Keyword clustering only works when it maps to entities, intent, and internal links that machines can read.

Random clusters create duplicate content and weak signals; AI assistants ignore you.

This playbook shows you how to research, cluster, prioritize, brief, and measure topics so they win in SERPs and AI Overviews.

Use it alongside our semantic strategy pillar at Semantic SEO that Scales: Entity-Led Strategy & KPIs and structured data pillar at Structured Data: The Complete Guide for SEO & AI.

What keyword clustering is (today)

Grouping semantically related queries into intent-driven groups that map to one pillar and supporting pages.

Each cluster ties to explicit entities, schema, and internal links—so Google and AI assistants see coherent coverage instead of thin duplicates.

Why clustering matters for AI and SEO

  • Reduces cannibalization; clarifies which page answers which intent.

  • Improves entity clarity with consistent @id and about/mentions.

  • Feeds AI assistants complete, disambiguated answers, boosting citation odds.

  • Speeds production with reusable briefs and link patterns.

Workflow overview

  1. Collect keywords and questions.

  2. Normalize and enrich with entities and intent.

  3. Cluster (AI + human review).

  4. Map clusters to pillar/support structure.

  5. Create briefs with schema, links, and E-E-A-T requirements.

  6. Publish, validate, and monitor performance and AI citations.

Step 1: collect data

  • Sources: Search Console, PAA, AI answers, site search, sales/support calls, competitor pages, forums.

  • Include modifiers: location, industry, job role, language.

  • Capture target entities (brand, product, problem, integration) in the sheet.

Step 2: normalize and enrich

  • Clean duplicates, fix casing, standardize brand/product names.

  • Add intent labels: define/compare/how-to/troubleshoot/pricing/decision.

  • Attach entities: primary concept, products/services, locations, audience; add @id from your map.

  • Flag YMYL queries needing reviewers and stronger E-E-A-T.

Step 3: cluster with AI + human review

  • Use embedding-based clustering or tools; set a distance threshold to avoid over-merged clusters.

  • Human-review clusters to split by intent and audience; remove outliers that need their own page.

  • Name clusters with clear scope (topic + intent) and align to entities.

Step 4: map to site architecture

  • Pillar per cluster for broad intent; supports for sub-intents and tasks.

  • Link to commercial pages where intent shifts (pricing, demo, booking).

  • Ensure one page per intent; avoid two supports targeting the same query set.

  • Add cluster to navigation via breadcrumbs and related modules.

Step 5: create briefs

  • Include entities/IDs, intent, persona, required questions, and CTAs.

  • Schema type (Article/HowTo/FAQ/Product/Service) and about/mentions list.

  • Internal links: pillar, siblings, commercial targets, case studies.

  • E-E-A-T: credentials, reviewer (if YMYL), sources to cite.

  • Media: tables/diagrams for comparisons and steps.

Step 6: publish and validate

  • Answer-first intros; define the entity/topic immediately.

  • Add schema matching visible content; reuse @id across cluster.

  • Validate with Rich Results Test; crawl for missing fields and duplicate IDs.

  • Check parity (price, hours, credentials) vs page and feeds.

  • Submit sitemaps; run prompt spot checks in AI Overviews/assistants.

Prioritization matrix

  • Impact: revenue potential, AI citation potential, strategic importance.

  • Effort: content depth, SME time, design needs.

  • Competition: SERP authority, AI answer landscape (who gets cited?).

  • Data health: do you have clean IDs/sameAs for the entities in this cluster?

Internal linking rules

  • Pillar links to all supports; supports link back and to relevant siblings.

  • Use entity + intent anchors (“AI search workflow checklist”) not generic text.

  • Add related modules keyed to shared entities; avoid duplicate links on-page.

  • Keep supports within three clicks of home; fix orphans monthly.

Schema checklist per cluster

  • Article/BlogPosting with about/mentions, author Person, publisher Organization, BreadcrumbList.

  • FAQ/HowTo on supports where relevant; visible Q&A/steps.

  • Product/Service on commercial pages; offers, identifiers, brand, price currency.

  • Person and Organization schema consistent across cluster; @id reuse documented.

  • WebSite with searchAction; Sitelinks searchbox for brand navigation.

AI prompt bank for clustering QA

  • “What is [cluster topic]?”

  • “How do I [task] for [cluster topic]?”

  • “Best tools for [cluster topic]?”

  • “Common mistakes in [cluster topic]?”

  • “Pricing for [cluster topic] solutions?”

  • “Who provides [cluster topic] services in [location]?”

  • Log citations and accuracy monthly; fix definitions/schema/anchors when off.

Measurement and KPIs

  • Coverage: % clusters with pillar + supports published; schema coverage per template.

  • Eligibility: rich result detections per cluster; zero blocking errors goal.

  • AI citations: mentions of cluster pages in AI Overviews/assistants; share vs competitors.

  • CTR: change after schema/link/brief rollout; compare to control clusters.

  • Conversion: leads/bookings/cart adds from cluster entry pages; assisted conversions.

