Tags: Advanced

The last article on this blog was published almost a year ago — on July 30. It’s not that I didn’t have anything to write about, but somehow I just never got around to it. Meanwhile, the desire to share knowledge has been building up all this time, and in early April, I finally sat down to write a new post. It’s still in drafts. It can wait. But today’s topic? It can’t wait. In my opinion, it’s high time everyone dove into it — because this is where the future of analytics is headed. I’m talking, of course, about the new version of Google Analytics 4 (formerly App + Web) and the setup of Ecommerce.

One of the main problems for marketers and web analysts is the large number of tabs users have open in their browsers. These tabs prevent visitors from focusing solely on our content. That’s why marketers come up with different “tricks” to keep users’ attention. This is an even bigger problem for analysts: with so many tabs open, you can’t be sure that the user spent the entire time — from page load to a certain moment — actually on the page.

Google Tag Manager is a very useful tool, and it can be used for more than just setting up data collection in Google Analytics 4. For example, dynamically replacing content on a website is also a matter of just a few dozen clicks in GTM. There are many reasons why you might want to replace content dynamically on a page. Below are two of the main ones: Replacing data to track the traffic source from which the user arrived. This is similar to how call-tracking services work. Replacing content as a way to provide personalized information to different user segments.

When working with large volumes of information, you often need a programming language — in my case, it’s R — to collect, prepare, and process data. At first, the desktop version of RStudio and the taskscheduleR package were more than enough for me to automatically run scripts on my personal computer. But over time, the number of scripts increased significantly, and all updates became dependent on whether my computer was turned on. That’s when I started thinking about installing R on a server. For my needs, I decided to use a Google Cloud Platform (GCP) server.