Post by account_disabled on Feb 20, 2024 4:16:16 GMT -5
I have previously done a lot of research on whether there is any content regarding the use of Google Analytics data with Machine Learning algorithms. However, I could not find any content using Machine Learning methods on Analytics data specifically about which parameters provide conversion. Therefore, in this article, I will try to talk about topics such as how Analytics data can be analyzed better using these methods and what insights we can draw from it. Google Analytics is one of the most important data-oriented tools we use to track the behavior of users who visit our site and make our site more useful for them. With Analytics, we can also get information about how many users we can bring to our site from which sources.
Google Analytics presents us the data in an Greece Phone Number aggregated form (we cannot see each user behavior in detail in a list), and this prevents us from performing advanced analysis. In order to know according to which parameters your users convert, it is necessary to use more advanced methods such as machine learning algorithms. Moreover, thanks to these methods, we can predict with great accuracy how close the users who will visit our site in the future are to the conversion channel and take the necessary actions to persuade them to convert.
Data We Will Use In this article, we will use Google Analytics data and the most basic Machine Learning algorithm to find out which parameter has the biggest role in the conversion and to what extent other parameters affect the conversion. You can also follow these steps if you have basic programming knowledge. I will use the R programming language in this article. If the software language you use has ready-made packages similar to the one I will use, your job will be much easier. We will download the data we will use from the User Explorer report under the Audience Tab from Google Analytics. This report includes how many sessions the users who visit our site have made according to their Customer IDs, what their average session duration is, bounce rates and Target Conversion Rates.
Google Analytics presents us the data in an Greece Phone Number aggregated form (we cannot see each user behavior in detail in a list), and this prevents us from performing advanced analysis. In order to know according to which parameters your users convert, it is necessary to use more advanced methods such as machine learning algorithms. Moreover, thanks to these methods, we can predict with great accuracy how close the users who will visit our site in the future are to the conversion channel and take the necessary actions to persuade them to convert.
Data We Will Use In this article, we will use Google Analytics data and the most basic Machine Learning algorithm to find out which parameter has the biggest role in the conversion and to what extent other parameters affect the conversion. You can also follow these steps if you have basic programming knowledge. I will use the R programming language in this article. If the software language you use has ready-made packages similar to the one I will use, your job will be much easier. We will download the data we will use from the User Explorer report under the Audience Tab from Google Analytics. This report includes how many sessions the users who visit our site have made according to their Customer IDs, what their average session duration is, bounce rates and Target Conversion Rates.