusters should be divided into smaller, more focused groups for better targeting.

Case Study: Boosting Organic Traffic with Keyword Clustering

One of my clients saw a 167% increase in organic traffic after implementing keyword clustering.

Before:

• 12 separate blog posts on protein powder
• Each targeting only 1-2 keywords
• Keyword cannibalization
• Mediocre rankings across all terms

After:

• Consolidated into 4 in-depth guides
• Each aligned with a keyword cluster
• Improved topical authority
• Rankings increased for all primary keywords

Final Thoughts

Keyword clustering is a powerful tool for organizing content and improving rankings.

Now, I’d love to hear from you:

Have you tried keyword clustering? Do you have questions about refining your strategy?

Drop a comment below and let’s discuss.