How to Cluster Keywords

Definition

Keyword clustering is the process of grouping keywords with similar search intent and meaning to design a content structure where one page covers multiple variant keywords simultaneously. Instead of creating a page for each individual keyword, integrating groups of highly related keywords into one page concentrates topical authority.

The output is largely divided into two tiers: a Pillar page that comprehensively covers a broad topic, and cluster pages that address its subtopics. Internal links connect these two tiers, and search engines assess a site's expertise from this structure.

Why Clustering Is Needed

The Keyword Cannibalization Problem

What happens if you create separate pages for "what is keyword clustering," "keyword clustering methods," and "keyword clustering tools"? All three pages target similar keywords, and search engines become confused about which page to prioritize. The result is keyword cannibalization—where all three pages remain at low rankings.

Topical Authority: Dispersed vs. Concentrated

Conversely, integrating the three keywords into one structured page concentrates backlinks and user signals on a single URL. Both search engines and AI answer engines rate pages that cover a topic deeply. Clustering is a strategy to consolidate dispersed authority into one.

5 Steps of Keyword Clustering

Step 1: Define Seed Keywords

Determine the core keyword that will anchor the cluster. This is usually a 2–3 word keyword directly tied to the business's core topic. For a SaaS marketing tool, keywords like "AEO," "AI search optimization," and "content strategy" become seeds.

Step 2: Keyword Expansion (Using Tools)

Collect related keywords as broadly as possible around the seed keyword. Tools you can use:

• Ahrefs Keywords Explorer: related keywords, question keywords, autocomplete suggestions
• Semrush Keyword Magic Tool: extensive variant keywords and seed-based expansion
• Google Search Console: check actual search queries already generating impressions
• Google Autocomplete / People Also Ask: collect real user queries for free

Step 3: Classification by Search Intent

Sort collected keywords into informational, navigational, commercial, and transactional (see search intent classification). Keywords with different intents cannot be grouped on the same page no matter how similar they seem. Clusters only work when intent matches.

Step 4: Semantic Clustering

Among keywords with the same intent, check whether they actually appear on the same search results page (SERP). Keywords with high SERP overlap belong in the same cluster.

How to analyze SERP overlap:

• Compare Google first-page results for two keywords—many overlapping URLs mean the same cluster
• Use SERP Overlap features in Ahrefs or Semrush
• For manual verification without tools, compare the top 10 URLs for each keyword

Step 5: Cluster → Page Mapping

Once clusters are complete, determine the page type for each cluster. Large clusters (high search volume, diverse subtopics) become Pillar pages; small clusters (specific subtopics) become cluster pages. If a page already exists, strengthen content to cover additional cluster keywords; if not, plan new production.

Korean Clustering Example

Example: B2B SaaS marketing automation tool

Cluster A (Informational – Pillar)

• Seed: "marketing automation"
• Included keywords: "what is marketing automation", "marketing automation methods", "how to implement marketing automation", "B2B marketing automation"

Cluster B (Commercial – Cluster page)

• Seed: "marketing automation tool recommendations"
• Included keywords: "marketing automation solution comparison", "marketing automation tool rankings", "HubSpot vs Salesforce comparison"

Cluster C (Informational – Cluster page)

• Seed: "email marketing automation"
• Included keywords: "how to set up email automation", "creating email workflows", "automated newsletter sending"

Cluster A becomes the Pillar; B and C become cluster pages, all connected via internal links.

Relationship with CEP

CEP (Category Entry Point) is a cue for the purchase situation in which a consumer thinks of a specific category. While keyword clustering addresses the technical question of "which keywords should be grouped on the same page," CEP mapping addresses the strategic question of "which situations and contexts trigger category entry."

The two tasks are complementary. Converting purchase situations discovered through CEP mapping into keywords enriches seed keywords and intent classification for clustering. Conversely, search patterns discovered during clustering can be added to the CEP map.

Changes in the AEO Era

Traditional clustering grouped 2–4 word keywords. In the AEO era, natural language questions (prompt keywords) must also be included in clusters.

For example, the "email marketing automation" cluster adds the following prompt keywords alongside existing keywords:

• "Tell me how to set up email automation for the first time at a startup"
• "How to send newsletters differently based on subscriber behavior"
• "Which is better for small teams, Mailchimp or Brevo?"

Covering these prompt keywords in FAQ or answer blocks on cluster pages increases the likelihood of AI answer citations.

Application in the Korean Market

Automatic Korean clustering tools have accuracy limitations. Most SEO tools are trained on English data, so features that automatically analyze SERP overlap for Korean keywords often have lower accuracy. For important clusters, manual verification by checking Korean SERPs directly is necessary.

Also consider that Naver and Google may have different cluster structures. On Google, one Pillar page covering multiple keywords works well, but Naver tends to surface separate content for each individual keyword, which may require a different strategy.

Frequently Asked Questions

What is an appropriate cluster size?
There is no fixed standard, but in practice 5–20 keywords per cluster is common. Too many make a single page difficult to manage; too few reduce the benefits of clustering.

What should I do if multiple existing pages need clustering?
The principle is to consolidate cannibalizing pages into one (via canonical tags or 301 redirects) and merge content. However, analyze traffic and backlinks for each page first to identify the core URL before consolidating to preserve existing authority.

Is the cluster structure still effective in AI answer engines?
Yes. AI answer engines prefer sites with broad and deep topic coverage. A structure where Pillar pages provide broad context and cluster pages answer detailed questions also works favorably for AI citations.

How often should clustering work be done?
After initial structure design, a quarterly review is recommended. This involves deciding whether to add newly emerging keywords to existing clusters or create new clusters. For fast-changing industries, monthly review is appropriate.

Related Sources

• Ahrefs Blog. Keyword Clustering: What It Is & How to Do It. — Practical clustering methodology guide
• Semrush. How to Use Keyword Clustering to Optimize Your SEO Strategy. — Keyword Gap, Topic Research tool usage
• HubSpot. Topic Clusters: The Next Evolution in SEO. — Reference for Pillar-Cluster concept foundation