Updated: May 5th, 2021. When I first heard about Google Tag Manager (GTM) in early 2013, I felt a bit confused. At that time I had no idea that tag management was a thing at all, so naturally, I found myself questioning what is Google Tag Manager? What’s the difference between Google Analytics events and GTM events? And nowadays I actually […]
If your website is using some no-name video player, there is a very high chance that this is a generic HTML5 video player. If you want to track its engagement with Google Tag Manager, you will need to do some additional configuration. In this blog post, I will show how to track HTML5 video player […]
Even though Google Analytics 4 offers some built-in video tracking capabilities, there might still be some cases where you will need to do additional configuration in Google Tag Manager. And I wanted to cover different situations, from simple ones to more complex ones. In today’s blog post, I will show you how to track videos […]
In Universal Analytics, a goal (a.k.a. conversion) was counted once per session. If a visitor completes the same goal multiple times during the same session, it will be counted only once. In Google Analytics 4, on the other hand, conversions are tracked every time they occur (regardless of whether it’s the same session or not). […]
Note: If you are looking for a Cross-domain tracking guide for Universal Analytics, read this guide instead. If you are familiar with cross-domain tracking in UA, you probably know that most of the configuration is done either on the code level (e.g. in gtag.js) or in GTM (GA tags). Because of that, many marketers and […]
If you have tried to configure Google Analytics 4, you should already be familiar with custom dimensions. Basically, any custom text parameter that you send to GA4 (and that you want to use in the reports), should be registered as a custom dimension. But you can also customize your setup by sending custom metrics. What […]
Once you have created your basic reports, using a combination of KPIs, the next logical step is to move towards more advanced data analysis techniques, like predictive analytics.
Big data is messy. It’s scattered across platforms, it’s diverse, and in its raw form, it’s practically unusable. We know, it’s a painful truth.
If you think about how you do marketing today and how you did it a few years back, you can probably come up with a few differences. For one, you may have the feeling that pretty much everything about your day-to-day has become more overwhelming. And you’re likely going to be right.
The challenge with data is that it’s big. It’s scattered across platforms and devices. It’s messy. Yes, you’re thinking what we’re thinking: That’s more than just one challenge. But what if there was one solution? Well, there is – and it’s called ETL.
