Keyword clustering

Keyword clustering is the practice of grouping related queries so each cluster becomes one page, not many. Done well, it prevents keyword cannibalization and produces content that ranks for dozens of queries at once.

The problem clustering solves

Suppose you have these keywords:

• "best insurance CRM"
• "top insurance CRM software"
• "insurance agency CRM"
• "CRM for insurance brokers"
• "insurance CRM comparison"

The naive approach: 5 pages, one per keyword. This produces duplicate-ish content, competes with yourself (cannibalization), and dilutes backlinks across many pages instead of one strong one.

The right approach: 1 page targeting all 5 queries. That's a cluster.

How to cluster

Manual clustering

For small lists (<200 keywords): spreadsheet, sort by topic, group by hand. Slow but you develop an instinct for it.

SERP-similarity clustering (the right way)

Queries belong in the same cluster if Google returns similar top-10 results for them. Tools (Keyword Insights, SE Ranking, SurferSEO, Clusterai) automate this: they pull the top 10 for each query and compare overlap. Queries with >3 URLs in common usually belong in the same cluster.

Semantic clustering

Using embeddings or NLP to cluster by meaning rather than SERP overlap. Faster but less accurate, two queries can be semantically similar yet have completely different SERPs (different intent).

The SERP-similarity rule of thumb

• 3+ URLs overlap in top 10 → same cluster
• 0-2 overlap → different cluster, different pages

Cluster types

• Primary-plus-secondary, one lead keyword per cluster, supporting variations included naturally in content
• Pillar + clusters, broad pillar page links to narrower cluster pages, each cluster page handles long-tail variations
• Product family, product category page + one page per SKU/variant

When to split a cluster into two pages

• Intent is different (informational vs commercial)
• SERPs diverge significantly
• The page would be over-long (>4000 words)
• Different audiences would land on the page

Output

A clustered keyword list looks like:

• Cluster name: "Insurance CRM comparison"
• Primary keyword: "best insurance CRM" (800/mo, KD 35)
• Supporting keywords: 12 related queries totaling 2,400/mo
• Target page URL: /blog/best-insurance-crm
• Intent: commercial investigation
• Format: listicle / comparison