The purpose of the article is to give you an understanding of how Google algorithms work, and not to talk about individual factors, their weight and importance or the backstage of a rating system. This is all kept secret.
From the article you will learn:
How Google Search Rankings Work
What are the rating factors?
There are thousands of ranking factors, and Google does not tell us about them in detail. But we can roughly get an understanding of how the Google search engine works.
Google says that they group them: by topic, quality, page loading speed, entities, RankBrain algorithm, freshness of content and structured data.
Of these seven factors, we explain two not-so-clear terms in the way Google’s search engine works: RankBrain Algorithm: Upon receiving a request, RankBrain determines which signals will be most important to provide the best result. For example, for a request (best Christmas presents), the publication time factor will be decisive. At the same time, for such a request as (Holodomor in Ukraine) credibility will be more important than freshness.
Entities in SEO: Google defines an entity as “a unique and well-defined thing or concept.” Entities are important to SEO because they represent the real world in the form of entities / objects and the relationships between them. More on Entity Concepts Important points:
These seven factors are real factors that we can rely on.
Each rating factor has a set of signals. For example, page quality is calculated primarily by page rank, but also includes other signals. Data structure includes not only Schema.org, but also HTML5 tables, lists, and semantics.
Google’s algorithms calculate the page score for each of the ranking factors:
Remember that in the whole article all figures are hypothetical. The diagram depicts 7 known Google factors, the other two are hypothetical How ranking factors affect your bid
Google takes points for individual ranking factors and adds them up to calculate the total score. For this, the term bid is used.
The total bet is calculated by multiplying these points.
The total score after multiplying points for all factors
Caution: a low score for one factor can cut your rating
Any point below 1 will greatly harm your rating, even if all other points are excellent.
An example of a low score for one of the rating factors
Look at how much the rating falls due to a decrease in the score for one factor. This is enough to throw the page out of the competition of competitors.
The numbers below show how important it is for all other factors to be high when one of the indicators dipped. You can’t ignore a weak indicator – you need to work to raise it above unity.
The system rewards pages with good scores for all indicators.
How a rating based on a bid system looks like
Example of rating based on points per bet
Final bid adjustment
Top results in the top ten are sent to the second algorithm, which should output the final rating and exclude all inappropriate results.
This recount may raise, lower the bet or leave it the same. It seems that this filter basically blocks irrelevant, low-quality and unscrupulous content that Google’s original algorithm skipped.
Now we are looking at the final set of bets, which may look something like this:
Final bet list after evaluating the second algorithm
Please note that in this example, one result gets a zero point and therefore is completely excluded. Since we multiply points for indicators, zero for one indicator will lead to the fact that the overall indicator will also be zero.
We now have a list of the final 10 candidates on the first page of search results.
Candidates compete for first place on the page for cumulative results.
Each type of result / extended description competes for a place on the first page of search results.
News, images, extended descriptions, carousels, maps – each item provides a list of candidates at their rates for page 1.
Candidate ranking factors
The combination of factors that affect the rating of the aggregate results of candidates is specific for each, since some factors will be unique in one case and will not be used in another. For example, alt tags that apply to images for the results of one candidate, a site news map that will be counted for news from another candidate.
Weighting factor in the overall results of candidates
The relative weight of each factor will also differ for each candidate, since each of them provides a certain type of information in a certain format.
The goal is to provide the most appropriate user elements for:
place on the page.
For example, the freshness of the content will be a factor with great weight for the News, and the RankBrain indicator will be for the quick response block.
Settlement of bids on the aggregate results of candidates
Bids on the aggregate results of each candidate are calculated in the same way as in the example above: using multiplication and the second refinement Google search algorithm.
Then Google receives a number of candidates competing for a place or several places.
A number of candidates competing for a place or several places in search results
Competition for a place on the first page
Candidates compete in aggregate results. It seems that the rules for victory are different for each candidate.
The rules used in this table are intended to be inconsistent with how Google actually does this.
Google is looking for any advanced result that will provide the best solution for the user. When it determines the best candidate result, a place is assigned to this result due to one or more classic blue links.
Final selection of advanced items on page 1
Each candidate’s cumulative result has certain limitations, and they all obey the traditional classic blue links.
One result, one possible position (extended description, news)
Many results, many possible positions (images, videos)
Many results, one possible position (news, carousel)
The winners from the table above are:
Images: We have one winner who exceeded the result of web links in terms of image scores. Video: two winners who outnumbered the result of web links in column 2. Expanded description: here are three winners who outnumbered the result of web -link in the column.
Ranking example for the first search page
Since places are given for extended descriptions, lower rank web results go to page two.
A little theory in the end
Natural selection in search results
It seems that some extended descriptions will naturally grow and will increasingly gain space on the first page.
Other elements will naturally appear less frequently, and some will die out completely. All in the spirit of natural selection 🙂 This system is not going anywhere in the near future.
Google’s principle of ranking extended descriptions will expand and adapt to changes in response delivery.
Google can create a new advanced element, add it to the system and let it compete for a place. He will win a place in the search results if he is a more suitable option than the classic blue links.