After years of working on client sites and building out content strategies at Proworks SEO, I’ve learned that keyword clustering is one of those things that sounds simple in theory but gets messy fast in practice. Everyone talks about it, but few people have a repeatable system that actually works.

The problem with most keyword research is that we approach it backwards. We start with a keyword tool, export thousands of variations, and then try to figure out what Google actually wants. But here’s the thing: Google has already told us exactly what it wants. We just need to look at what’s already ranking.

That’s what this workflow is about. It’s not theoretical. It’s the exact process I use to reverse engineer Google’s understanding of a topic, extract the keywords that matter, and cluster them in a way that leads to pages that rank for dozens or even hundreds of related queries.

The Tools That Make This Possible

Before I walk through the process, let me explain why each tool matters.

SERP Location Changer: This is critical because search results are not universal. If you’re optimizing for a UK audience but pulling data from US results, you’re building content for the wrong SERPs. Intent shifts dramatically by location, and so do the pages that rank. I use this to view Google search results exactly as they appear in the target market.

Ahrefs Traffic Checker: Instead of brainstorming keyword variations or relying on suggested keywords from a tool, I use Ahrefs to extract the actual organic keywords that real ranking pages are getting traffic from. This gives me proven, Google-validated queries rather than guesses.

ContentGecko: This is where the magic happens. ContentGecko clusters keywords based on SERP overlap, which means it groups keywords that Google considers similar enough to serve with the same or nearly identical pages. This is the only clustering method that matters because it reflects how Google actually thinks about query relationships.

Starting With a Single Seed Keyword

Every clustering project starts with one seed keyword. This is usually a commercial or informational query that represents the core topic I want to dominate. At this stage, I’m not trying to find variations yet. I’m trying to identify which pages Google already trusts for this topic.

For example, if I’m working on a project in the project management software space, my seed keyword might be “best project management tools” or “project management software for small teams.” The goal here is to understand what Google considers authoritative for this query.

Analyzing the SERPs by Location

Once I have my seed keyword, I search it using a SERP location changer set to the exact country or city I’m targeting. This is not optional. A keyword searched in the US will often return completely different results than the same keyword searched in the UK, Australia, or Kenya. Different markets have different buying behaviors, content preferences, and even different dominant brands.

I focus exclusively on the first page of results. From the top 10 organic listings, I ignore the ads, and I usually ignore featured snippets unless they link to a full content page. What I’m looking for are standard organic results that represent complete, authoritative content pages. Each of these URLs becomes a data source in the next step.

Extracting Keywords From Every Ranking Page

Here’s where most people stop too early. They might look at the top one or two results, but I go through all ten. For each URL in the top 10, I paste it into Ahrefs Traffic Checker and extract every keyword that page ranks for organically.

I’m specifically looking for keywords with measurable traffic, keywords that are topically relevant to my seed keyword, and keywords ranking in reasonable positions (typically positions 1 to 50). Then I copy all of these keywords into a spreadsheet.

At first, the spreadsheet looks chaotic. There are duplicates, overlapping terms, and keywords pulled from multiple competing pages. But that chaos is actually valuable data. If multiple top-ranking pages rank for the same keyword, that’s a strong signal that the keyword is core to the topic. If a keyword appears across several URLs, it’s a strong candidate for clustering.

The Power of Repetition

The real value of this workflow comes from repetition. By extracting keywords from all ten top-ranking results, I’m effectively reverse engineering Google’s mental model of the topic. I’m seeing which keywords Google consistently associates with this subject, which variations it considers interchangeable, and which angles it thinks deserve coverage.

This is far more reliable than any keyword suggestion tool because it’s based on actual ranking data, not algorithmic predictions.

Cleaning and Preparing the Data

Once I have keywords from all ten URLs in my spreadsheet, I do a light cleaning pass. I remove obvious branded terms (like company names that aren’t relevant to my content), navigational queries (like “login” or “pricing page” for competitor sites), and anything clearly off-topic. But I keep everything that has informational or commercial intent.

Then I copy the cleaned keyword list and paste it into ContentGecko to begin clustering.

Setting Up ContentGecko for Accurate Clustering

ContentGecko has several settings, but three are critical for getting useful results.

Minimum SERP Overlap: I always set this to 3. This means a keyword must appear in at least three similar SERPs to be grouped into the same cluster. Setting it lower creates clusters that are too broad and lack focus. Setting it higher creates too many tiny clusters that fragment your content strategy. Three is the sweet spot.

Target Location: I set this to match exactly the same location I used in the SERP location changer earlier. Consistency across all data sources is essential for accurate clustering.

Email Delivery: I enter my email address so ContentGecko can send me the completed cluster output when it’s ready.

Once configured, I run the clustering process and wait for the results.

Turning Clusters Into Content Plans

After ContentGecko finishes, I download the results and start analyzing. This is where raw data becomes an actionable content strategy. But not all clusters are ready to use immediately. I apply strict quality rules before approving any cluster for content creation.

The cluster title must be the first keyword: The title of each cluster should be the first keyword listed. This keyword usually represents the primary search intent, the most authoritative query in the group, and the best candidate for the page’s main title and URL slug. If the cluster title doesn’t clearly represent the group, I know the cluster needs restructuring.

Keywords must be sorted by traffic: Within each cluster, I make sure keywords are sorted in descending order by search volume. This helps me prioritize which keywords get featured in H2 subheadings, which ones get their own sections, and how to allocate word count across the page. Higher traffic keywords get more prominence.

One clear search intent per cluster: Each cluster should satisfy one dominant search intent. If I see informational and transactional keywords mixed in a way that creates internal conflict (like “how to choose project management software” mixed with “buy project management software cheap”), I split the cluster. Trying to serve multiple conflicting intents on one page dilutes the content and hurts ranking stability.

Building the Content Brief

Once a cluster passes all my quality rules, I analyze what the top-ranking pages include, which keywords map naturally to H2 or H3 headings, and which keywords can be addressed contextually within paragraphs.

The final output is a content brief that includes the primary keyword, supporting keywords sorted by priority, a suggested heading structure, and internal linking opportunities. This brief then goes to a writer or gets used to optimize an existing page.

Why This Workflow Actually Works

Most keyword research relies on assumptions about what Google wants. This approach flips that. It starts with what Google has already proven it wants by looking at what it’s already ranking.

SERPs define relevance. Ahrefs reveals keyword relationships that actually exist in the wild. ContentGecko validates intent overlap based on real SERP data, not algorithmic guesses.

Instead of chasing individual keywords and hoping they rank, this system builds pages that align with how Google naturally groups queries. That’s why pages built this way tend to rank for 50, 100, or even 200+ related keywords instead of just one or two.

The Real Goal of Keyword Clustering

Keyword clustering isn’t about volume for the sake of volume. It’s about structure, intent alignment, and building content that matches how search engines understand topics. When you get clustering right, you stop creating dozens of thin pages targeting similar keywords and start creating fewer, stronger pages that dominate entire topic clusters.

This is the methodology I use at Proworks SEO to build topical authority and drive long-term organic growth. It’s repeatable, it’s scalable, and most importantly, it works because it’s grounded in real ranking data rather than assumptions about what might work.