How Google finds signals through the noise


30-second summary:

  • Google has the ability to measure content quality signals like never before, and with each new Core Update, they understand content quality more as humans do.
  • Google uses different search models depending on the search query, meaning ranking signals can vary depending on search intent.
  • In a recent correlation analysis we did in the sports ticketing industry, the top factors that correlated with good rankings were long-form landing page content and high domain authority.
  • Given Google’s guidance about their forthcoming 2021 web vitals update, page experience and performance are likely going to become more essential for ranking on page one.
  • LinkGraph’s CTO explains how machine learning is helping us better understand how Google finds and evaluates content quality signals when ranking web pages.

What makes a web page rank on the first page of Google? Historically, the best correlation studies regressed thousands of search factors against page one rankings in an attempt to understand the primary drivers of SERP performance. 

But these ranking factors are weighted differently depending on the type of search. Google uses varying search models depending on the search intent behind the question. Local searches with the map pack, higher economic value searches with high CPC, informational queries with high search volume, searches where the most relevant results may be rich media like videos/images, and even search within highly regulated industries like health and money, can all weight ranking factors differently

To make correlation studies even more challenging, Google updates its core algorithm several times a year, meaning those signals continue to evolve. As Google continues to refine its ability to measure and analyze those signals, their bots are getting better at understanding website quality the way that humans do.

Opening Google’s black box and the North star

In the last decade, the academic fields of machine learning and natural language processing have made great strides. Starting around 2012, Google’s search algorithms evolved beyond regression-based models towards deep learning. Google’s ranking algorithm has now become a black box, and even their engineers find it challenging at times to understand why their models produce the results they do. 

Ultimately, we know their goal has always been to bring the highest quality search results to users, and they’ve always done the best they could with the technologies and datasets available to them at the time.

Every core update is consistent with what they have been telling us for years: they reward high-quality pages and penalize low-quality sites and spam. Google’s updates have always been marching in the same direction, and it’s well understood among SEOs that there is a North Star towards:

  1. Higher-quality content
  2. Fast, snappy user experiences
  3. Increasing site authority and reputation

What can be frustrating for webmasters is Google’s lack of specific guidance about which specific ranking factors matter the most in their specific industries? When studying how certain properties like page speed, text content, and backlinks relate to rankings, advanced SEOs are turning to correlation analysis and machine learning tools to better understand how Google finds these signals through the noise.

What we learned about the primary drivers of SERP rankings

We look into an industry-specific correlation analysis, and what we learned about the primary drivers of SERP rankings.

To better understand these signals, we studied the correlation of 18 important ranking factors across 200 searches in the sports ticketing vertical. Each keyword had a CPC of over $8. 

Google finds ranking factor signals content quality signals

What we discovered was that the most important factors that were correlated with top rankings were high Domain Authority and long-form landing page content. Domain organic traffic value, URL organic traffic, and page load speed were among the weaker correlations. 

Unfiltered backlink count and Referring Domain counts themselves were not as strongly correlated as metrics like Moz’s Domain Authority, which differentiates between low-quality links and high-value links. Additionally, Majestic’s Trust Flow and Citation Flow metrics proved less correlated to ranking on the first page then Moz’s Domain Authority.

For the startup client, we were working with, this analysis taught us that maximizing the amount of page rank on their most competitive landing pages was critical in order to contend with the heavyweights in their space, more than anything else they could do for their SEO.

From a content perspective, we found the web pages that rank on the first SERP align with what Google claims to reward: Long-form, topically-rich pages with interactive elements that provide a great page experience, like jump links, expandable content modules, and interactive javascript or videos.

In our analysis, we were surprised to see that page load speeds weren’t as strong of a factor as we might have expected. Nevertheless, given Google’s guidance about their forthcoming 2021 web vitals update, performance signals are likely to become more important. Even though these signals may not be as important as site authority in the algorithm today, I would strongly advise brands to begin preparing for the update and begin making on-page content improvements and page speed improvements right now and understand how your site stacks up in the ‘Chrome User Experience’ report.

What this means for ranking on page one in your industry

If you’re an upstart and looking to break into page one of Google, most of these Google algorithm updates should come as very good news because they create a more democratic SEO landscape. In every correlation analysis, we’ve performed, site authority has always been the most influential factor. It’s also the hardest ranking factor to improve because link building takes such a concerted amount of effort and can take years in big industries. 

Seeing other ranking factors demonstrating strong influence over rankings – such as content length and quality, page speed, and web vitals, and high-quality UX. It means that new entrants gain more opportunities to succeed in SEO on the merits of their web pages, not just because they are incumbents with massive backlink profiles.

Even if you don’t have the tools to do your own comprehensive correlation analysis, you can manually reverse engineer the SERPs in your industry. Studying how your competitors to benchmark on these important search factors can reveal valuable tactical insights that help you direct your team’s SEO efforts towards the most impactful work possible and deliver faster-ranking improvements. 

Manick Bhan is the founder and CTO of LinkGraph, an award-winning digital marketing and SEO agency that provides SEO, paid media, and content marketing services. He is also the founder and CEO of SearchAtlas, a software suite of free SEO tools. He is the former CEO of the ticket reselling app Rukkus.


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