Keyword Clustering: Grouping by SERP Overlap

Keyword clustering by SERP overlap means grouping keywords together when Google already ranks the same URLs for both of them — not when the words look similar. If you search “best running shoes for flat feet” and “flat feet running shoe recommendations” and see three or more of the same pages in both top-10 results, Google has already decided those two queries deserve one answer. Build one page. If the URLs barely match, Google is treating them as separate questions, and splitting them into two pages will usually outrank trying to cram both into one.

This is the difference between clustering by what keywords mean to a human and clustering by what Google does with them. The second one is testable. The first one is a guess dressed up as strategy.

Why does SERP overlap beat semantic similarity for clustering?

Semantic similarity tools group “keyword clustering” with “keyword grouping” and “keyword bucketing” because the words are close. That feels right until you check the actual search results. Sometimes Google ranks blog explainers for one phrase and software product pages for the other, because the underlying intent has split even though the words haven’t. Semantic grouping misses that split every time, because it never looks at the SERP.

SERP overlap works from evidence instead of assumption. Semrush’s Keyword Manager, for example, clusters by comparing the top 10 organic results for each keyword and grouping terms whose ranking pages substantially match — not by parsing the words themselves. Google has already run the experiment for you, at scale, with real user behavior baked into the ranking. Reading that result is more reliable than guessing what a searcher meant from the phrase alone.

The catch: SERP overlap needs rank-tracking or SERP data, and it takes longer per keyword pair than eyeballing a word list. For a handful of keywords, that’s a non-issue. For 500+, you need a process — which is what the rest of this article gives you.

What do you need before you start clustering?

Three things, in this order:

• A keyword list with search volume. Pull it from Google Search Console, Ahrefs, Semrush, or a free tool — see our guide on free keyword research tools if you’re starting from zero. Aim for at least 30-50 keywords per topic area; clustering three or four keywords isn’t worth the setup time.
• Top-10 SERP data for each keyword. This is the ranking URLs Google shows right now, for your target market. Location matters — SERPs for “best running shoes” differ between Mumbai and Manchester, so pull data set to the country you’re writing for.
• A place to compare URLs. A spreadsheet works fine up to a few hundred keywords. Past that, a dedicated clustering tool saves hours of manual VLOOKUP work.

You do not need an enterprise SEO platform to do this. A ₹0 Google Sheet and 20 minutes of copy-pasting will cluster 50 keywords correctly. It just won’t scale past a few hundred without a tool.

How do you cluster keywords by SERP overlap, step by step?

1. Export your keyword list with search volume and current URL (if you already rank). Sort by topic area first, so you’re comparing keywords that plausibly belong together — don’t run overlap checks across your entire 2,000-keyword list at once, it’s wasted compute on obviously unrelated pairs.
2. Pull the top 10 ranking URLs for each keyword. Use a rank tracker, Ahrefs’ Keyword Explorer, Semrush’s Keyword Manager, or a SERP API if you’re scripting this yourself. Store the 10 URLs per keyword in one row or one column block.
3. Compare URL lists for each keyword pair. Count how many URLs appear in both top-10 sets. A simple spreadsheet formula (COUNTIF or a Python set intersection if you’re scripting) does this in seconds per pair.
4. Set your overlap threshold. Three or more shared URLs out of 10 is a common starting point for treating two keywords as the same cluster — some practitioners require four, some go as low as 30% overlap. Pick one threshold and apply it consistently across the whole list; changing it mid-project makes your clusters incomparable.
5. Group keywords that clear the threshold into a single cluster. Name the cluster after its highest-volume keyword — that’s usually your target for the page title and H1.
6. Assign one URL to each cluster. If you already have a page ranking for any keyword in the cluster, that page absorbs the whole cluster. If not, this cluster becomes a brief for one new page — not one per keyword.
7. Manually QA the edge cases. Clusters sitting right at your threshold (say, exactly 3 shared URLs on a 3-URL cutoff) deserve a human look. Open both SERPs side by side and check whether the ranking pages are actually answering the same question, or whether Google is just serving broad, tangentially related content for both.

