Tag sequencing was introdced to Google Tag Manager in late 2015. Its main purpose was to facilitate the sequential firing of tags that have dependencies with each other. Due to the asynchronous nature of third-party libraries like Google Tag Manager, it’s difficult to establish an order of completion with tags that compete for their chance to fire. Tag sequencing changed this, as it allows you to establish setup and cleanup tags - the former firing before the main tag, and the latter after.
This article is a guest article by someone from the analytics community I really look up to. Dan Wilkerson is an analytics developer at LunaMetrics, a company I hold in high esteem. Dan is one of the smartest technical analytics experts out there, and a large bulk of the awesome scripts and hacks that LunaMetrics produces (almost on a daily basis) have been orchestrated by him. So I’m very pleased to give the floor to Dan, so that he can tell you all about using the pesky document.
Form abandonment isn’t always easy to define. Most often, it refers to when someone starts to fill in an HTML form, but leaves the page without submitting it. As a definition, this works nicely. However, with multi-page forms it naturally refers only to the last page of the form. Also, especially with government institutions, forms can be saved to be submitted later. Here, again, form abandonment must be reconsidered.
If you read my previous post on fetching the Client ID from the Universal Analytics tracker object with Google Tag Manager, you might have agreed with me that it sucks you can’t access the tracker object interface in real time using Google Tag Manager. This is because all of the set commands you add to a Universal Analytics tag template take place before the analytics.js is loaded and the tracker object is properly created.
Over the last couple of posts I’ve mainly been doing proof-of-concept (POC) tests with Google Tag Manager. The great thing about a POC is that you don’t really need to have any viable results or insight-driving technological innovations. The point is to showcase some feature of the platform on which the experiment was conducted. In this post, I’ll take a care-free step into the world of POCs again. My goal is to do a simple split test in order to identify which variant of a landing page (or key element thereof) produces the most conversions.
You’ve probably come across a number of guides or posts talking about why it’s necessary to block so-called internal traffic from your web analytics reports. The reasons are pretty solid: internal traffic does not emulate normal visitor behavior, it rarely contributes to conversions (skewing up your conversion rate), it inflates page views, and it wreaks havoc on your granular, page-by-page data. Internal traffic is vaguely described as “your employees”, “people really close to your brand”, “your marketing department”, “your web editors”, and so on.
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