Iemand die hub en spoke gebruikt voor contentstrategie? Proberen te begrijpen of het helpt met AI-zichtbaarheid

Great question. The short answer: yes, hub and spoke matters for AI, but the implementation needs updating.

Why hub and spoke works for AI:

AI systems assess topical authority when deciding what to cite. When they see:

• Comprehensive pillar content on a topic
• Multiple supporting pieces diving deeper
• Strong internal linking showing relationships
• Consistent expertise signals across all

…they conclude you’re an authority worth citing.

What’s different for AI:

Traditional hub optimization:

• Keyword targeting
• Link structure
• Page hierarchy

AI-optimized hub structure:

• Question-answer format throughout
• Citable statements in each section
• Clear expertise demonstration
• Structured data connecting content
• Each piece stands alone AND connects

The key insight:

AI doesn’t just look at your pillar page. It sees your entire topic coverage. If you have 20 spoke articles showing deep expertise, that influences citations even when the question maps to the pillar.

It’s holistic topic authority, not just page-level optimization.

Technical structure perspective.

Hub optimization for AI:

URL structure:

/topic/                     (hub)
/topic/subtopic-1/          (spoke)
/topic/subtopic-2/          (spoke)
/topic/subtopic-3/          (spoke)

Clear hierarchy signals topic relationships.

Internal linking:

Hub → All spokes (obvious) Spokes → Hub (critical, often missing) Spokes → Related spokes (creates mesh)

Schema markup:

On hub pages, implement:

• Article or WebPage schema
• BreadcrumbList showing hierarchy
• FAQPage if including Q&A sections
• Consider ItemList referencing spoke pages

The structured connection:

Use “about” and “mentions” properties in schema to explicitly connect hub and spoke content. AI systems can parse these relationships.

Common mistakes:

1. Orphan spokes (not linked from hub)
2. Hub too shallow (not comprehensive enough)
3. No expertise signals on spokes
4. Inconsistent topic naming across pieces

Operational perspective on hub and spoke for AI.

How we structure hubs now:

Hub page anatomy:

1. Definition section - Clear, citable definition
2. Overview - Comprehensive topic coverage
3. Subtopic summaries - Each with link to spoke
4. FAQ section - Common questions answered
5. Expert perspective - Author credibility
6. Resources - Links to spokes and external authorities

What changed for AI:

Previously: Hubs were navigation-focused. “Here’s the topic, here’s where to learn more.”

Now: Hubs must also be citation-worthy standalone. AI might cite the hub directly, not send users to spokes.

The content depth tradeoff:

Hubs need to be:

• Comprehensive enough to cite directly
• Not so exhaustive that spokes become redundant

Balance: Hub answers the “what” and “why broadly.” Spokes answer specific “how” questions in depth.

Data perspective on hub vs spoke citations.

What we measured:

6-month study across 5 topic clusters, 50+ pieces of content total.

Citation distribution:

• Hub pages: 45% of topic cluster citations
• Top 3 spokes: 35% of citations
• Remaining spokes: 20% of citations

The pattern:

Hubs get cited for broad queries (“What is X?”) Spokes get cited for specific queries (“How do I do Y within X?”)

Both matter. Complete coverage drives total citations.

What predicts spoke citation rate:

1. Specificity of topic (more specific = more citable)
2. Question-answer format in headings
3. Original data or examples
4. Clear expertise signals

The surprising finding:

Some spokes outperform hubs for their specific queries. A detailed “how-to” spoke might get cited more than the broad hub for procedural questions.

Don’t assume the hub is always the citation target. Optimize each spoke for its specific query set.

Client implementation learnings.

What we do for new hub and spoke builds:

Phase 1: Topic Mapping

• Identify core topic (hub candidate)
• Map all related subtopics (spoke candidates)
• Analyze competitor topic coverage
• Prioritize based on search + AI opportunity

Phase 2: Hub Development

• Comprehensive coverage
• Every subtopic summarized
• FAQ section addressing common questions
• Expert author with strong bio
• Schema markup complete

Phase 3: Spoke Development

• Each spoke deep on one subtopic
• Question-focused headings
• Links back to hub
• Standalone expertise signals
• Related spoke cross-linking

Phase 4: Measurement

• Am I Cited monitoring for cluster
• Track hub and spoke citations separately
• Measure total topic visibility
• Compare to competitors

Timeline: Hub development: 2-4 weeks Initial spokes (5-10): 4-6 weeks Citation improvement: 6-12 weeks after publication

Smaller scale perspective.

If you don’t have resources for 20 spoke articles:

Minimum viable hub and spoke:

• 1 comprehensive hub page
• 3-5 high-priority spokes
• Strong internal linking
• Clear topic authority signals

Prioritize spokes by:

• Search volume for the subtopic
• Question frequency in AI responses
• Competitive gap (where you can win)

The quality principle:

5 excellent, deeply expert spoke articles > 20 shallow ones.

AI systems recognize depth. One truly authoritative piece on a subtopic can outperform three mediocre ones.

Build over time:

Start with hub + 3 spokes. Monitor citations. Add spokes based on where you see opportunity in AI responses.

The AI-specific hub optimization.

What makes a hub AI-citation-worthy:

Structure:

• Definition in first 100 words (citable)
• Section headings as questions
• Bullet points for key concepts
• Tables for comparisons
• FAQ section at bottom

Authority signals:

• Expert author with detailed bio
• Citations to authoritative sources
• Original insights or data
• Comprehensive scope

Technical:

• Fast loading
• Clean HTML structure
• Comprehensive schema
• Mobile-optimized

The hub citation test:

Ask ChatGPT a question about your topic. If it doesn’t cite your hub:

1. Look at what it does cite
2. Identify what those sources have that you don’t
3. Fill the gaps

Often it’s about: clearer structure, better expertise signals, or more comprehensive coverage.

The spoke question test:

For each spoke, ask the specific question it answers. Monitor if your spoke gets cited. If not, optimize that spoke’s structure and depth.

This thread clarified how to adapt hub and spoke for AI.

My key takeaways:

1. Hub and spoke does matter for AI - Topic authority is holistic
2. Hubs need to be citation-worthy standalone - Not just navigation
3. Spokes contribute to hub authority - Depth across spokes matters
4. Track cluster-level citations - Not just individual pages
5. Structure matters more for AI - Question-answer format, citable statements

Our updated approach:

Hub optimization:

• Add clear definition in first paragraph
• Include FAQ section
• Ensure each subtopic has summary + link
• Complete schema implementation

Spoke optimization:

• Question-focused headings
• Standalone expertise signals
• Links back to hub
• Deeper than hub section on same topic

Measurement:

• Am I Cited for cluster monitoring
• Track hub and spoke citations separately
• Compare to competitor topic coverage

Thanks everyone for the practical insights!