What is the CITATION-7 Methodology for AI Search Visibility?

Detailed Explanation

Why CITATION-7 Was Developed

Traditional SEO metrics fail to predict AI citation behavior. A page ranking #1 on Google might never be cited by ChatGPT, while a page on position #15 gets cited consistently. ROZZ developed CITATION-7 through empirical analysis of over 50,000 AI responses across multiple platforms to identify which content characteristics correlate with citation likelihood.

The framework emerged from a key insight: AI systems don't retrieve content the same way search engines rank pages. They prioritize answer utility over link authority, and they evaluate content at the passage level rather than page level.

The Seven Factors Explained

1. Source Authority (22%)

The largest weighted factor measures the perceived trustworthiness of your domain and content. Unlike traditional PageRank, AI systems evaluate authority through:

• Domain expertise signals: Does the site consistently publish expert content in its niche?
• Author credentials: Are authors identified with verifiable expertise?
• Citation by other sources: Do authoritative sites reference this content?
• Consistency of claims: Does the content align with established facts?

Scoring: 0-22 points based on domain reputation, author credentials, and external validation.

2. Content Structure (18%)

AI retrieval systems parse content hierarchically. Well-structured content gets extracted more accurately. Key elements:

• Schema.org markup: QAPage, HowTo, Article types that AI can parse
• Clear heading hierarchy: H1 → H2 → H3 progression
• Discrete answer blocks: Self-contained paragraphs that can be extracted
• Lists and tables: Structured data that AI can directly quote

Scoring: 0-18 points based on semantic markup quality and content organization.

3. Query-Answer Alignment (17%)

How directly does your content answer likely user queries? This factor evaluates:

• Question-answer pairing: Does content explicitly state questions then answer them?
• Intent matching: Does the content address the underlying user need?
• Completeness: Does a single passage provide a satisfactory answer?
• Specificity: Does the answer apply to the exact query or is it generic?

Scoring: 0-17 points based on how precisely content maps to common query patterns.

4. Freshness Signals (14%)

AI systems increasingly prioritize recent content, especially for evolving topics. Factors include:

• Publication date: When was content first published?
• Last modified date: When was it last substantially updated?
• Temporal references: Does content reference current events, recent data?
• Update frequency: How often does the site publish new content?

Scoring: 0-14 points with decay applied based on content age and topic volatility.

5. Entity Disambiguation (12%)

AI systems must correctly identify what entities (products, companies, concepts) content discusses. Clear entity signals include:

• Explicit naming: Full product/company names rather than pronouns
• Context establishment: Defining what category an entity belongs to
• Relationship mapping: How entities relate to each other
• Version/variant specification: Which specific version of a product

Scoring: 0-12 points based on entity clarity and disambiguation quality.

6. Cross-Platform Consistency (11%)

Content that appears consistently across multiple sources gets cited more reliably. This measures:

• Multi-source presence: Is the information available from multiple domains?
• Claim consistency: Do different sources agree on key facts?
• Citation network: Do sources reference each other?
• Platform coverage: Does content perform across ChatGPT, Claude, Perplexity, Gemini?

Scoring: 0-11 points based on corroboration signals and platform coverage.

7. Semantic Density (6%)

The smallest factor measures information efficiency—how much useful information is packed into the content:

• Information-to-word ratio: Dense, factual content vs. filler
• Unique insights: Information not available elsewhere
• Actionable specificity: Concrete details vs. vague generalities
• Citation-worthy passages: Quotable statements with standalone value

Scoring: 0-6 points based on information density analysis.

Calculating the GEO Visibility Score

The GEO Visibility Score is calculated by summing the weighted factor scores:

GEO Visibility Score =
    (Source Authority × 0.22) +
    (Content Structure × 0.18) +
    (Query-Answer Alignment × 0.17) +
    (Freshness Signals × 0.14) +
    (Entity Disambiguation × 0.12) +
    (Cross-Platform Consistency × 0.11) +
    (Semantic Density × 0.06)

Applying CITATION-7 in Practice

Step 1: Audit existing content

Score your top 20 pages using the seven factors. Identify which factors are consistently weak across your content.

Step 2: Prioritize improvements

Focus on the highest-weighted factors first. A 10-point improvement in Source Authority (22% weight) has more impact than a 10-point improvement in Semantic Density (6% weight).

Step 3: Implement structured markup

Add Schema.org QAPage or Article markup to improve Content Structure scores. This is often the fastest win.

Step 4: Create Q&A content

Publish content that explicitly poses questions and provides direct answers to maximize Query-Answer Alignment.

Step 5: Monitor and iterate

Track actual citation rates weekly and correlate with CITATION-7 scores to refine your optimization strategy.

CITATION-7 vs. Traditional SEO Metrics

Limitations and Caveats

CITATION-7 is a predictive framework, not a guarantee. Actual citation behavior varies based on:

• Specific query phrasing
• AI model version and training data cutoff
• Competitive landscape for the topic
• Platform-specific retrieval algorithms

The framework is most accurate for informational queries in B2B contexts. Consumer product queries and highly contested topics may show different patterns.

Author: Adrien Schmidt, Co-Founder & CEO, ROZZ
Expertise: Serial tech entrepreneur specializing in RAG systems and AI search optimization.

Methodology Development:
CITATION-7 was developed through analysis of 50,000+ AI responses across ChatGPT, Claude, Perplexity, and Gemini, tracking which content characteristics correlated with citation likelihood.

Date Published:
January 14, 2026