Lily Ray is VP of SEO Strategy at Amsive Digital, and one of the more data-driven voices working in search. When an algorithm update rolls out, her analysis tends to arrive early: who won, who lost, and what appears to have changed. The work bridges a gap most commentary never crosses. On one side, Google's public statements about E-E-A-T, meaning Experience, Expertise, Authoritativeness and Trustworthiness. On the other, what visibility data shows about how those signals move rankings. She does not guess at what Google rewards, she measures it and then publishes the measurement.
Who They Are
Ray built her reputation on methodical, transparent analysis of Google's core updates. While an update is still rolling out, she is tracking which content types are gaining visibility, which are losing it, and what the two groups have in common. That combination of live commentary and slower, more careful analysis underneath it is what makes her one of the first people the industry checks when the rankings start moving. Different roles use the same material differently. A technical SEO correlates their own drop against her datasets to work out which update type hit them, while a marketer uses the findings to make an internal case for investing in content quality.
What distinguishes the approach is a refusal to settle for anecdote. When the rest of the field says E-E-A-T matters, she produces charts showing the relationship between specific E-E-A-T signals and SERP volatility. When the industry argues about what helpful content means, she audits pages at volume and shows what the algorithm appears to have favoured. It is slower work than having an opinion, and it ages better.
She also specialises in YMYL sectors, which stands for Your Money or Your Life: finance, health, legal, medical. These are the areas where Google applies its strictest standards for trustworthiness, and her work in them keeps showing that the threshold for authority is genuinely higher there. A financial advice page cannot simply be competent. It has to demonstrate unmistakable expertise and trustworthiness, or it will not rank, however well written it is. The net reaches further than the classic verticals suggest, too. An ecommerce store selling supplements or skincare, or a B2B company trading on claimed expertise, gets judged against a version of the same standard.
Her particular strength is seeing the patterns that are not obvious from the raw data. She does not just report that this content won and that content lost. She digs into why: what specific signals did the winners have, what was missing from the losers, and how do those patterns differ from one industry to the next. That second layer is what makes the work actionable rather than merely descriptive.
What They Teach
Ray teaches E-E-A-T signals analysed through concrete, measurable data. This is not theoretical. The subject is the relationship between specific on-page signals and how a site performs when the algorithm changes underneath it. Content quality benchmarking sits at the centre of it. What does helpful content actually look like in the rankings, how does featured snippet content differ from ordinary ranking content, and how do topical depth and comprehensiveness vary across categories and YMYL verticals? Those questions matter practically, because getting the answer right shapes your whole content strategy, and she answers them from analysis rather than from instinct.
Algorithm update impact studies are her primary output. She runs comparative analyses: which content gained visibility after an update, which lost it, and what the gainers have in common. Her case studies are quantitative, showing correlations rather than just telling stories. The markers she works with are the ordinary furniture of a credible page: author bylines carrying real credentials, publication dates, freshness signals, source attribution. The analysis asks how each of those tracks against ranking movement. Pages with clear author expertise behaving differently from anonymous or unverified ones is a recurring finding in the YMYL work. That level of specificity is rare, because most analysis stops at the general. Hers breaks down by sector, by content type, by intent category.
The second pillar is YMYL-specific trust standards, and the pattern that keeps repeating across sectors is the same one. Content from a credentialed practitioner performs differently from content on the same subject written by a generalist, whether the field is medicine, finance or law. These are not opinions. They are patterns in the data, documented with the methodology attached so that anyone can see how the conclusion was reached. For anyone working in a trust-sensitive sector, that transparency is the reason the work is usable at all.
She also teaches the relationship between data and narrative. Not all updates hit all sites equally. Some sectors see major shifts while others stay flat, and some content types suffer while others benefit. Understanding why means looking at the specifics of what changed, not at Google's headline announcement. That habit of thinking stops you applying generic SEO logic to an industry context where different rules are clearly in force.
How It Maps to Opportunity and Authority
Lily Ray's work is heavily Authority-weighted. Almost all of it applies to understanding and building Authority, for a straightforward reason: her entire focus is on what Google treats as high-quality, trustworthy and expert. The data analysis reveals which signals Google uses to evaluate authority. The YMYL work shows how to build credibility where trust is scarce, and the E-E-A-T analysis demonstrates how expertise gets measured and ranked.
The value to the O+A framework is that she supplies a data-backed answer to a question most people answer from feel. What does Authority actually look like to Google's algorithm? Instead of following received wisdom, you can follow the practices the data says are rewarded. This is diagnostic work. If your authority assessment shows you losing visibility whenever an update lands, her analysis helps you work out why and what to fix. Are the E-A-T signals missing, is author credibility thin for your vertical, or is the source attribution too weak to carry the claims? The case studies show how those specific factors correlate with performance, so you are working from evidence instead of from a hunch.
Inside the Authority side of the framework, her work helps you diagnose whether your problem is existential or a matter of signalling. Existential means you lack the underlying expertise or credibility. Signalling means you have the credibility and are not communicating it clearly enough for an algorithm to read. That distinction matters, because the solutions are completely different. A signalling problem might be resolved by fixing author bylines and credentials markup. An existential one is not fixed by optimisation at any price, and no amount of markup substitutes for expertise you do not have. Her analysis helps you tell the two apart by showing what credible content in your sector actually looks like.
The smaller Opportunity component comes out of her work on formats and structures that are currently rewarded. When she analyses featured snippets or top-ranking content, patterns emerge about organisation, length, section structure and comprehensiveness. Those patterns help you see which Opportunity formats align with what is being rewarded in your sector right now. There is a recovery angle too. When a quality issue costs you visibility after an update, a clear diagnosis of what the update targeted tells you which corrective work, often content work, wins that lost Opportunity back.
