If you've been doing SEO for any amount of time, you've probably experienced this: you find 200 keywords that seem relevant, build a spreadsheet, and then stare at it wondering which ones deserve their own page and which ones can share one.

That's the keyword clustering problem. And most people solve it badly — either manually (painful) or by ignoring it entirely (worse).

What is keyword clustering?

Keyword clustering is the process of grouping related search terms that share the same search intent so you can target them with a single page instead of creating separate content for each one.

For example, these keywords probably belong in the same cluster:

"keyword clustering"
"keyword grouping SEO"
"how to group keywords for SEO"
"keyword clustering strategy"

They all mean roughly the same thing. A searcher typing any of these wants the same answer. So one well-optimized page can rank for all of them.

On the flip side, "keyword clustering tool" might deserve its own page — because the intent shifts from "explain this concept" to "show me software that does this."

Why it matters

Without clustering, you end up with two common problems:

Keyword cannibalization — Multiple pages on your site compete for the same terms. Google gets confused about which to rank, and often picks neither.
Wasted effort — You write 10 blog posts when 4 would have covered the same ground with better internal linking and topical depth.

Clustering fixes both. You produce fewer, stronger pages. Each page targets a tight group of related terms. Your internal linking makes sense. Google sees topical authority instead of scattered content.

The traditional approach (and why it's painful)

Here's how keyword clustering has worked for most SEOs:

Step 1: Pull a massive keyword list. Export from Ahrefs, SEMrush, Google Search Console — wherever. You end up with hundreds or thousands of terms.

Step 2: Check SERP overlap. For each pair of keywords, check if the top 10 Google results overlap. If 3+ of the same URLs rank for both terms, they belong in the same cluster.

Step 3: Group manually. Drag keywords into spreadsheet groups. Maybe use a tool that automates the SERP comparison, but it still takes time — some tools need 50+ minutes to cluster a few thousand keywords.

Step 4: Map clusters to content. Decide which cluster becomes a pillar page, which become supporting articles, and how they link together.

This process works. It's also brutally slow. For a serious content strategy with 1,000+ keywords, you're looking at hours of work — and you'll need to redo it every quarter as search intent evolves.

How AI changes keyword clustering

AI-powered clustering doesn't just speed up the SERP overlap check. It fundamentally changes how grouping works by understanding semantic relationships between keywords, not just URL overlap.

What AI gets right

Intent detection beyond URL overlap. SERP overlap is a proxy for intent. AI can detect intent directly. It understands that "best moka pot coffee" and "how to make coffee in a moka pot" are related but have different intents — even if their SERPs happen to overlap.

Semantic connections humans miss. When you're staring at a spreadsheet of 500 keywords, you'll miss connections. AI won't. It can identify that "content gap analysis" and "finding topics competitors rank for" belong in the same cluster, even though they share zero words.

Scale without compromise. The real win isn't just speed — it's that AI clustering scales linearly while maintaining quality. Clustering 100 keywords or 10,000 keywords takes roughly the same amount of human effort.

Dynamic re-clustering. Search intent shifts over time. A keyword that was informational last year might be transactional now. AI can re-cluster your entire keyword universe periodically to catch these shifts.

Building a keyword clustering workflow in 2026

Here's a practical workflow that leverages AI without overcomplicating things:

1. Start with seed keywords

Don't boil the ocean. Pick 5-10 seed topics for your niche. If you're in SEO software, that might be: "keyword research," "backlink analysis," "rank tracking," "technical SEO audit," "content optimization."

2. Expand with AI-assisted research

Use an AI keyword research tool to expand each seed into 50-200 related terms. Look for:

Long-tail variations ("how to do keyword research for free")
Question keywords ("what is the best keyword research method")
Comparison keywords ("ahrefs vs semrush keyword research")

3. Cluster by intent and semantics

Feed your expanded list into an AI clustering tool. The best ones group by:

Primary intent (informational, transactional, navigational, commercial investigation)
Semantic similarity (meaning overlap, not just word overlap)
SERP landscape (what's actually ranking, as a validation layer)

4. Map to content types

Each cluster maps to a content type:

Pillar clusters (high volume, broad intent) → comprehensive guides
Supporting clusters (medium volume, specific intent) → focused blog posts
Commercial clusters (buyer intent) → landing pages, comparisons
Question clusters → FAQ sections or dedicated answer posts

5. Build your hub-and-spoke structure

Connect supporting content to pillar pages with internal links. This is where topical authority compounds — Google sees an interconnected web of related content, not isolated pages.

6. Monitor and re-cluster quarterly

Set a reminder to re-run your clustering every 90 days. New keywords emerge, intent shifts, and competitors change the SERP landscape.

Common mistakes to avoid

Over-clustering. Don't create 50 tiny clusters when 15 solid ones would work better. Each cluster should have enough search volume to justify dedicated content.

Ignoring intent mismatches. Two keywords can be semantically related but have different intents. "Email marketing software" (commercial) and "what is email marketing" (informational) shouldn't share a page.

Set-and-forget. Clustering isn't a one-time exercise. The keywords your audience uses and what Google considers relevant results both change constantly.

Skipping the content audit. Before creating new content for your clusters, check what you already have. You might be able to optimize existing pages instead of starting from scratch.

Tools for AI keyword clustering

The tool landscape has evolved significantly:

Traditional SEO suites (Ahrefs, SEMrush) now include clustering features, though they primarily use SERP overlap rather than deep semantic analysis.
Dedicated clustering tools (KeywordInsights, Cluster AI) focus specifically on grouping and intent classification.
AI-native SEO tools like Jello use NLP and embeddings to cluster keywords by meaning and intent simultaneously, handling thousands of keywords in seconds rather than minutes.

The best choice depends on your workflow. If clustering quality and speed are your priority — especially at scale — an AI-native approach will outperform SERP-overlap methods.

The bottom line

Keyword clustering isn't new. But the way we do it is changing fast.

The manual spreadsheet approach still works for small sites with simple content strategies. But if you're producing content at any real scale, or competing in niches where topical authority matters, AI-powered clustering isn't optional — it's the baseline.

The sites winning in 2026 aren't the ones publishing the most content. They're the ones with the most intentional content structure. Clustering is how you build that structure.

Start with your top 5 topics. Cluster them properly. Build content that covers each cluster comprehensively. Then watch what happens to your rankings.

Jello helps you find and cluster keywords with AI — turning hours of keyword research into minutes. Try it free.