SEO Research Lead

Marcus Veltrino

“Without data, you’re just another person with an opinion.”
— W. Edwards Deming

Marcus Veltrino designs the experiment frameworks that underpin every ranking test published on KatvTech. His background is in software engineering, where reproducibility is not a preference but a baseline requirement. That same standard applied to SEO research is what KatvTech’s methodology is built on.

Marcus Veltrino

 

How Marcus approaches SEO research

Most SEO practitioners learn by doing. They publish content, watch rankings move, and draw conclusions from the outcome. Marcus does the same thing, with one difference: he changes only one thing at a time and waits long enough to be reasonably confident the result was not caused by something else.

That discipline comes from software engineering, where shipping a build with multiple simultaneous changes and then trying to diagnose which change caused a production error is considered a rookie mistake. Marcus brought the same thinking to SEO in 2023 when he started running his first controlled ranking tests on personal projects. The results were inconsistent with the advice he had been reading. That inconsistency became the foundation of what KatvTech publishes today.

 


What Marcus focuses on at KatvTech

Marcus leads all experiments in the Ranking Experiments category. His particular areas of focus are:

  • Schema markup impact. Whether and by how much specific schema types influence ranking speed, People Also Ask appearances, and AI Overview citation rates on new and established domains.
  • Internal linking architecture. How different internal linking structures, specifically pillar and cluster versus flat linking, affect the speed at which new content earns organic visibility.
  • Google core update pattern analysis. Identifying the consistent signals across winning and losing sites after each confirmed update, separating observable patterns from community speculation.
  • Indexing speed variables. What technical and structural factors influence how quickly new content on a new domain gets discovered and indexed by Google’s crawlers.

 


Marcus on the current state of SEO research

The SEO industry has a publishing problem. The incentive structure rewards confident advice over honest uncertainty. A post titled “The Definitive Guide to Ranking in 2026” gets more clicks than “Here Is What We Tested, Here Is What We Found, Here Is Why You Should Be Cautious About Applying It To Your Situation.” The first post is almost always less useful than the second.

Marcus’s position is that SEO should be treated as a testable discipline with documented methodology, reproducible conditions, and explicitly stated limitations. Not every question has a clean answer. Not every experiment produces a clear result. Publishing the ambiguous results alongside the clear ones is part of what makes the research credible.

 


Experiments led by Marcus Veltrino

Every experiment article Marcus has authored includes the full five-part methodology: hypothesis, setup, metrics, results, and limitations. His articles are listed below. Each one documents a specific variable, the conditions under which it was tested, and the outcome measured through Google Search Console over a defined period.

If you want to suggest an experiment for Marcus to run, use the contact page. Submissions that include a specific hypothesis and a practical application are prioritised over general topic suggestions.


See Posts from Marcus

What Is Glue Google System? The Universal Search Layer

Classic web results are only one piece of what a Google search results page actually shows, images, videos, local packs, and other verticals all appear alongside the ten blue links, each requiring its own ranking logic. What is Glue Google system, in short: it is the…

How to Write an SEO Case Study People Actually Trust

The industry is full of SEO case studies claiming a single tweak tripled traffic overnight, and most of them share a common flaw: no control group, no measurement window discipline, and no acknowledgment of confounding factors. A genuinely credible seo case study…

Data Nosnippet: Controlling What Google Quotes

Most technical SEO tools control whether a page gets indexed at all. Data nosnippet does something narrower and, for the right situation, more useful: it lets you keep a page fully indexed and ranking while blocking specific chunks of text from ever being quoted back…

HCU Recovery Signs: What Genuine Progress Looks Like

A site hit by the helpful content system, now folded into Google’s broader quality reassessments rather than run as a fully separate classifier, often shows a confusing mix of small ups and downs during recovery, which makes distinguishing real progress from noise…

Keyword Cannibalization: How to Spot It in Search Console

Keyword cannibalization happens when two or more pages on your own site compete for the same search query and the same search intent, so Google keeps switching between them instead of consistently ranking one strong page. The clearest place to spot it is your own…

The Google Leak: A Complete Guide to What It Revealed

In May 2024, over 2,500 pages of internal Google API documentation, detailing more than 14,000 potential ranking attributes, became public. The google leak remains the single largest, most detailed unofficial look at Google’s ranking infrastructure ever released, and…

Twiddlers Google Ranking Explained: The Re-Rankers

Most SEO discussion treats Google’s ranking process as one algorithm producing one ordered list. The leaked API documentation revealed something closer to a pipeline: an initial ranking, followed by a whole layer of adjustment functions applied just before results are…

How Algorithm Updates Changed Gaming Content Rankings

Gaming content rankings changed sharply between 2022 and 2024, as Google’s helpful content updates and the March 2024 core update shifted visibility away from generic, aggregated content and toward pages showing genuine first-hand experience. Gaming sites built on…

Sports Content AI Overviews: Why Predictions Don’t Get Cited

The sports content AI Overviews tend to cite is factual rather than predictive. Questions with stable, checkable answers, such as records, rules, standings, and how a scoring system works, are the queries where AI-generated answers most often appear and where sports…

Navboost Engagement Signals: Why Some Dog Content Ranks and Some Doesn’t

Navboost engagement signals are the user interaction data, mainly clicks on search results, that Google’s Navboost system uses to refine rankings for specific queries. Navboost was described publicly during the US Department of Justice antitrust trial against Google…