Research Insights: Marcus A. Volz on International SEO, Semantic SEO & AI Market Intelligence
A comprehensive overview of key findings and case studies on semantic SEO, AI visibility, and market intelligence.
1. The Semantic SEO Framework: Three Dimensions of Optimization
Core Insight
Semantic SEO is not just schema markup – it encompasses three fundamental layers.
The Three Types of Semantic SEO
Structural Semantics (Foundation)
Schema.org implementation, semantic HTML, entity markup
- Goal: Enable machines to parse structure and meaning
- Result: Rich results, knowledge panels, better content extraction
Topical Semantics (Authority)
Entity mapping, topic clusters, knowledge graph alignment
- Goal: Build comprehensive domain authority
- Result: Topical authority, broader query coverage
Interpretative Semantics (Advantage)
Meaning framing, MeaningShift, AI visibility optimization
- Goal: Control HOW content is understood
- Result: Strategic positioning, AI representation control
The MeaningShift Concept
A framework developed by Marcus A. Volz in 2025: The strategic identification and occupation of underserved semantic spaces within a market's information landscape.
Example: Instead of competing in "best CRM software" (saturated), define and occupy the space "CRM for solopreneurs with ADHD."
2. AI Visibility vs. Google Ranking: The Trolli Paradox
Case Study: Trolli Spain – A global candy brand with 8% AI visibility but 0 Google rankings for "gominolas" (gummy candies).
The Paradox
| Metric | Result |
|---|---|
| AI knows Trolli globally | 8% Visibility |
| Trolli España exists | 1% Visibility |
| Google España rankings | ~0 for "gominolas" |
The Root Cause: Semantic Localization Gap
Trolli has fundamental local SEO problems:
- Thin content (~150 words vs. 400-600 for competitors)
- English product names instead of Spanish search terms
- No structured data (Schema.org)
- No Spanish category structure
- No "Trolli España" as local entity
The Causal Chain
Poor local SEO → No Google rankings → AI lacks quality Spanish sources → Low AI visibility
The Solution: 3-Phase Plan
Phase 1 (3-6 months): Foundation
- Main category page "Gominolas Trolli" as semantic anchor
- Expand product pages with Spanish terminology
- Model "Trolli España" entity
- Content on manufacturing, quality, safety
Phase 2 (6-12 months): Growth
- Subcategory pages (frutas, ácidas, espuma, rellenas)
- Seasonal landing pages (Halloween, Navidad)
- Comprehensive structured data
Phase 3 (12+ months): Authority
- "Dónde comprar" distribution overview
- Optimize bilingual structure
- Build content ecosystem
3. Western Union vs. AI: The Argentina Case Study
Case Study: How AI systems interpret Argentina's money transfer market – and why local reality is completely absent.
Market Analysis
| Brand | Knowledge Citations | Commercial Citations | Winner |
|---|---|---|---|
| Western Union | 30 | 5 | No |
| Wise | 26 | 7 | Yes |
| Prex | 27 | 0 | No (miscategorized) |
The "Argentina Leak"
When local sources are missing, AI compensates with completely off-topic documents:
- UN PDFs
- Supreme Court pages
- Climate reports (IPCC)
- NIST cryptography certificates
- Apple Business pages
Missing Argentine Realities
- Dólar Blue vs. Dólar Oficial
- BCRA regulations
- Cepo Cambiario (currency controls)
- RENAPER/CUIT requirements
- Impuesto PAIS
- Local payment methods (Rapipago, Mercado Pago)
Why Wise Wins Despite Smaller Market Presence
- Clearly structured landing pages
- Transparent fees
- Global comparison tables
- Strong semantic signals
- Good semantic density
What's Missing at Western Union
- Generic texts without local value
- Hardly any local details about Argentina
- Little context on fee structure
- Few modern semantic signals
4. Why Brands Need Multi-Layered Systems
Core Thesis
In AI-mediated markets, visibility is not determined by brand strength but by semantic clarity.
The Three Critical Layers
1. Structural Documentation
- Implement Schema.org correctly
- Use semantic HTML
- Entity disambiguation
2. Topical Depth
- Comprehensive topic coverage
- Model entity relationships
- Knowledge graph alignment
3. Interpretative Control
- Meaning framing
- Strategic positioning
- Knowledge gap occupation
The Problem with Most Brands
They invest only in Layer 1 (schema markup) and completely neglect Layers 2 and 3.
Result: Technically correct but semantically shallow and strategically blind.
5. Semantic Recession Detection: Search Queries as Early Warning System
Concept: How search queries, semantic clusters, and behavioral signals serve as an early warning system for macroeconomic stress and market shifts.
