For years, SEO practitioners have grappled with the concept of E-E-A-T—Experience, Expertise, Authoritativeness, and Trustworthiness—often reduced to vague advice like “build your brand.” However, beyond this simplified narrative lies a complex web of signals that Google uses to evaluate content quality, trust, and authority.

Based on over eight years of research across 40+ Google patents and official sources, I’ve identified more than 80 actionable signals that clarify how E-E-A-T operates at the document, domain, and entity levels. It’s time to unpack these insights and understand how Google’s algorithms incorporate relevance, pertinence, and quality to shape search rankings.

The Misconception About E-E-A-T

Many SEOs mistakenly downplay the significance of E-E-A-T in rankings, dismissing it as little more than a buzzword. However, terms like “helpful content” and “E-E-A-T” are part of Google’s public-facing narrative, designed to frame its search product in a positive light. These labels encompass a wide range of underlying signals and algorithms working behind the scenes.

Google applies various signals to assess E-E-A-T, creating a framework that algorithmically prioritizes trustworthy resources in search results and scales quality evaluations. This process may even influence the selection of resources for training large language models (LLMs), making it crucial to understand and optimize for E-E-A-T.

Though E-E-A-T is not directly referenced in Google patents, API leaks, or DOJ documents, my research has focused on sources related to quality, trust, authority, and expertise—core elements for understanding E-E-A-T’s role.

Relevance, Pertinence, and Quality in Search Engine Results

Before diving into the researched E-E-A-T signals, it’s important to distinguish between relevance, pertinence, and quality in information retrieval.

• Relevance: This is the objective relationship between a search query and the content. Google evaluates relevance using factors like keyword usage, TF-IDF scoring, internal/external linking, and user signals (e.g., DeepRank, RankEmbed BERT).
• Pertinence: This represents the subjective value of content to individual users. For example, an SEO professional and a beginner searching for “search engine optimization” will have different expectations for content depth and complexity.
• Quality: Quality in search engines is a broader evaluation that encompasses factors like how well content fulfills its purpose, the demonstrated expertise across topics, and the overall user experience. Quality is assessed by systems like Coati (formerly Panda) and the Helpful Content System.

The Three Levels of E-E-A-T Evaluation

Google’s ranking system now evaluates content at three levels: document, domain, and source entity, with increasing emphasis on E-E-A-T principles.

1. Document Level: Assesses the quality of individual content pieces.
2. Site/Domain Level: Evaluates domain-wide quality factors affecting the entire website or specific sections.
3. Source Entity Level: Focuses on the entities behind the content—such as authors and organizations—using E-E-A-T criteria to determine their authority and trustworthiness.

The concept of source entity aligns with Google’s entity-based search, introduced in the Hummingbird update, which applies E-E-A-T principles to entities and refines content evaluation.

Key Signals for E-E-A-T Quality Assessment

In 2022, I outlined 14 ways Google might evaluate E-A-T on Search Engine Land. Since then, this framework has expanded to include new dimensions of E-E-A-T, reflecting Google’s evolving approach to quality assessment.

Document-Level Signals

• Content Originality and Comprehensiveness: High-quality, unique content that thoroughly covers a topic and aligns with user intent.
• Grammar and Presentation: Clean, well-structured, and error-free content.
• Quality of External References: High-quality outbound links to authoritative sources.
• User Engagement Metrics: Indicators like click-through rates (CTR), dwell time, and URL visits that reflect content value.
• Search Term Relevance: Content that matches user intent and uses relevant vocabulary.

Domain-Level Signals

• Trustworthiness:
  • Business verification (consistent company details).
  • Link profile quality (high-quality backlinks and clean outbound links).
  • Security measures (HTTPS protocols and transparent information).
  • Brand consistency (consistent business info and brand recognition).
• Authoritativeness:
  • Link diversity and PageRank.
  • Historical performance (sustained ranking and user engagement).
  • Content network strength (interlinked relevant content).
  • Topical focus (expertise in specific subject areas).
• Expertise and Experience:
  • Content freshness (regularly updated content).
  • Category relevance (strong performance in relevant content categories).
  • Topic coverage depth (comprehensive content that satisfies informational and navigational user intent).

Source Entity-Level Signals

• Trustworthiness:
  • Contributor verification (authenticating content creators).
  • Reputation tracking (monitoring historical accuracy and credibility).
  • Peer endorsements (reviews and recognition from reputable sources).
  • Publication history (volume and quality of past content).
• Authoritativeness:
  • Citations and references to authoritative sources.
  • Recognition and awards from industry leaders.
  • Brand presence (verified business information and online reputation).
• Expertise and Experience:
  • Subject matter alignment (author expertise matching content topics).
  • Publication consistency (regular content contributions).
  • Original contributions (unique, first-instance content).

By understanding these signals across document, domain, and entity levels, you can better optimize for E-E-A-T and improve your site’s performance in Google’s search rankings.

If you’re still finding it all a bit overwhelming, check out our monthly SEO packages and let the experts take the reins for you!