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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. You can do away with a lot of complexity by resorting to macros, especially if you ever find that you need the same piece of code or the same operation over and over again.

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. To put it simply, a lookup table looks through an array of source values, and assigns a value to the target depending on what the source value is. Well, maybe it’s easiest to show it in an image:

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.

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. In short, last click attribution is a model where success of a particular goal is attributed to the conditions underlying the active session. So if a person visits a page via bookmark and downloads a PDF file on that page, the direct visit channel (which covers, by default, bookmarked visits) is the only one which receives the kudos for the file download. Nothing is attributed to other channels, which might have exposed the user to the brand, the product, and the file itself. Ridiculous, right? R.I.P. last click attribution!

(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.

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.

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?

(Last updated June 2014) Google Analytics provides us with a nifty way of tracking social interactions. With a simple plugin, you can track how many +1s and Likes your pages accumulate.

This guide shows you how to activate social interaction tagging with Google Tag Manager and Universal Analytics. The instructions are for Facebook Likes, Google+ +1s (now deprecated since Google Analytics tracks +1s automatically), Twitter Tweets and Pinterest Pins.

Note that if you use a third-party API (e.g. AddThis) these tricks probably won’t work. You’ll need to use the API functions themselves to enable tracking.

UPDATE 9 Oct 2013 - This works in Google Spreadsheets now as well! Feel free to make a copy of the sheet and work with the code. It’s still just a prototype, but I’m happy to see data bouncing from spreadsheets to Google Analytics.

View the Google Spreadsheet here

ORIGINAL POST Inspired by Daniel Waisberg’s excellent post detailing the use of Google Web Forms to send data to your Analytics account, I decided to create something similar.

I’ve had such a blast in the Google+ Google Analytics community. Not only are the people super-duper-nice, but I have the wonderful opportunity of helping people with their Google Analytics problems without feeling obligated to invoice them or ask for compensation for my troubles. I do it because I love to help, because I feel like I have a lot to share with the community, and because I’ve always believed that the more knowledge you share the more you accumulate.