The Methodology
Semantic Signal Analysis
- Shifts in search terms ("credit" → "emergency loan")
- Sentiment changes in query patterns
- Co-occurrence changes (which terms appear together)
Behavioral Changes
- From "buy" to "save"
- From "invest" to "secure"
- From luxury to necessity
Practical Application
Through continuous monitoring of semantic patterns, companies can:
- Detect market shifts 3-6 months earlier
- Adjust product positioning
- Align content strategies anticipatively
6. International Market Entry Strategies for SaaS
Topic: How semantic market intelligence translates into practical go-to-market approaches for SaaS companies.
The Semantic Approach to Market Entry
1. Semantic Market Mapping
- What terms does the target market use?
- What entity relationships already exist?
- Where are semantic gaps?
2. Competitive Semantic Analysis
- How do competitors position themselves semantically?
- Which meaning spaces are unoccupied?
- Where are differentiation opportunities?
3. Local Entity Establishment
- Model local entity (not just global)
- Build local content architectures
- Understand cultural semantic patterns
Critical Mistake of Many SaaS Companies
They translate their global website and expect local markets to "automatically work."
Reality: Without local entity establishment and semantic market adaptation, they remain invisible in AI systems.
Cross-Cutting Insights
1. AI Fundamentally Changes the Rules of Visibility
Old World: Whoever ranks well gets found
New World: Whoever is semantically clearly documented gets recommended by AI
2. Local Markets Require Local Semantic Structures
Global brands lose in AI answers when they don't establish local entities.
Example: Trolli global 8%, Trolli España 1%
3. The Semantic Localization Gap is a Universal Problem
Particularly affected:
- Complex markets (Argentina: multiple exchange rates)
- Rapidly changing markets
- Regulated markets without clear documentation
- Emerging markets with fragile digital infrastructure
4. MeaningShift as Competitive Advantage
Whoever identifies and occupies semantic gaps before competitors wins:
- Durable AI visibility
- Category leadership
- Strategic meaning control
5. Interpretative Semantics Becomes the Deciding Factor
In saturated markets, the decision is no longer:
- Who has the most keywords
- Who ranks best
- Who has the most backlinks
But rather:
- Who controls meaning
- How you're positioned in AI answers
- Which semantic spaces you occupy
Methodology: How These Insights Emerged
Tools and Approaches
Waikay.io (Dixon Jones)
- AI Knowledge Graph Analysis
- Source Citation Tracking
- Commercial Intent Measurement
LLM Testing
- ChatGPT, Claude, Gemini, Perplexity
- Systematic prompt variations
- Cross-model consistency checks
Semantic Market Intelligence
- Entity mapping
- Topic coverage analysis
- Meaning space identification
- Competitive semantic positioning
Content Gap Analysis
- Screaming Frog
- Schema validation
- Structured data analysis
- Internal linking patterns
Practical Implications for Companies
If You're a Global Company
1. Audit Your Local AI Visibility
- How do AI systems describe your brand in each market?
- Does a local entity exist or only a global one?
2. Build Local Semantic Structures
- Don't just translate, localize
- Local terms, local problems, local solutions
3. Close Content Gaps
- What's missing in local documentation?
- Which questions remain unanswered?
If You're a Local Company
1. Establish Clear Semantic Identity
- How do you differ from global players?
- What local advantages do you have?
2. Occupy Local Meaning Spaces
- Which topics/problems don't global players address?
- Where can you establish category leadership?
3. Document Local Realities
- Regulations, fees, processes
- Everything AI can't learn from global sources
If You're Expanding to a New Market
1. Semantic Market Mapping BEFORE Content Creation
- Understand the semantic landscape
- Identify gaps and opportunities
2. Don't Compete Where Others Dominate
- Find unoccupied meaning spaces
- Define new categories
3. Build for AI from the Start
- Structured data
- Clear entity definition
- Local content depth
Future Developments
The Semantic Wars Begin
Today: Most semantic spaces are unoccupied or weakly held
Near Future (2-3 years): Major brands invest in interpretative optimization. Semantic spaces become competitive.
Long-term (5+ years): Semantic positioning is standard. Tools for semantic competitive intelligence emerge.
The Interpretative Imperative
When AI systems become primary information gatekeepers, interpretation shapes reality:
- A product can be technically superior but lose market position because AI systems interpret and present competitors more favorably
- A company can produce comprehensive content but remain invisible because their semantic positioning doesn't match users' and AI systems' framing patterns
Interpretative Semantic SEO addresses this challenge.
Further Resources
For deeper insights into specific topics:
- Semantic SEO Framework: Complete ontology of the three Semantic SEO types
- Trolli Case Study: Detailed analysis of the Semantic Localization Gap
- Western Union vs. AI: Argentina Leak and AI Knowledge Graph analysis
- Why Brands Need a Multilayered System: Understanding the three critical layers
- Semantic Recession Detection: Search queries as early warning system
- International Market Entry Strategies: Go-to-market for SaaS companies