There are ways to understand how the variables you may encounter along the way work, as well as how to understand and minimize the impact of differences in the computed data. This is what we will cover in this article.
Operating principle of web analytics tools
Using Google Analytics 4 as the main example, there are four main 'characters' that deserve our attention and are the stage for the linearity of the data:
Application server: the one that provides the page that will be loaded;
Client browser: which uses Java Script code to “observe” user behavior and send it to the next server;
Google Analytics Server: A raw Google database that receives and stores browser information;
GA Report: where the data is consumed and the database is queried, with the possibility of applying filters and displaying them in report format.
Along the way, there are some factors that can cause the data to reach you differently and end up harming your team's analyses.
Points that generate changes
One of the main points that can cause this discrepancy between the data is the direct russia whatsapp number dependence that the main Analytics tools have on Java Script, a technology that depends on the user's browser to function, which causes some factors to limit or hinder the sending of information.
It is also worth mentioning that there is a dependence on cookies, which causes some data to become inflated due to browser mechanisms that limit the number of days this information exists.
Another important point is that, due to the LGPD, which requires the website to request authorization for the user to be monitored throughout their journey, a percentage of its users may reject this, affecting the web Analytics data, but maintaining the record in the back-end tool.
Other variables that cause discrepancies between backend tool data and Web Analytics:
Filters applied to GA reports;
Cancellations and refunds reflected in the backend, but not in Google Analytics;
Page reloading that causes one more interaction to be counted;
Errors in the data layer that affect accounting in the new version of GA.
A viable path to more reliable analysis is to export sales data from a given period in both tools and combine them in a spreadsheet for accounting analysis, in order to understand the percentage of discrepancy.
Those that have a glaring difference (>10%) may indicate an implementation error that should be corrected, but it is still important to remember that it is common for one piece of data or another to not be 100% the same.
The difference within does not necessarily mean that everyone points out errors in the implementation. One must understand how the tools work differently, analyze them in depth, understand the data and have confidence in it.
Want to understand more about these differences? Watch the full video in our Google Analytics 4 playlist.