What is the most important SEO Ranking Factor?
We get asked a lot of questions as we educate clients and prospects about SEO, and one of the most common questions we get is “what is the most important SEO ranking factor?“. The exact answer can vary from query to query, so “it depends” is often the answer we discuss. If its a local query, there are important local factors. If its informational, then content depth and related terms matter more. If its transactional or competitive search then we see links playing a bigger role. Some queries deserve freshness (or, were recently published). Given all the variables, is it possible to identify one ranking factor that is “most important”.
Surprisingly, yes. And its exactly what we thought it would be.
For most of our clients with competitive search terms, we run a series of deep correlative analysis to identify what Google is telling us is most important from query to query. We look at hundreds of factors across the top 50-100 results to identify what factors are likely influencing the results for a specific keyword or phrase. This also gives us some great benchmarks as to what it will take to earn first page rankings. If we are going to compete for competitive keywords in SEO, we have to at least get on par with first page results. That part makes sense.
Using this approach, we can get a sense if content length or links or any other factors are heavily influencing results. We do see some factors carrying more weight from one query vs another, and knowing this gives us a good idea of how to compete. As we were running these analysis, we started looking at a new metric. That was “the number of factors influenced“. We had a theory that we’d find a strong positive correlation between the number of factors that had been influenced and rankings. We put this to the test on our collection of terms across a lot of campaigns, and we found that on almost all of them, the strongest factor that had the highest correlation to rankings was the number of factors that were influenced.
Google’s algorithms are based on complex math, and the results that the algorithms produce are checked by Quality Raters to determine if the algorithms are producing the desired type of results. This gave rise to the Quality Raters Guidelines which is a series of checks that determine if a website is “quality” and best answers a query. The algorithms are refined by feedback from the Quality Raters. So you can look at search engine optimization as math plus quality. If we understand the math, and we understand the type of quality Google is trying to produce, then we have a roadmap for creating websites and landing pages to meet those requirements.
That part is quite important because if you look closely, it strengthens the case for attention to detail and comprehensiveness being important to the success of any SEO strategy. If the most important factor that best correlates to ranking is the number of factors influenced, then you can see where you shouldn’t focus on over-optimizing any one area. The goal would be to naturally influence as many factors as possible without overdoing it. We want to become a better resource, not simply a website with more keywords on it.
So now on to the data portion. First, we’ll set the boundaries. We look at correlation from a linear and a curved perspective using Spearman’s and Pearson’s coefficients to find areas where correlation is linear or across a curve. A correlation above 0.20 is 95% statistically significant. We also know that correlation does not equal causation, and correlative data has to be vetted against knowledge and expertise, as well as other known factors. Below is the output of the overall analysis.
In this data set, the x-axis represents the Google results page (1 being positions 1-10, 2 being positions 11-20, etc). The y-axis is the number of factors influenced on average per search results page. The Spearman’s Correlation is -0.49 and the Pearson’s Correlation is -0.56, meaning there is a strong correlation as you approach page one that increasing the number of factors you influence will likely increase your rankings. The average on page one was 140 factors influenced, and the tracked websites (our group) averaged 165 factors influenced. A few of the test group sites actually had featured snippets for their terms.
Example factors used include content factors (keyword in heading(s), keyword in first 100 words, number of related terms used, total words used, etc), link factors (number of referring domains, keywords in anchors, partial match anchors, etc), and technical factors (responsive/viewport attribute, schema, open graph, internal links, https, etc). In all, over 300 individual factors were analyzed to determine how likely each factor is to influence rankings.
This is important to understand, because as competition increases, you tend to see more factors influenced in a heavier way – like more in-depth content, more links, and better site structure. The individual query analysis will help focus extra effort where it is needed but the overall strategy would be to influence more factors in a healthy and natural way before pushing the envelope too far on any single ranking factor.
This has been our approach at SEOteric for a while, and we’ve outlined our SEO processes by defining our “Four Pillars of SEO” approach. Taking this approach and looking at it from a ranking factor perspective helps validate some of the ideas and concepts that helped shape this approach in the first place. We could see the impacts as we watched rankings improve, and now we can see a strong correlation between a holistic approach and improving ranking positions.