What is topical research for AI-search?
Topical research for AI-search is systematically researching which questions your audience asks in AI tools, which sources are cited, and where you can fill content gaps. It goes beyond traditional keyword research.
From keywords to questions
Traditional SEO approach
- Focus on search volume
- Exact keyword targeting
- Rankings as success metric
AI-search approach
- Focus on question intent
- Thematic coverage
- Citations as success metric
Competitor visibility mapping
A crucial part of topical research is understanding which sources AI tools currently cite.
How to do this:
-
Collect relevant prompts
- "Best [your service] in Belgium"
- "What is [core concept from your industry]"
- "Compare [option A] vs [option B]"
- "How to [solve problem you address]"
-
Test in multiple AI tools
- ChatGPT (with and without web search)
- Perplexity (for explicit source citation)
- Gemini
- Claude
-
Document the results
- Which sources are cited?
- How are competitors described?
- Where is your brand mentioned (or not)?
- What information is missing?
Content gap analysis
After competitor mapping, identify where opportunities lie:
Type 1: Definition gaps
AI tools look for clear definitions. If no one in your market offers a good definition of a core concept, that's an opportunity.
Example: "What is B2B marketing automation?" – many sites talk about it, few give a citable definition.
Type 2: Comparison gaps
Decision makers often look for comparisons. Those who compare objectively get cited.
Example: "HubSpot vs Salesforce for mid-sized companies" – a neutral, comprehensive comparison scores.
Type 3: How-to gaps
Practical step-by-step guides that actually help.
Example: "How to implement lead scoring in HubSpot" – specific, practical, citable.
Type 4: FAQ gaps
Frequently asked questions that no one answers well.
Example: "How much does a fractional CMO cost?" – often evasively answered.
Topical clustering
Build your content around core themes, not loose keywords:
Example cluster: "B2B Marketing Automation"
Pillar page:
- Complete guide to marketing automation
Cluster content:
- What is marketing automation?
- HubSpot vs Pardot comparison
- Lead nurturing best practices
- Calculating marketing automation ROI
- Common automation mistakes
- Case study: automation implementation
Benefits of clustering
- AI recognizes you as an authority on the topic
- Internal linking strengthens all pages
- Higher chance of multiple citations per query
- Better user experience
Practical research workflow
Week 1: Setup
- Define 5-10 core themes
- Collect 50+ relevant prompts
- Choose AI tools for testing
Week 2: Testing
- Test all prompts in all tools
- Document results structurally
- Identify patterns
Week 3: Analysis
- Competitor visibility matrix
- Content gap prioritization
- Identify quick wins
Week 4: Planning
- Create content calendar
- Set priorities
- Allocate resources
What can MatthCon do for you?
MatthCon performs extensive topical research for your market. We deliver a competitor visibility matrix, content gap analysis and prioritized content roadmap. Schedule a call or discover our complete AI-search approach.