Keyword Clustering: Turn Similar Queries Into Fewer, Better Pages
Keyword clustering groups queries that can reasonably be satisfied by the same destination. The goal is not to create more groups; it is to prevent a raw keyword list from turning into unnecessary page sprawl.
Semantic similarity is not enough
Two phrases can look similar while producing different search objectives, and two differently worded phrases can lead to almost identical result pages. Search intent and SERP evidence therefore matter alongside language similarity.
How SERP-overlap clustering works
SERP clustering compares the URLs ranking for different queries. LowFruits documents that its default method groups keywords when at least 40% of the top-10 URLs overlap with the cluster's main keyword, and that users can adjust the setting.
Analyze promising SERPs faster
If manual SERP checking is becoming the bottleneck, LowFruits can extract results in bulk and surface weak-site and targeting signals so you can focus your attention on the candidates worth reviewing.
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Treat the cluster as evidence
A cluster is a strong hint that one page may satisfy several queries, but editorial judgment still matters. Separate terms when the user needs a genuinely different outcome or when one combined page would become incoherent.
Build pages, not keyword buckets
For each approved cluster, define one primary search job, supporting subtopics, related entities, and internal links. A useful page should read naturally even if the reader never sees the keyword spreadsheet.