Over the past few weeks, I’ve been coding like crazy. The three biggest outcomes of this frenzy have been this new blog design (switched finally away from WordPress and took the plunge back into the world static sites using Hugo), a new Google Sheets add-on for managing Google Tag Manager containers and assets, and a Slack integration in GTM Tools. In this article, I’ll quickly introduce the last two, as I’m writing a separate article about the site redesign.
Scroll depth tracking in web analytics is one of those things you simply must do, especially if you have a content-heavy site. Tracking scroll depth not only gives you an indication of how much users are digesting your content, but it also lets you turn meaningless metrics such as Bounce Rate into something far more useful. If you’ve already been tracking scroll depth in Google Tag Manager, you’ve probably been using either Rob Flaherty’s brilliant Scroll Depth jQuery plugin, or LunaMetrics’ equally ingenious Scroll Tracking recipe.
Holy visibility, Batman! Visibility is a seriously undervalued aspect of web analytics tracking. Too often, we fall into the trap of thinking that “Page Views” actually have something to do with “viewing” a page. Or that tracking scrolling to 25%, 50%, or 75% of vastly different pages makes sense on the aggregate level. So you will be very pleased to know that the Google Tag Manager team (who have been on FIRE recently), have just published the Element Visibility trigger.
5 years ago, on 1st October 2012, this lovely video popped up in Google’s Analytics Blog: It was accompanied by a blog post, which contained a brief look into many of Google Tag Manager’s key features, some of which are still relevant today. Google Tag Manager is a free tool that consolidates your website tags with a single snippet of code and lets you manage everything from a web interface.
Ever since the Lookup Table variable was introduced in Google Tag Manager, users have been craving for more. The Lookup Table does exactly what it promises: lookups. These are exact match operations, which are extremely inexpensive to perform, because they can only have a binary result: either the match exists in the data store being queried or it doesn’t. This performance stays constant even if the data store being queried increases in size.
- OLDER POSTS
- page 1 of 24