The Strategy Breakdown
Ray's core methods each pull on the two levers differently. Here is the split, so you know what each one buys before you commit the time.
E-E-A-T and content quality analysis
Auditing content and off-page factors against Google's E-E-A-T guidelines to find specific strengths and weaknesses, letter by letter rather than as a slogan. Experience means genuine testimonials, case studies, evidence you have actually used the thing. Expertise means qualified authors, reputable sources cited, and detail that is accurate rather than merely plausible. Authoritativeness means brand reputation, mentions from known authorities, recognition you can point at. Trustworthiness means HTTPS, clear contact and about information, transparent policies and a managed reputation. Opportunity impact: moderate, and indirect. Content that demonstrates strong E-E-A-T tends to be more comprehensive and more genuinely helpful, which makes it relevant to a wider set of related queries and better at satisfying the intent behind them. Authority impact: very high. This is the centre of authority-building, because the audit turns a vague credibility problem into a list of specific, fixable gaps.
Winners-and-losers update analysis
Tracking visibility data across large numbers of sites after a major update, whether that is a core update, a helpful content change or a product reviews change. Then identifying the patterns that separate the sites that gained from the sites that lost. The findings ship as data-rich case studies with the methodology attached. Opportunity impact: moderate, contextual. Seeing which content types were rewarded highlights formats worth pursuing, and seeing which were hit tells you whether your own Opportunities carry the same risk. Authority impact: very high. This is empirical evidence linking specific quality characteristics to shifts in how Google perceives authority. It moves past theoretical guidelines to what actually happened, and it tells you which E-E-A-T factors to prioritise based on what the recent updates punished and rewarded, so the fixing happens before the next one rather than after it.
YMYL scrutiny
Applying extra rigour to sites in health, finance, legal, safety and news, where Google holds the quality bar significantly higher because inaccurate information causes real-world harm. Opportunity impact: low, directly. This work is not about finding new Opportunities. It is about clearing the higher bar on the ones you already hold. Authority impact: extremely high for relevant sites. In these niches, exceptional E-E-A-T is closer to a requirement than an advantage. Expert authorship, alignment with scientific consensus where it applies, strong sourcing, unambiguous trust signals: miss those and rankings do not soften, they collapse. That applies whether you run a hospital or a supplements store.
Benchmarking against the winners
Comparing your own site and content against the sites that performed well after the updates relevant to your sector, then running quality audits against the criteria those updates appear to have tested. Opportunity impact: moderate. Post-audit improvements to relevance and helpfulness can win back rankings an update took away, which is recovered Opportunity rather than new Opportunity. Authority impact: high. It converts published analysis into a repeatable practice for your own site: a concrete picture of what credible looks like in your vertical, and a gap list between that and what you have actually built.
When to Learn From Them
If your Opportunity and Authority diagnostic shows Authority problems, Ray's work is essential reading. When you know there are trust or credibility issues but cannot name the specific signals to address, her case studies show the path. If a core update or a helpful content update has hit you, her analysis of the winners and losers in your sector helps explain what changed and why your site was caught by it. That matters, because updates routinely shock the industry with unexpected winners and losers, and her analysis is what turns a surprise into a pattern.
Learn from Ray if you operate in a YMYL sector: finance, health, legal, wellness, or anywhere trust is not negotiable. To my eye her work on trust standards in these verticals is the most useful in the field. The stakes are genuinely higher, since a ranking loss in YMYL can mean real harm when people act on bad financial or health advice, and Google's algorithms reflect exactly that. The standards are tighter, and her work shows what they look like in practice and how to meet them.
If you want to understand what Google means by E-E-A-T rather than relying on your own interpretation, her data-backed definition is the clearest I have found. She shares that territory with Marie Haynes, and the two divide it cleanly: Haynes interprets what quality and trust mean inside Google's guidelines, while Ray measures what large-scale visibility data shows when the updates actually land. If trust is the problem, read both. If you believe in evidence-based decisions and want to move past opinions and frameworks to what the algorithm rewards, her methodology is the template. The process, the data sources and the limitations are stated openly enough for you to run the same analysis yourself.
Learn from her too if you are building content in a sector where your authority is contested or thin. Being new to a vertical, or representing a brand less established than its competitors, makes the question of how to signal expertise and trustworthiness an urgent one. Her work shows what the established authorities are doing differently, which is the most direct guide to your own authority-building there is.
Where to Start
Begin with her real-time commentary during an update rollout. Follow the threads where she breaks down which content types gained or lost visibility and what the winning pages had in common, because that is where the most immediate analysis appears. The discussion around it is often valuable in its own right, since other practitioners add sector-specific observations that extend the findings.
Next, look for her conference presentations. These typically carry the full dataset and the comparative analysis behind the headline, and she is generous with methodology, which means the analytical approach transfers to your sector even where she has never published on it directly.
Her longer-form research lands as case studies: the comparative analyses of algorithm winners and losers, and the YMYL-specific guidance. This is where the work goes once it is polished, so it is worth checking back after each round of Google updates rather than only during one.
If you work in a specific vertical, look for the analysis nearest to it. Trust-heavy industries get the most attention in her published work, so use whatever covers your niche as the benchmark for which Authority signals matter most. If nothing covers your vertical directly, the methodology from an adjacent one translates well enough to apply to your own data.
Part of the Expert Series. Back to the framework or the diagnostic. Part of the Marketing Universe. Explore Traffic Plus Offer : The Trust Algorithm : 4-Quadrant AI. Read the book: Marketing Curious: Working the Noise.