I’ve written a new Variable Guide for Google Tag Manager, which covers the new GTM UI. This guide is for the old UI. You might be vaguely familiar with macros if you’ve ever used a computer. Basically, whenever you perform a complicated task with a simple gesture, or reuse complex code with a simple input mechanism, you’re using macros. Think keyboard shortcuts. In Google Tag Manager, this is the essence of macros.

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Having just come hot of the press with my latest article on GTM and Content Grouping which, to my delight, Bounteous had written an amazing tutorial on earlier, Brian Kuhn and the amazing Google Tag Manager development team came out with another incredible new feature: The Lookup Table Macro. In software engineering, a lookup table is an array which takes away a layer of complexity in runtime computation, and replaces it with a simple value assignment based on array indexing.

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Content Grouping is a nice new feature from the good folks at Google Analytics. Basically, it allows you to group your content according to a logical structure. You can create up to five Content Groupings, and you can have as many Content Groups within these groupings as you like. The difference between a Content Grouping and Content Group is hierarchy. The second is a member of the first. Read Justin Cutroni’s post on Content Groupings to get you started.

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Have you ever stopped to think about what the chain of events was that led you to a particular decision? Maybe not, but in web analytics it is something that should be considered. After all, there is something counter-intuitive about analytics tools such as Google Analytics, which require us to think in terms of clicks and recorded events that occur on the website, during the visit. Thankfully, we have evolved as a species, and we no longer place too much emphasis on last click attribution.

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(Last updated March 2014) This post is the final chapter of a trilogy. The ultimate refinement, if you will. It all started with my foray into the murky waters of context, when I tested how weather data could be used to provide extra information about site visits. When I wrote that post, I had two trepidations: 1) does sending the API call with every single page view affect site performance negatively, and 2) does forcing the page view call to wait for the API call to complete affect the quality of visit metrics.

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There is a new version of this post for GTM V2 here. While going over my previous post about using weather conditions to segment data in Google Analytics, I started thinking about performance issues. Since I’m using a visit-scope custom dimension, it seems futile to have it send the weather details with every single page load. The odds of the weather changing drastically during one visit are slim (unless you live in the UK), and I have yet to come up with a good reason to change my on-site behavior because the weather changed from a drizzle to a downpour.

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A word of warning. This is not a developers’ post, a guide, or a thought experiment. This is a bona fide rant. Sometimes we just need to vent. A couple of weeks ago, I checked one of our (inactive) client’s Google Analytics accounts I still had access to. What I saw in the acquisition report was this: See how direct traffic gobbles up a great big share of organic traffic in late October?

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