  • Salience: NLP scores for target entities on pillar/supports; track improvement.

Experiments to prove value

  • Add FAQ/HowTo schema to half the supports; measure CTR and citations vs holdout.

  • Rewrite intros with answer-first definitions; track prompt accuracy and CTR.

  • Anchor optimization test: entity-rich vs generic anchors on sibling links.

  • Refresh bios and sameAs for authors on a cluster; track AI citations and SERP CTR.

  • Localize one cluster with stable IDs; measure rich results and assistant mentions by locale.

Maintenance cadence

  • Monthly: crawl for schema/link issues; run prompt bank; fix parity gaps.

  • Quarterly: refresh stats, offers, bios; prune/merge duplicates; audit ID map.

  • After major releases: validate rendered schema; check anchors and related modules.

Case snapshots

SaaS integrations cluster

  • Clustered integration queries; built pillars and supports per integration with shared Product/SoftwareApplication IDs.

  • Added FAQs and HowTo for setup; linked to partner pages; prompt bank focused on “does it integrate with…”.

  • Result: AI Overviews cited integration guides; CTR +11%; demos from integration pages +9%.

Local services cluster

  • Clustered services and conditions; pillars per service; supports for symptoms, treatments, FAQs.

  • LocalBusiness/Person/Service schema tied to each support; hours parity enforced.

  • Result: assistants answered with correct hours and practitioners; calls/bookings up.

Ecommerce cluster

  • Clustered “how to choose/compare/care” queries; pillars plus how-to/FAQ supports; linked to Product pages.

  • Product schema with identifiers; HowTo schema on care guides; related SKUs modules.

  • Result: Product rich results returned; add-to-cart from cluster pages +10%; AI answers cited correct specs.

Governance and roles

  • SEO/content: owns clustering, briefs, and prompt bank.

  • Engineering: owns schema templates, ID reuse, and link modules; CI validation.

  • Analytics: dashboards for coverage, eligibility, citations, CTR, conversions; alerts for drops.

  • PR/Brand: sameAs alignment and naming consistency.

  • Ops/PM: prioritization and sprints for cluster production and refreshes.

90-day rollout

  • Weeks 1–2: gather data; run clustering; human-refine; draft cluster map and IDs.

  • Weeks 3–4: write first pillar + 3–5 supports; add schema and links; validate.

  • Weeks 5–6: publish pilot cluster; set dashboards and prompt logs; annotate launch.

  • Weeks 7–8: expand supports; add FAQs/HowTos; optimize anchors and CTAs.

  • Weeks 9–12: localize if relevant; prune overlaps; run experiments and report results.

Analytics and dashboards

  • Coverage: % clusters with pillar + supports live; schema coverage per template; duplicate ID count.
  • Eligibility: rich result detections per cluster; errors/warnings trend; time to fix.
  • AI citations: assistant mentions by cluster; accuracy notes; competitor share where visible.
  • Performance: impressions/CTR/conversions per cluster entry page; assisted conversions.
  • Freshness: age of stats, offers, bios, and screenshots; alerts for stale items.
  • Link health: orphan count, average depth, broken/redirected anchor rate.

Experiments to prove value

  • FAQ/HowTo schema A/B: add to half of similar supports; measure CTR and citations.
  • Anchor test: entity + intent vs generic anchors on sibling links; monitor CTR and crawl depth.
  • Module placement: move related-content modules higher; measure engagement and AI citations.
  • Refresh test: update intros and definitions on select supports; track salience scores and AI answer accuracy.
  • Localization test: localize one cluster with shared IDs; compare rich results and citations by locale.

Governance and roles

  • SEO/content: owns clustering, briefs, prompt bank, and audits.
  • Engineering: owns schema templates, ID reuse enforcement, link modules, CI validation.
  • Analytics: owns dashboards, alerts, and reporting; joins Search Console, analytics, and prompt logs.
  • PR/Brand: keeps sameAs and naming consistent; aligns press with canonical entities.
  • Ops/PM: prioritizes clusters, schedules sprints, and ensures refresh cadence.

Migration and cleanup

  • Crawl and export all URLs; tag by topic/entity; spot duplicates and thin pages.
  • Choose canonical pillars/supports; redirect duplicates; preserve legacy IDs where possible.
  • Update anchors to new structure; fix orphaned supports; refresh sitemaps.
  • Validate schema and links post-migration; monitor traffic and citation stability.

Internal linking deep dive

  • Anchor rules: use entity + intent; avoid generic anchors; ensure anchors describe destination.
  • Reciprocal linking: supports must link back to pillar; pillar must link to each support.
  • Sibling linking: connect related supports to reduce pogo-sticking and improve crawl paths.
  • Depth control: supports within three clicks from home; use breadcrumbs and footer links for large clusters.
  • Crawl monthly to catch broken anchors and missing links; fix promptly.

Structured data QA (detailed)

  • Article/BlogPosting: headline, author Person, publisher Organization, dates, image, about/mentions, BreadcrumbList.