Keyword Clustering Tool
Paste a list of keywords and group them into topic clusters by shared theme word, so you can plan one page per cluster instead of one thin page per keyword.
A long keyword list is not a content plan. Dozens of close variants want to live on the same page, and scattering them across thin, near-duplicate posts splits your relevance and competes with yourself. This free keyword clustering tool takes a pasted list, normalizes it, drops stop words and groups the keywords that share a significant word, so "best running shoes" and "cheap running shoes" land together under one theme. It is built for SEOs and content planners who want to turn a raw keyword dump into a clear map of pages to write.
How to use it
Paste your keyword list
Drop your keywords into the box, one per line, straight from a keyword tool, Search Console or a spreadsheet. The clusters build instantly as you paste.
Read the clusters
Each cluster is labelled with the theme word it shares and a count of its members, and anything with no shared theme falls into an ungrouped bucket for you to review.
Plan one page per cluster
Use each cluster as a single page targeting the whole group, then copy a cluster out with one click to drop it into your brief or content calendar.
Why it's useful
From a dump to a plan
The tool turns a flat list into grouped topics in one step, so you immediately see which keywords belong on the same page and how many real topics your list actually contains, instead of reading hundreds of rows by hand.
Accent and stop-word aware
Keywords are normalized before grouping: lowercased, stripped of accents and punctuation, with common stop words and generic modifiers like "best" and "cheap" removed, so clusters form around the real theme word rather than the filler.
Copy a cluster in one click
Each cluster has a copy button that lifts its keywords out as a clean list, ready to paste into a content brief, a spreadsheet or a page outline. Nothing is uploaded; it all runs in your browser.
Why clustering keywords beats one page per keyword
One page per topic, not per keyword
Search engines rank pages on topics, not on exact strings. "best running shoes", "cheap running shoes" and "running shoes for flat feet" are the same topic seen from three angles, and a single strong page that covers all three will almost always out-rank three thin pages that each chase one. Splitting close variants across separate posts creates keyword cannibalisation, where your own pages compete for the same results and none of them wins cleanly.
Clustering fixes the planning problem at the root: instead of a page per keyword, you get a page per cluster. This tool groups each keyword under the most frequently shared significant word in your list, so the natural topics surface on their own and you can see at a glance how many pages your list really implies.
How this tool groups, and where it stops
The clustering here is lexical. The tool normalizes every keyword, drops stop words, counts how often each remaining word appears across the whole list, and assigns each keyword to the cluster of the shared word it has in common with the most others. It is fast, transparent and needs no API, which makes it ideal for a first pass on a few dozen or a few hundred keywords.
What it does not do is read search intent. Two keywords can share a word yet want different pages, and two with no word in common can serve the same intent, which is why there is an ungrouped bucket and why the groups are a starting point, not a verdict. Review each cluster, merge the ones that share an intent, and split any that have drifted, using the SERP itself as the tie-breaker when you are unsure.
Check the SERP when in doubt
The surest test of whether two keywords belong on one page is Google's own results: search both and see if the same pages rank for each. If the top results overlap heavily, one page can serve both; if they are different, they are separate intents and deserve separate pages, whatever words they share.
From clusters to published pages
A clean set of clusters is the skeleton of a content plan: each cluster becomes a page, its theme word points at the angle, and the member keywords map the subtopics and headings to cover. The slow part is everything after the plan, writing each page well, publishing it and keeping it fresh, in every language your audience searches in. That is the work Otorank takes off your plate: it researches, writes, publishes and tracks content built to get found across 150+ languages, so a clustered list becomes live pages rather than a spreadsheet that ages.
They use Otorank
Questions
How the clustering works, why intent still needs a human eye, and how to turn clusters into pages.
It normalizes each keyword, removes stop words, counts how often each remaining word appears across your list, and groups each keyword under the shared word it has in common with the most others. Keywords with no shared word go to an ungrouped bucket.
No. This is lexical clustering by shared word, which is fast and transparent but does not read intent. Use it for a first pass, then review and merge or split groups by meaning, checking the SERP when you are unsure.
A keyword is ungrouped when it shares no significant word with at least one other keyword in the list. That often means it is a genuine singleton topic, or that you need more related keywords for its theme to form.
It runs in your browser with no limit imposed, and handles a few hundred keywords comfortably. Very large lists will simply take a moment more to render.
No. Everything is processed in your browser; nothing is uploaded or stored, so you can cluster confidential or client keyword lists safely.