Competitive Analysis
Semantic Competitive Analysis: Understanding Your Amazon Competitors Through an AI Lens
Learn how semantic competitive analysis provides deeper insights than traditional methods in the age of AI-driven commerce. LMO7 explores AI-focused competitor research.
6 June 2025
18 min read
Competitive analysis has long been a cornerstone of effective Amason strategy. Traditionally, this analysis has focused on visible metrics like pricing, ratings, review counts, and Best Seller Rank. While these metrics remain important, they capture only the surface-level performance of competitors—not the underlying semantic structures that increasingly determine visibility in AI-mediated shopping experiences.
Semantic competitive analysis represents a fundamental evolution in how brands understand their competitive landscape. Rather than focusing solely on performance metrics, this approach examines how competitors structure their content semantically, establish entity relationships, and position themselves for AI understanding.
How LLMs Compare and Differentiate Products
To understand the importance of semantic competitive analysis, we must first examine how AI systems approach product comparison and differentiation:
1. Entity-Based Comparison
LLMs don't simply match keywords when comparing products—they build entity-based understanding of each product's attributes and characteristics. When a consumer asks about differences between brands, the AI attempts to:
- Identify the specific entities being compared
- Extract key attributes for each entity
- Determine which attributes are comparable
- Identify meaningful differences between comparable attributes
2. Confidence-Weighted Responses
When generating comparative responses, LLMs assign confidence scores to different pieces of information. Information that appears consistently across multiple sources receives higher confidence than isolated claims.
3. Contextual Relevance Filtering
LLMs don't present all differences between products—they filter based on contextual relevance to the query and perceived importance to the consumer.
4. Narrative Construction
Beyond simply listing differences, advanced LLMs construct comparative narratives that help consumers make decisions.
Conducting a Semantic Gap Analysis
The foundation of semantic competitive analysis is a thorough gap analysis that examines how your products and competitors are represented semantically:
1. Entity Coverage Assessment
Compare how comprehensively your products and competitor products establish key entities:
- Identify all entity types relevant to your product category
- Document entity coverage across competitor listings
- Score entity comprehensiveness for each competitor
- Identify entity gaps in your own listings
2. Attribute Specificity Comparison
Analyse the level of specificity in how attributes are described:
- Compare specificity of measurements and specifications
- Assess use of precise vs. general terminology
- Evaluate quantitative vs. qualitative descriptions
- Analyse technical detail depth
3. Relationship Mapping Analysis
Examine how competitors establish relationships between entities:
- Identify explicit connections between features and benefits
- Map compatibility relationships with other products/systems
- Analyse problem-solution relationships
- Document comparative relationships to alternatives
4. Semantic Structure Comparison
Analyse the overall semantic structure of competitor content:
- Compare content organisation and hierarchy
- Assess logical flow and information architecture
- Evaluate heading structure and information chunking
- Analyse paragraph and sentence structure
AI Response Pattern Analysis
Understanding how AI systems currently represent your competitors provides crucial insights for improvement:
1. Query Response Mapping
Test how different AI assistants respond to category and product-specific queries:
- Develop a comprehensive query set covering category, feature, problem, and competitive dimensions
- Test each query across target AI assistants at regular intervals
- Record which competitors appear in responses and how they're positioned
- Analyse response patterns and positioning consistency
2. Feature Emphasis Analysis
Analyse which product features AI systems emphasise for different competitors:
- Document which features AI systems highlight for each competitor
- Compare feature emphasis patterns across different query types
- Identify features that give competitors advantages in AI responses
- Map correlation between content structure and AI feature emphasis
3. Competitive Positioning Assessment
Evaluate how AI systems position competitors relative to each other:
- Document positioning language used for each competitor
- Analyse whether competitors are positioned as premium, budget, or mid-range
- Identify use case specialisations attributed to each competitor
- Map competitive advantages emphasised by AI systems
Semantic competitive analysis represents a fundamental shift in how brands understand and respond to competitive pressures in AI-driven commerce. UK brands that master this approach gain sustainable advantages in AI visibility, customer acquisition, and market positioning.
Semantic competitive analysis represents a fundamental evolution in how brands understand their competitive landscape. Rather than focusing solely on performance metrics, this approach examines how competitors structure their content semantically, establish entity relationships, and position themselves for AI understanding.
How LLMs Compare and Differentiate Products
To understand the importance of semantic competitive analysis, we must first examine how AI systems approach product comparison and differentiation:
1. Entity-Based Comparison
LLMs don't simply match keywords when comparing products—they build entity-based understanding of each product's attributes and characteristics. When a consumer asks about differences between brands, the AI attempts to:
- Identify the specific entities being compared
- Extract key attributes for each entity
- Determine which attributes are comparable
- Identify meaningful differences between comparable attributes
2. Confidence-Weighted Responses
When generating comparative responses, LLMs assign confidence scores to different pieces of information. Information that appears consistently across multiple sources receives higher confidence than isolated claims.
3. Contextual Relevance Filtering
LLMs don't present all differences between products—they filter based on contextual relevance to the query and perceived importance to the consumer.
4. Narrative Construction
Beyond simply listing differences, advanced LLMs construct comparative narratives that help consumers make decisions.
Conducting a Semantic Gap Analysis
The foundation of semantic competitive analysis is a thorough gap analysis that examines how your products and competitors are represented semantically:
1. Entity Coverage Assessment
Compare how comprehensively your products and competitor products establish key entities:
- Identify all entity types relevant to your product category
- Document entity coverage across competitor listings
- Score entity comprehensiveness for each competitor
- Identify entity gaps in your own listings
2. Attribute Specificity Comparison
Analyse the level of specificity in how attributes are described:
- Compare specificity of measurements and specifications
- Assess use of precise vs. general terminology
- Evaluate quantitative vs. qualitative descriptions
- Analyse technical detail depth
3. Relationship Mapping Analysis
Examine how competitors establish relationships between entities:
- Identify explicit connections between features and benefits
- Map compatibility relationships with other products/systems
- Analyse problem-solution relationships
- Document comparative relationships to alternatives
4. Semantic Structure Comparison
Analyse the overall semantic structure of competitor content:
- Compare content organisation and hierarchy
- Assess logical flow and information architecture
- Evaluate heading structure and information chunking
- Analyse paragraph and sentence structure
AI Response Pattern Analysis
Understanding how AI systems currently represent your competitors provides crucial insights for improvement:
1. Query Response Mapping
Test how different AI assistants respond to category and product-specific queries:
- Develop a comprehensive query set covering category, feature, problem, and competitive dimensions
- Test each query across target AI assistants at regular intervals
- Record which competitors appear in responses and how they're positioned
- Analyse response patterns and positioning consistency
2. Feature Emphasis Analysis
Analyse which product features AI systems emphasise for different competitors:
- Document which features AI systems highlight for each competitor
- Compare feature emphasis patterns across different query types
- Identify features that give competitors advantages in AI responses
- Map correlation between content structure and AI feature emphasis
3. Competitive Positioning Assessment
Evaluate how AI systems position competitors relative to each other:
- Document positioning language used for each competitor
- Analyse whether competitors are positioned as premium, budget, or mid-range
- Identify use case specialisations attributed to each competitor
- Map competitive advantages emphasised by AI systems
Semantic competitive analysis represents a fundamental shift in how brands understand and respond to competitive pressures in AI-driven commerce. UK brands that master this approach gain sustainable advantages in AI visibility, customer acquisition, and market positioning.