Keyword clustering is the process of grouping a keyword list into sets that one page can rank for. The most common method compares search results: if two keywords show many of the same URLs in the top results, Google is treating them as the same need, and one page should cover both. Clustering turns a long spreadsheet of phrases into a manageable list of pages, and it is one of the best defenses against creating duplicate or competing content.
Methods
SERP-based clustering pulls the top results for every keyword and groups those that share a threshold of URLs, for example three or more of the top ten. It reflects how Google currently interprets the searches, so it handles synonyms and odd phrasings well. Semantic clustering groups keywords by meaning using text similarity or embeddings. It is cheaper and works for keywords with no results data, but it can group phrases that look similar and have different intent.
Many teams use semantic grouping as a first pass and SERP overlap to confirm the clusters that matter.
- SERP overlap: shared ranking URLs, reflects real intent
- Semantic similarity: shared meaning, cheaper, less precise
- Manual review: still needed for edge cases and high-value terms
Choosing a threshold
A strict threshold produces many small clusters and more pages; a loose one produces fewer, broader pages. There is no universal right setting. Pick one, review a sample of clusters by hand, and adjust. Pay particular attention to clusters that mix page types, such as a product page result and a how-to guide result, which usually signals mixed intent.
From clusters to a plan
Each cluster gets one primary keyword, a page type based on what ranks, and a combined demand estimate. Clusters then feed the keyword map and the content calendar. Related clusters often form a topic cluster, with a pillar page linking out to each supporting page.
Example
Collapsing a list into pages
Imagine Ferncastle, a fictional email deliverability tool, starts with eight hundred keywords. SERP clustering shows that “email warmup”, “warm up email domain” and “how to warm up an email account” share most of their top results, so they become one guide.
“Email warmup tool” returns vendor pages and review lists instead, so it becomes a separate commercial page. The eight hundred keywords end up as roughly ninety page targets in this illustrative case.
Common mistakes
Clustering by words alone
Keywords that share words can have different intents. “Email warmup” and “email warmup tool” look alike and are served by different page types.
Never re-clustering
Results pages change over time. Clusters built two years ago may no longer reflect how Google groups the searches.
Questions
For large lists, a tool that pulls results data saves a lot of time. For a few dozen keywords, comparing results pages by hand works fine.
Clustering decides which keywords belong together. Mapping assigns each cluster to a specific URL on your site.