For a version of this workflow you can run entirely in a spreadsheet with no paid tool, see how to cluster keywords in a spreadsheet without a tool. Once your clusters exist, the next job is deciding which page owns which cluster — that’s covered in our piece on keyword mapping one page to one primary intent.

How many shared URLs count as a cluster?

Thresholds vary by tool and by how strict you want to be. Here’s how the common cutoffs behave in practice:

What mistakes kill keyword clustering projects?

Most failed clustering projects share the same handful of errors:

• Clustering on word similarity alone. “Keyword clustering tools” and “keyword clustering software” look identical but can return different SERPs if one skews toward free tools and the other toward enterprise platforms. Check the overlap before assuming.
• Ignoring location in SERP data. Pulling US SERPs to cluster keywords for an India-focused site produces clusters that don’t hold up locally. Set your rank tracker to the actual target market every time.
• Using one threshold for every topic. Transactional keywords (“buy running shoes online”) tend to show tighter SERP overlap than informational ones (“how to choose running shoes”), because commercial intent narrows the field of relevant pages. A single hard threshold across both types will misclassify some of them.
• Merging clusters that are too broad to serve in one page. If a cluster’s keywords each need 300+ words of distinct explanation, cramming them onto one page dilutes all of them. Overlap tells you Google sees them as related — it doesn’t override basic judgment about page length and scope.
• Never re-checking old clusters. SERPs move. A cluster built two years ago on old ranking data can be stale now — especially in fast-moving categories like AI tools or anything Google has run a core update against recently.

What tools can automate SERP overlap clustering?

For teams past the spreadsheet stage, several tools automate the URL-overlap comparison directly: Semrush’s Keyword Manager clusters by comparing top-10 organic results across your saved keyword list, and dedicated clustering tools built specifically around SERP similarity (rather than semantic matching) exist for exactly this workflow. Ahrefs doesn’t ship a one-click clustering button, but its Keyword Explorer’s “also rank for” and SERP overlap columns give you the raw data to build the same comparison manually.

Whichever tool you use, the underlying logic should stay the same: compare actual ranking URLs, not just the words in the query. If a tool clusters keywords without showing you SERP data at all, it’s doing semantic grouping with a marketing label on it — verify before you trust the output.

Once your clusters and page mapping are settled, the next decision is which clusters to build first. Our guide on prioritizing a 2,000-keyword list into a publishing queue covers how to rank clusters by opportunity instead of working through them alphabetically.

FAQ

What is the difference between keyword clustering and keyword grouping?

They’re often used interchangeably, but “clustering” more often implies a data-driven method like SERP overlap, while “grouping” can mean anything from manual topic tagging to spreadsheet sorting by word stem. Ask what method a tool or team actually uses — the label alone doesn’t tell you.

Can I cluster keywords without a paid SEO tool?

Yes. Google Search results themselves are free to check manually, and a spreadsheet with COUNTIF formulas can compare URL lists you copy in by hand. It’s slower for large lists, but for under 100 keywords it costs nothing but time.

How often should I re-cluster my keyword list?

Every 3-6 months for volatile topics, or after any Google core update that visibly reshuffled your rankings. Stable, evergreen topics (like basic how-to content) need re-checking far less often — once a year is usually enough.

Does keyword clustering help with AI search visibility?

Yes, indirectly. A single well-clustered page covering a full topic gives AI answer engines (Google’s AI Overviews, ChatGPT search, Perplexity) one clear, comprehensive source to cite instead of several thin, competing pages. Consolidation tends to help both classic rankings and AI citation odds.

What’s a good cluster size?

There’s no fixed number — it depends on search volume and how much distinct content each keyword actually needs. A cluster of 5-15 closely related keywords feeding one 1,500-2,000 word page is a common, workable range for most topics.

Building this out for a real site — 50, 200, or 2,000 keywords at a time — is exactly the kind of project our one-time keyword clustering and mapping service handles as a single deliverable, no monthly retainer required. For the full process this article builds on, see our complete keyword research process.

Written by Palash, founder of PalV’s DM. 5+ years in SEO, 1,000+ articles published.