Keyword Clustering Guide + Free Tool (Entity, Intent, Topic Trees) | Accord Content

Advanced SEO Workflows

Keyword Clustering Guide + Free Tool (Entity & Intent Trees)

Turn hundreds of keywords into a clean plan. This page explains clustering (topic, intent, entities), shows tree diagrams, and includes a free in-page tool to group, tag, and export. When your clusters are clear, content planning, internal links, and topical authority get a lot easier.

Updated: ~15–20 min read

Definitions

Keyword clustering

Grouping semantically related queries so you plan one page per topic (or intent) instead of separate, competing pages. Good clusters reduce cannibalization, make internal linking obvious, and help with entity coverage.

SERP overlap

How many URLs appear on both results pages for two queries. If the overlap is high, the queries likely belong in the same cluster because searchers see them as the same task.

Entity-led clustering

Grouping by the people, products, brands, places, or concepts in the query. Entities give you durable structure when word forms vary and help your pages be quotable by AI systems.

Tip: one page per dominant intent. If two queries share the topic but have different intents (e.g., “what is…” vs “pricing”), plan separate pages and link between them.

Why clustering matters

  • Avoid cannibalization: multiple weak pages for the same idea split equity.
  • Faster planning: clusters give you a backlog and a content calendar in one.
  • Topical authority: clusters become hubs with internal links that signal coverage.
  • Better UX: readers find all angles of a topic without pogo-sticking.
SERP-based clustering is the gold standard, but you can start with lexical/entity similarity (like the free tool below) and upgrade to SERP overlap with a specialized platform.

Clustering methods (choose by your constraints)

1) SERP overlap (best)

  • Fetch top results for each query, count URL overlap
  • Group queries with overlap above a threshold (e.g., 3+ shared URLs)
  • Pros: most faithful to what searchers see
  • Cons: needs live SERP data and rate limits

2) Token similarity (fast)

  • Normalize queries, remove stopwords, compare token sets
  • Use Jaccard similarity with a threshold (e.g., 0.4–0.6)
  • Pros: simple, local, instant
  • Cons: misses nuance without entities

3) Entity-led + intent (robust)

  • Extract entities and modifiers (“best”, “pricing”, “vs”)
  • Cluster by shared entities, subgroup by intent
  • Pros: great for planning content types
  • Cons: light NLP or rules required

For SERP-first workflows that also pull entities from results, consider Keyword Insights. It’s built for clustering by SERP similarity, tagging intent, and extracting entities—handy when you want production-grade clusters in one export.

Data prep

  • Collect your queries from Search Console, ad tools, and internal search.
  • Deduplicate and trim. Keep volume and any labels if you have them.
  • Remove brand-only navigational terms from generic clusters; plan their own pages if needed.
keywordvolume (optional)
content clustering1900
keyword clusters for seo320
best keyword grouping tool140

Free Keyword Clustering Tool (client-side)

Paste your keywords or upload a CSV. Choose the method, set a similarity threshold if applicable, and generate clusters. You can copy results, view tree diagrams, and export CSV/JSON. Everything runs in your browser.

0.50
Modes: Jaccard, Head Term, Entity+Intent Copy & Export Tree view

From clusters to pages

Choose the parent

  • Pick a “cluster head” keyword with clean intent and volume
  • Make it the primary page; others become subheads/FAQs

One intent per page

  • Informational: “what/how/guide”
  • Commercial: “best vs alternatives”
  • Transactional: “pricing, demo, download”

Internal links

  • Hub → detail pages in the cluster
  • Commercial → solution/pricing
  • FAQ schema only if visible Q&A exists

Tree diagrams (examples)

The tool renders a simple tree for each cluster. Root = topic, branches = intents, leaves = keywords. Use it to sense-check structure before writing.

Tree layout here is lightweight for speed. If you want force-directed or dendrograms from SERP similarity scores, export JSON and render in your BI tool or a notebook.

Exporting & next steps

  • Export CSV/JSON: plan briefs and roadmaps from the outputs.
  • Upgrade to SERP overlap: for production scaling, try Keyword Insights to cluster by live SERPs and pull entities automatically.
  • Publish order: start with cluster heads, then supporting pieces, then comparisons/alternatives if the topic warrants it.

FAQ

How big should a cluster be

Enough to cover a topic without mixing intents. Many healthy clusters end up 5–20 keywords with one parent page.

Do I merge synonyms

Yes if SERPs are largely the same. If SERPs change meaningfully by region or audience, plan per variant and cross-link.

What about brand terms

Give brand navigational queries their own pages (pricing, login, docs). Keep generic clusters clean.

Can I feed this into briefs

Absolutely. Use the CSV to define H2/H3s, FAQs, and internal links per page.

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