Cluster keywords by
what Google actually does.

Group queries by the page Google routes them to, so cannibalisation and content gaps surface on their own.

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Matt S.
“Cheaper than what I was using and far easier to get around. I had never seen keyword data laid out so visually before, the topic clusters especially, which made the whole thing easy to understand.”
Live scan · utilityseo.com
0
Grade A
Scanning 247 pages
+23 issues found
19 fixed
4 critical12 warning84 passed

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Clustering by word similarity is the wrong method

Most clustering tools group the wrong thing

Give a typical clustering tool a keyword list and it groups by lexical similarity: terms sharing words go in a bucket. It is fast, it looks tidy, and it frequently produces groups that no search engine agrees with.

"cheap running shoes" and "running shoes sale" share two words and are usually the same intent. "seo audit" and "seo audit checklist" share two words and are usually different: one wants a tool, the other wants a document. Word overlap cannot see that difference. The results page can.

Cluster by SERP overlap instead

The reliable method is to ask which URLs actually rank for each query. If two queries return substantially the same results, Google considers them the same job and one page can serve both. If they return different results, they need different pages no matter how similar the words look.

This flips the workflow. Instead of grouping keywords and hoping, you group by observed behaviour and get a defensible answer to the only question that matters: one page or two?

The problem clustering is really for

Clustering exists to prevent two things.

Cannibalisation. When several of your pages target one query, Google picks one and discounts the rest. The picked page is often not your best. The symptom is a query where the ranking URL keeps changing week to week, or several near-identical pages splitting impressions.

Orphaned intent. A cluster with real search demand and no page assigned to it is a content gap, and it is far better evidence than any keyword difficulty score.

How UtilitySEO does it

Keyword clusters are built from your Search Console data by grouping queries according to which page Google actually routes each one to. That is the SERP-overlap method applied to your own site, using data Google gives you rather than scraped estimates.

Cannibalisation detection then surfaces the pages competing for the same query without any manual grouping, which is the step most teams never get around to doing by hand.

What to do with a cluster once you have it

Pick the strongest page as the target. Consolidate the near-duplicates into it, or redirect them if they add nothing. Point internal links at the winner. Then check the cluster again in a month: if the ranking URL has stabilised, the cannibalisation is resolved.

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Keyword clustering questions

Grouping search queries that one page can rank for together. Done well it tells you how many pages to build; done badly it produces tidy-looking groups Google disagrees with.
By the results page, not by shared words. If two queries return substantially the same URLs, one page can serve both. Word similarity misses cases like 'seo audit' versus 'seo audit checklist', which look alike and want different things.
Two or more of your pages competing for the same query. Google picks one and discounts the others, often not the page you would choose. The tell is a ranking URL that changes week to week.
In Search Console, filter to one query and add the Pages dimension. If several of your URLs appear across weeks for that query, they are competing. It works but it does not scale past a handful of queries.
No, and doing so causes cannibalisation. One page should target a cluster of queries that share intent. Separate pages are only needed when the results pages genuinely differ.

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