For years, SEO practitioners have wrestled with the puzzling concept of E-E-A-T – experience, expertise, authoritativeness, and trustworthiness – often simplified to vague advice like “build your brand.”
However, behind this public-facing narrative is a sophisticated web of signals that Google evaluates to determine quality, trust, and authority.
Based on over eight years of research into 40+ Google patents and official sources, I’ve identified more than 80 actionable signals that reveal how E-E-A-T works across document, domain, and entity levels.
It’s time to unpack these insights and understand how Google’s algorithms weave relevance, pertinence, and quality into search rankings.
The big misunderstanding about E-E-A-T
Many SEOs mistakenly believe that E-E-A-T has little impact on rankings and dismiss it as a buzzword.
However, Google often uses terms like “helpful content” or “E-E-A-T” as public-facing narratives to frame its search product positively.
These labels encompass numerous independent signals and algorithms working behind the scenes.
To implement E-E-A-T, Google identifies and measures various signals, building a framework that algorithmically promotes trustworthy resources in search results and scales quality evaluations.
This process could also influence the selection of resources for training large language models (LLMs), highlighting the importance of understanding and optimizing for E-E-A-T.
Notably, E-E-A-T is never explicitly mentioned in Google patents, API leaks, or DOJ documents.
Instead, my research focuses on sources that address quality, trust, authority, and expertise – concepts essential to grasping E-E-A-T’s role.
Relevance, pertinence and quality in search engines
Before exploring the researched E-E-A-T signals, it’s important to understand the distinctions between relevance, pertinence, and quality in information retrieval.
Relevance
This refers to the objective relationship between a search query and the corresponding content. Search engines like Google determine this through advanced text analysis.
Ranking factors for relevance evaluate how well a document aligns with search intent, including elements such as:
Keyword usage in headlines, content, and page titles.
TF-IDF and BM25 scoring.
Internal/external linking and anchor texts.
Search intent match.
User signals through systems like DeepRank and RankEmbed BERT.
Passage-based indexing.
Information gain scores.
Pertinence
This concept introduces the human element into search results, representing the subjective value of content to individual users.
It recognizes that users searching for the same term may find different levels of usefulness in the same content. For example:
An SEO professional searching for “search engine optimization” expects advanced technical content.
A beginner searching for the same term needs foundational knowledge and basic concepts.
A business owner might seek practical implementation strategies.
Relevance and pertinence are primarily managed by Google’s Ascorer/Muppet system for initial ranking and Superroot/Twiddler for ongoing reranking.
Relevance is assessed through Scoring, which assigns numeric values to quantify specific properties of input data.
Quality
In search engines, quality operates on multiple levels, serving as an evaluation metric for entities, publishers, authors, domains, and documents.
It is assessed by systems like Coati (formerly Panda) or the Helpful Content System, which evaluate quality at the site, domain, and entity levels.