The basic idea is to “do like Google”, which is to go to the root to understand what makes a web page relevant for a search. The task is to “decode” the choices that the Mountain View search engine makes.
The approach, therefore, starts from the SERPs, patiently cataloging all the URLs and keywords of a given semantic area.
Why the semantic area? Because every customer lives on the web in a specific ecosystem made up of topics, competitors, and particular researches. Just as Google treats searches from different areas differently, we also need to analyze that particular ecosystem.

Our analysis allows us to create 3 very important Machine Learning models:
- identify which URLs deserve the first page and for what reason
- identify the Search Intent behind each keyword
- identify which topics and words are decisive within the content
- keep track of all ongoing changes in Google SERP using the best Google Rank Tracker
To do all this we have analyzed more than 500 factors that we take directly from SERPs and Web Pages.
The result is a clear and objective vision of which are the most important areas for good positioning.
The model should report categories and individual ranking factors by identifying their relative importance. This allows to prioritize the actions that can actually make the difference between the first and second page.
Furthermore, from such a model we can identify which thresholds are necessary for the individual actions to be performed, facilitating the task of those who put their hand directly to the site.
While for the contents we can both analyze the content of every single page and consequently which topics and words are important to use for each keyword.