Significance, advantages and disadvantages of correlation dependencies in SEO

Today we will talk about correlation dependencies in the field of SEO promotion, and also try to determine the degree of their significance in the framework of search strategy optimization.

SEO is a direction within which the performance indicators of the measures taken are revealed by analyzing the actual ranking factors in search engines. This view is shared by most SEOs.

The share of common sense in such an approach is really present, because no one can say with certainty what is necessary for the productive promotion of the site in the TOP-5 search results. However, one cannot categorically trust any discovered correlation dependence. At least, without a thorough study of all causal relationships, this is certainly not worth doing.

In this article, we will analyze the most popular myths about correlation dependencies in SEO. The second part of the article will be devoted to the analysis of techniques for the correct use of data of this type. So let’s get started.

Relationships in SEO Research

What is the essence of performing the so-called correlation analysis? A large amount of search results is taken (we are talking about the study of one or even several regions), after which they are studied in detail. The result of the study usually sounds like this:

Upon completion of the analysis of 50,000 (for example) search results and parallel analysis of the actual ranking factors (they were previously distributed into groups A, B, C and D), we identified specific correlations demonstrating the relationship between the characteristics of search engines (related, for example, to groups A and C) and high positions of the site in the search results.

At the same time, the result of the study (most often) indicates the need for urgent optimization of the site / web page for these specific factors.

Myths and factual evidence: we understand what’s what

1. Correlation dependence does not indicate the direction in which the discovered relationship effectively functions.

Clarification: correlation does not help us figure out what influences what: feature A on the ranking process, or the first lines in the results on feature A.
An illustrative example is the number of reposts on the social network Facebook. Can it be argued that search results that rank well on Google get the maximum number of shares on Facebook due to their repeated exposure to a wide target audience? Probably yes. However, this cannot be proven today.

Objective findings:

• correlation is not direct evidence that Facebook reposts have an impact on Google rankings (perhaps it is search algorithms that determine the dynamics of reposts);
• it can also be assumed that there is an additional (third) factor that affects the above parameters;
• it cannot be ruled out that in practice there is no connection between them at all.

2. Correlation is not a reason, but just an SEO guideline. That is, we are talking about obtaining data for those aspects of search engine promotion that need to be focused on in the first place.

3. SEO correlation is information that definitely deserves attention. It does not prove anything, it does not indicate a direct relationship. However, a subsequent study of the potential of the identified hypotheses should in any case be carried out.

4. Correlation can indicate the successes and shortcomings of competing sites. Let’s go back to the Facebook repost example. In this situation, in principle, one should not talk about direct / indirect ranking factors in Google. The correct approach lies in the correct processing and productive use of a third-party resource base (in our case, it is the result of the study “sections of the site that received the maximum number of reposts on Facebook function better than its other pages”).

What should be done:

• find out how / with the help of what competitors reached the TOP;
• develop a strategy for introducing conversion-viral content (text content, video materials, etc.) to your site in order to promote it within organic search results.

5. Correlation is not the most effective practical tool. That is, the results of this type of analysis cannot be considered as an objectively formed technical task.

What problems can be solved using correlation dependencies?

1. To identify structural elements that function on top sites / web pages.

2. To track changes in the process of increasing / decreasing the influence of certain factors within the framework of the relationship of interest to us. Example: we are evaluating the effectiveness of external link mass in order to study its impact on the quality of ranking of our site.

If we find a significant drop in the correlation coefficient, then we will put forward the following hypothesis: “There was a need to re-examine the influence of the reference material.” What will it give us?

We can understand:

• whether the impact of external link mass on ranking has really decreased;
• or one should speak exclusively of a decrease in the correlation dependence.

3. To correctly compare sets of search results in order to identify indicators that really affect the ranking process.

Example

Starting data:

• parameter in question: site domain authority;
• researched Internet niches: SEO-promotion and floristry.

For the first niche, this indicator will be more significant due to the specifics, purpose of web sites, as well as the information content necessary for their high attendance. In the case of floristry, domain authority does not play a decisive role, so small and young web resources are more likely to get into the TOP for a limited period of time.