E-E-A-T

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. It is the framework Google's human Search Quality Raters use when evaluating pages, as documented in the public Search Quality Rater Guidelines. The original framework, E-A-T, was expanded in December 2022 with the addition of Experience as a first-class signal.

E-E-A-T is not a single algorithmic score. There is no "E-E-A-T score" to optimise directly. Instead, it is a conceptual rubric that informs how Google trains and tunes its ranking systems, particularly for Your Money Your Life (YMYL) queries — topics where bad information could harm a user's finances, health, or safety.

Each pillar has a specific meaning. Experience: has the author actually used the product, lived the situation, or done the thing? First-person account, photographs, original measurements, dated screenshots — these are experience signals. Expertise: does the author have the formal or demonstrated skill to speak credibly? Author bios, credentials, publication history. Authoritativeness: is the author or site recognised by others in the field? Inbound editorial citations, mentions, Backlink from authoritative sources. Trustworthiness: is the information accurate, transparent, and safe? Citations, HTTPS, clear authorship, corrections policy, contact details.

Trust is the foundational pillar — Google states explicitly that a page can have strong Experience and Expertise yet still fail if Trust is weak. Untrustworthy financial advice, undisclosed sponsorship, fabricated reviews, or anonymous medical claims are all Trust failures.

Practical implementation: add real author bylines with linked bios, cite primary sources, publish a clear About page, surface authorship via Schema Markup (Article and Person), maintain dated content with last-updated stamps, and earn editorial links from recognised industry sites. These actions individually are small. Cumulatively, they build the surface area that Google's quality systems are designed to detect.

E-E-A-T matters most in YMYL niches but applies everywhere. Sites that consistently win post-Helpful-Content updates tend to be sites that look credible to a skeptical human reader — which is exactly what the rater guidelines optimise for.