Keyword Clustering
Group a keyword list by the words the phrases share, turning a long export into a page plan.
About this tool
Takes a flat keyword list and groups the phrases that share a head term, so a few thousand rows become a few dozen topics you can assign to pages.
**This clusters on words, not on search results.** The clustering most SEO tools do compares which URLs rank for each keyword, and two phrases share a cluster when the results overlap. That needs live search data, which this platform does not have — so this groups by shared stems instead.
The difference shows up on words with more than one meaning. Lexical clustering will put "apple pie" and "apple watch" together; SERP clustering never would. It is still the fastest way to get from an export to a plan, as long as you know which question it is answering.
Plurals and common endings are stripped before grouping, so "seo service" and "seo services" land together.
Common questions
Is this SERP-based clustering?
No, and the distinction matters. SERP clustering groups keywords whose search results overlap and needs live ranking data. This groups keywords that share words. It is faster and free, and it is wrong about ambiguous terms in a way SERP clustering is not.
How do I turn clusters into pages?
One cluster is usually one page, targeting the shortest phrase in it, with the longer variants covered as sections. Splitting a cluster across several pages is what causes them to compete with each other.
Why is a keyword in a cluster it does not belong in?
Because it shares its most significant word with the rest. This is the known weakness of clustering on text: the tool sees the word, not the meaning. Move it by hand.
What does the minimum cluster size do?
Keywords whose group is smaller than the minimum are collected as unclustered rather than shown as dozens of one-item groups. Those are usually the terms that need their own page or do not belong in the list.