Keyword Clustering
Keyword clustering groups the terms Google answers with the same results, so one strong page covers each topic and the clusters become hub and spoke sections.
If Google answers two searches with the same pages, you need one page, not two.
Try a quick test. Search “furnace short cycling” and then “furnace keeps turning on and off.” Compare the URLs on each first page. If most of them match, Google has told you something useful: as far as it is concerned, those two searches are one topic. A separate page for each would split your effort across two pages fighting for one slot. Run that comparison across a whole keyword list and you are doing keyword clustering.
The method is called SERP clustering, and it beats grouping by shared words. “Roof repair cost” and “roof replacement cost” share two of their three words and can still need separate pages, if Google shows different results for each. Two phrasings with no words in common can belong on one page if the results match. Semrush's Keyword Strategy Builder works on this principle, comparing the top 10 organic results for each keyword and grouping terms that return similar URLs. Ahrefs takes a related route with its Parent Topic: the query that sends the most traffic to the page ranking first for a keyword.
How much overlap counts is a judgment call. We start strict, around three or four shared results out of ten, and loosen it only when a person reading both result pages would agree they answer the same question. Loose clustering produces bloated pages that try to rank for everything. Over-strict clustering produces a pile of near-duplicates that compete with each other.
Clusters are the building blocks of site sections. A broad cluster (everything about AC repair) becomes the hub page. The narrower clusters under it (not cooling, frozen coil, leaking water, strange noises) become spokes, each answering one problem fully and linking back up. That structure is what our work on topic clusters builds, and it is how a set of narrow pages becomes a section with a clear center instead of a scatter of posts.
Clustering and keyword mapping get confused because they happen back to back. Clustering answers “which terms belong together?” Mapping answers “which URL carries each group?” Cluster first and the map nearly writes itself. Map first and you can end up assigning six terms to six pages that Google treats as one topic, which is keyword cannibalization built in on purpose. Clusters also expose places where search intent splits a topic that looked unified.
Clustering sits in the middle of keyword research: after the list is gathered and trimmed, before anything is written. We run it on your list, review every cluster by eye, and hand back a section plan as part of our monthly SEO programs, which start at $625 a month. Our content marketing work then writes to that plan instead of to a hunch.
What the work is
From a long list to a short set of pages
Compare results, not words
Terms are grouped by how many of the same URLs rank for both. Shared results mean Google sees one topic, whatever the wording.
Set the overlap bar
We start strict and loosen only when two result pages genuinely answer the same question. The threshold is a judgment call, so a person makes it.
Hubs and spokes fall out
Broad groups become hub pages, and the narrower groups beneath them become the specific pages that link back up. The cluster list doubles as a section outline.
Every cluster reviewed by eye
Tools miss context, like two terms that share results but carry different intent. Each group gets a human review before it becomes a page.
Merges show up too
When two existing pages land in one cluster, that is a merge candidate. Clustering finds consolidation work as well as new pages to write.
Re-cluster when results shift
Search results change, and clusters drift with them. Priority clusters get rechecked so the structure keeps matching what Google shows.
Want your keyword list turned into a site plan?
Send us the list, or let us build one. We will group it by shared results and show you which pages it adds up to.
Three ways to read it
Why grouping by results pays off
Fewer, stronger pages
One complete page per topic collects the links and internal anchors that six thin ones would split between them. Clustering shows where the topic boundaries actually sit.
Topics, not strings
AI assistants summarize topics, and a page that covers a whole cluster gives them one complete source to draw from. Scattered half-answers are harder to cite.
The next question, answered
A homeowner with a frozen AC coil also wants to know whether it is safe to run and what the repair costs. A cluster page answers all three without sending them back to Google.
How we cluster a keyword list
1. Pull results for every term
Each keyword's current top ten organic results are collected from the area your customers search in. That snapshot is the raw material.
2. Group by overlap
Terms that share enough ranking URLs fall into the same cluster. Terms that share none stand alone, which is useful information too.
3. Review and name each cluster
A person checks each group for intent splits and odd matches, then names it the way a customer would. That name usually becomes the page's primary keyword.
4. Arrange hubs and spokes
Broad clusters become hub pages, narrow ones become spokes, and existing pages are slotted in where they fit. The result feeds straight into the keyword map.
Turn a pile of keywords into pages that make sense
Tell us where things stand and we will show you how your terms group.
Which of these sounds like you? (pick any)
Questions
Frequently asked
Grouping keywords that one page can serve. The dependable way is to compare the search results for each term: when two terms return many of the same URLs, Google is treating them as one topic, and one page should target both. Clustering by shared words alone is faster and far less reliable.
Clustering by search results rather than by word similarity. Two terms go together when enough of the same pages rank for both. It catches pairs that look alike but are not, and pairs that look nothing alike but are. The related vocabulary a page in each cluster should then cover is handled under semantic keywords.
There is no official number. We start at three or four of the top ten and adjust per topic: local service terms often need a stricter bar, broad informational terms a looser one. The deciding test is whether one page could honestly be the best answer to both searches.
Clustering groups the terms; mapping assigns each group to a URL. They run in that order, and the clustered list is the input to the map. How many pages a site ends up needing comes out of the two together, a question our guide on how many pages you need for SEO approaches from the other direction.
Often more than with new content. Old posts that land in the same cluster are candidates to merge into one stronger page with redirects, which is a common step in SEO visibility recovery. The merged page then gets the links that used to be spread thin, which is where internal linking comes in.
As of October 2026, Semrush's Keyword Strategy Builder clusters by comparing top results, and Ahrefs' Keywords Explorer can group a report by Parent Topic on its Standard plan and above. Both are good accelerators, and both still need a person to check the groups. Competitor terms worth adding to the list before clustering come from competitor keyword analysis.
Yes. Assistants answer questions that span a topic, and a page covering a whole cluster gives them one complete source rather than fragments. The specific questions inside each cluster are also where long-tail SEO pays off. The free AI visibility check shows whether assistants mention you now.
It is included in our monthly SEO programs, not billed on its own. Programs start at $625 a month, most local businesses land between $625 and $2,250, and there is no long contract. SEO financing is available.
Still have a question about Keyword Clustering? Ask us directly — we answer straight.
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