E-E-A-T, author pages and quality signals

0:00 E-E-A-T, author pages and quality

Imagine two articles about managing your savings during high inflation. One is by Team Editorial, with a stock photo. The other is by a named, chartered financial planner with fifteen years of experience, links to her professional registration, real examples from her practice, clear sources, and a note saying it was reviewed last month. Which do you trust with your money? That instinct is exactly what Google's quality framework describes. In this lecture, you'll learn what E-E-A-T really is and isn't, the four components, YMYL topics, Google's who, how and why questions, and how to build author pages, bylines, markup and an editorial policy that prove it.

0:47 What E-E-A-T is

E-E-A-T stands for experience, expertise, authoritativeness and trust. It comes from Google's search quality rater guidelines, used by human raters who evaluate search results to help Google test and improve its systems. Key points. E-E-A-T is a framework for assessing quality, not a single ranking factor or score. Google has said its systems use a mix of signals that align with what raters would consider E-E-A-T. Trust is the most important element, and experience, expertise and authoritativeness support it. Think of it like a doctor's office. The certificates on the wall matter. But what you really trust is that the doctor is honest, careful and has seen cases like yours.

1:35 YMYL + the four components

The bar is highest for YMYL topics: your money or your life. That means health, finance, safety, legal and civic information, where poor content could harm people. Now the four components. Experience: has the creator actually used, done or lived this? First-hand photos, original test results, case studies. Expertise: does the creator have the knowledge or skill? Qualifications, professional role, track record. Authoritativeness: is the creator or site a go-to source? Citations by others, reputable coverage, recognition. And trust: is the page accurate, honest, safe and reliable? Clear sourcing, transparency about who's behind it, secure checkout, policies and corrections.

2:18 Who, How, Why

Google's guidance on helpful content suggests three questions. Who created the content? Make it self-evident, with bylines and author pages. How was it created? Explain your methodology, testing and, where appropriate, how automation or AI was used. Why was it created? Primarily to help people, not to manipulate rankings. And a clarification about AI. Using AI to help create content is not against Google's guidelines. Using automation to mass-produce pages primarily to manipulate rankings is, and Google calls that scaled content abuse. Where readers would reasonably expect to know how content was made, disclosure helps trust.

3:00 Author pages that work

Author pages that work. For every author and expert: name, headshot and role. A short bio with specific experience, like has run technical SEO for more than forty e-commerce migrations. Credentials with verifiable links. Areas of expertise. Links to official profiles and professional bodies. Selected publications, talks and coverage. And a list of their articles on your site. Then add Person structured data, and reference it from each article's author field, with your Organization as the publisher. Remember, markup doesn't create expertise. It describes it. The underlying reality, real experts with real experience, is what matters.

3:42 Site-level and off-site trust

Site-level trust. An about page with the real people and organization behind the site. Contact information that works. An editorial policy explaining how content is researched, reviewed, updated and corrected, and how AI tools are used. A genuine review process for YMYL, like medically reviewed by a named doctor, only if it really happened. Customer service and policies, especially for e-commerce. Transparent monetization, with affiliate and sponsored labels. And honest dates. Updating the date without updating the content is a trust failure. Off-site matters too. Raters are asked to look for independent information: news coverage, reviews, awards and professional bodies. That's where digital PR supports quality directly.

4:29 Worked example 1: UK finance blog

Worked example one, simple. A personal finance blog in the UK has articles by the editorial team. The fixes: every article gets a named author linked to a profile page with real credentials and experience. Articles on pensions and tax are reviewed by a qualified professional, named, with their registration linked. Sources are cited in the text. An editorial policy explains how AI helps with outlines and editing, and that every article is written or substantially edited, fact-checked and approved by a named person. And the updated date changes only when the content actually changes.

5:10 Worked example 2: dental group (illustrative)

Worked example two, a business scenario with illustrative details. A dental clinic group in Riyadh publishes patient guides in Arabic and English. An audit shows that guides are anonymous, some advice is outdated, and a medically reviewed label appears on pages no dentist actually reviewed. That last one is a serious trust problem. The team removes the false labels immediately, assigns each guide to a named dentist who reviews and signs off, adds author pages with licenses and experience, updates outdated advice, and publishes an editorial policy in both languages. Patient questions to reception about the guides drop, and the guides start appearing in local health coverage.

5:57 Watch me do it: author system + gap audit

Watch me do it. I create a profile page for our head of technical SEO at a stable URL, with a real headshot, a specific bio, credentials and official profile links. Then I add a byline block to every one of her articles: name, role, a one-line credential, published date, and an updated date with a note on what changed. I add Person markup with an at-id and reference it from each article's author field. Next, I paste one article and its author box into an AI assistant and ask it to list what a skeptical reader couldn't verify, using Google's who, how and why questions, without inventing credentials. It points out our testing method isn't described. I add a method section with real screenshots.

6:51 Common mistakes and measures

Common mistakes. Fake author personas or stock-photo experts. Reviewed by labels without real review. Updating the date without updating the content. Treating E-E-A-T as a checklist of schema tags. And publishing AI-generated articles at scale with thin human input. How do you measure success? The share of articles with a named author linked to a complete profile. YMYL articles with documented expert review. Pages with accurate updated dates. And over time, searches for your brand and authors, citations of your experts, and how your key informational pages perform after quality improvements.

7:31 Recap and try this now

Let's recap. E-E-A-T is a quality framework from the rater guidelines, not a single ranking score, and trust matters most. YMYL topics need the highest standards. Make who, how and why obvious, with author pages, verifiable credentials, bylines, Person markup and an editorial policy that explains how AI is used. Build site-level trust, and let digital PR build the off-site reputation raters look for. Try this now. Audit one article against the six-question E-E-A-T review in the lesson, then rewrite its author box and build or improve the author's profile page.