Simo Ahava's blog

I really like Google Optimize. It has a fairly intuitive UI, setting up experiments is easy, and there’s integrations for both Google Tag Manager and Google Analytics built into the system. It’s still a JavaScript-based, client-side A/B-testing tool, so problems with flicker and asynchronous loading are ever-present (though this is somewhat mitigated by the page-hiding snippet).

One issue with the Google Analytics integration is the difficulty of creating segments for sessions where the users were actively participating in the experiment. In fact, there’s no specific signal in Google Analytics that tells you the NOW the user is “experiencing” an experiment. You’ll find the Experiment Name and Experiment ID dimensions, yes, but they have one drawback:

I really like Google Optimize. It has a fairly intuitive UI, setting up experiments is easy, and there’s integrations for both Google Tag Manager and Google Analytics built into the system. It’s still a JavaScript-based, client-side A/B-testing tool, so problems with flicker and asynchronous loading are ever-present (though this is somewhat mitigated by the page-hiding snippet).

One issue with the Google Analytics integration is the difficulty of creating segments for sessions where the users were actively participating in the experiment. In fact, there’s no specific signal in Google Analytics that tells you the NOW the user is “experiencing” an experiment. You’ll find the Experiment Name and Experiment ID dimensions, yes, but they have one drawback:

With GDPR looming around the corner, it’s time to explore the options you have at your disposal for respecting the new, stricter regulations for tracking users and for collecting data about their visits to your website.

UPDATE 20 June 2018: Google has released the allowAdFeatures field which renders the solution below redundant (at least for the displayFeaturesTask part of it). Please refer to this article for more details on how to conditionally block the advertising hit to DoubleClick.

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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 Bounteous’ equally ingenious Scroll Tracking recipe. I’m sure you’ll be very pleased to know that Google Tag Manager just released a native Scroll Depth trigger, with which setting up scroll depth tracking will be a doozy!

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. And oh BOY what a trigger it is!

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. You can add and update your own tags, with just a few clicks, whenever you want, without bugging the IT folks or rewriting site code. It gives marketers greater flexibility, and lets webmasters focus on other important tasks.

At this time, those of us who started using the tool immediately, or who had been beta-testing it, saw quickly what the key selling point of GTM was. It was a way to impact change on the website as quickly as possible, without having to wait for a release to be verified and pushed live.

Universal Analytics can collect Page Timing data from users that load your pages. This data is populated in to the Behavior -> Site Speed -> Page Timings report, and it’s a very useful feature for optimizing your website.

However, there’s a murky underside to this generous feature. The way Page Timings collection works is that when Pageview hits are sent from the site, a sample of these (1% by default) are automatically followed by a timing hit which includes page performance data grabbed from the Navigation Timing API.

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. However, exact match has one significant problem: it’s exact match. Thus even though the Lookup Table variable is extremely useful, it’s missing the flexibility of, I don’t know, say, regular expressions. You will be pleased to hear, then, that Google Tag Manager has released a new variable type: the RegEx Table!

There’s a fabled, mythical beast that prowls the jungles of digital marketing. They have no issues with running and analyzing crawler data, offering suggestions for server-side redirects, building remarketing audiences, implementing tag management solutions, speaking of Data Layers, copy-pasting code from Stack Overflow, configuring bid managers, and speaking at conferences presenting on all the aforementioned activities. However, for some reason, they still claim that they are “non-technical”, or “just marketers”.

Last updated 20 April 2020: Clarified how lazy-loaded videos can be tracked with this trigger.

Let’s cut straight to the chase. Google Tag Manager has just released the YouTube Video trigger, which gives you native support for YouTube video tracking. And it’s great! Even though we’ve been more than satisfied with the excellent tracking scripts provided by e.g. Cardinal Path and Bounteous (with a small modification from yours truly), this is a no-brainer for native support in Google Tag Manager.

Sending personally identifiable information (PII) to Google Analytics is one of the things you should really avoid doing. For one, it’s against the terms of service of the platform, but also you will most likely be in violation of national, federal, or EU legislation drafted to protect the privacy of individuals online.

In this #GTMTips post, I’ll show you a way to make sure that any tags you configure this solution with will not contain strings that might be construed as PII. The tip is for Google Tag Manager, but with very little modifications it will work with Universal Analytics, too.