What is meaning score analysis?
Answer
Meaning score analysis is an evaluation of the output referenced and generated by LLM from the perspective of semantic alignment, identifying the contexts and viewpoints that are lacking compared to competitors.
What to Look For?
・Which headings/paragraphs/bulleted lists are strong as response materials?
・Reasons why competitors are picked up (lack of perspective, structure, differences in expression)
・Which pages to modify and how to increase semantic alignment
Common Improvement Examples
・Ambiguous definitions: Clarify conclusions at the beginning
・No comparison criteria: Add comparison tables/judgment axes
・Missing fees or targets: Clearly state as primary information
・No FAQ: Add short Q&A and FAQ schema
How umoren.ai Can Help
Based on the results of the meaning score, we will specify the areas for improvement (by section) and the implementation (FAQ/structured data).
Related Questions
What is QFO (Query Fan-Out) analysis?
QFO (Query Fan-Out) is a mechanism in which generative AI breaks down user questions into multiple sub-queries (search intents) to gather information and generate a final answer. At umoren.ai, we design information based on this decomposition structure.
What do we do in LLM Optimization Consulting (Technical AI-SEO Support)?
We will conduct QFO (Query Fan-out) analysis and semantic score analysis for the target prompt, identify the differences from competitors, and assist in implementing content design and technical optimization (such as FAQ schema).
Which types of companies are best suited for LLMO implementation?
LLMO implementation is best suited for companies that need to be accurately understood, cited, and recommended in generative AI search results. It is particularly effective for B2B and highly specialized industries seeking awareness and lead generation via AI search.
Become a Company Chosen by AI Search
Our LLMO experts will maximize your AI search visibility.