DIVISION:
GENERATIVE ENGINE
OPTIMIZATION
CURRENT STATE: The "Ten Blue Links" era is collapsing. Users now query LLMs directly for synthesized answers.
MISSION: Transition SQUADROID's visibility strategy from Traditional SEO (Search Engine Optimization) to comprehensive GEO (Generative Engine Optimization). Ensure our entities are the source of truth for AI models.
THE NEW SEARCH PARADIGM
> KEYWORD DENSITY -> IRRELEVANT
> BACKLINKS -> SIGNAL NOISE
> -------------------------
> GEO: OPTIMIZING FOR LLMS
> ENTITY SALIENCE -> CRITICAL
> SEMANTIC AUTHORITY -> CRITICAL
> STRUCTURED DATA -> MANDATORY
- Credibility & Citations: LLMs prioritize sources with high citation authority.
- Semantic Structure: Content must be formatted for easy machine parsing (JSON-LD, Lists, Direct Answers).
- Fluency & Optimization: Content must be intelligible and valuable to the model's training objective.
FORMATTING FOR INCLUSION
Standardize all content output to "Answer First" methodology.
- The Definition: Clear, concise definition of the topic in the first 50 words.
- The List: Bullet points or numbered lists immediately following.
- The Table: Structured comparison data where applicable.
ENTITY ESTABLISHMENT
- SameAs Schema: Link all social profiles and external citations.
- About Schema: Explicitly define what the page is about using Wikidata IDs.
- Mentions Schema: Tag related entities to build semantic relationships.
Focus on proprietary plugins and Bing index data. High emphasis on reputable, mainstream citations.
The "Answer Engine". Requires real-time data freshness and clear, scholarly sourcing. Academic format preferred.
Heavy reliance on Google's Knowledge Graph. YouTube video transcripts and reputable news sources are prioritized.
Large context window analysis. Prefers comprehensive, well-structured long-form content over snippets.
- Share of Model (SoM): Frequency of brand mentions in AI-generated responses for category keywords.
- Sentiment Analysis: AI perception of the brand (Positive/Neutral/Negative).
- Citation Frequency: How often our URLs are provided as "Learn More" sources.