Log in to Google Analytics and have a look at the Acquisition reports, and you’ll find all kinds of data on how people get to your site. Ever wonder where that comes from, and how GA decides what the source, medium, or campaign values are? Wonder no more, because here we’ll de-mystify the rules.
The Source/Medium Rules
The basic dimensions that GA uses to describe where someone comes from are Medium and Source (along with Campaign, Keyword, and Ad Content where circumstances warrant). GA fills these in based on different sources of information, and there’s a specific order in which Google Analytics looks for this information: (more…)
As you may have heard, Google Tag Manager has a new interface. You can read the official announcement, but we’ve got 6 of the most important takeaways below. (more…)
Google Analytics export to BigQuery is great for getting at the raw session-level data of Google Analytics. But, it’s only for GA Premium (GAP) subscribers. If you have other reasons to need GAP – like increased sampling limits, DoubleClick integration, or additional custom dimensions — and you have the money to spend, GAP is a great option.
Raw GA data?
But what if you’re not a GAP subscriber? Can you still get the raw, session-level data?
In a word: no (at least not from GA). All of the data in GA reports and in its associated reporting APIs is aggregated data. You can create and export reports full of dimensions and metrics, but there’s no report that can give you all of the information for each session the way BigQuery can. (more…)
Segments are one of the most powerful features of Google Analytics, and they are often useful for zeroing in on the sets of users who are most valuable to us.
One way of looking at potentially valuable users is to look at the frequency with which they visit the website. Let’s look at a couple of ways to do that in GA.
You’ve heard the term “statistical significance”. But what does it really mean? I’m going to try to explain it as clearly and plainly as possible.
Suppose you run two different versions of an ad, and you want to know if the click-through rate was different (or you are comparing two different landing pages on bounce rate, or two campaigns on conversion rate). Ad A has a click-through rate of 1.1%, Ad B is 1.3%. Which one is better?
Seems like an easy answer: 1.3% > 1.1%, so Ad B is better, right? Well, not necessarily.
Consider a quarter
Suppose you have a quarter (and it’s a fair quarter, no tricks). The rate of getting heads when you flip should be 50%, right? If you flipped the coin an infinite number of times, you could expect it to come out heads half the time. Unfortunately in web analytics, we don’t have time to flip the quarter an infinite number of times. So maybe we only flip it 1000 times, and we get 505 heads and 495 tails. Do we conclude that heads are more likely than tails? What if we only flip it 100 times, or 10?
You can see that sometimes, the difference we measure is merely due to chance, not to a real difference.
You already know about event tracking in Google Analytics and using it for everything from downloads to video plays. Maybe you’re using jQuery or Google Tag Manager to capture events.
One thing to note about events is that, by default, events affect the bounce rate. That is, if a user lands on a page and an event is triggered, they are not a bounce (even if they don’t view any subsequent pages). In many cases, that’s what you want: after all, if someone engages with the page in some way, you probably don’t want to count them as a bounce any more.
However, you have control over whether those events affect bounce rate. There’s a parameter you can send with the event data to decide this called the “non-interaction” parameter. In a case where a video auto-plays when someone lands on the page, for example, we might want to set the non-interaction parameter so that the bounce rate of that page isn’t zero.
URLs are often one of the most problematic labels for data in web analytics: they’re messy, full of inconsistency, gunked up with a bunch of query parameters that may or may not be useful to you. It tends to make analyzing your content a mess.
Here, sort this stack of needles.
There are a number of suggestions for cleaning up those URLs (more…)
Back in May, Google announced that GA Premium customers would be able to export analytics data to BigQuery. It’s now rolling out to all Premium customers. What does this really mean? What’s it let you do beyond what you could before?
How do you access the data?
BigQuery stores your GA data in what is basically a giant table. It gives you a SQL-like interface to query that data, either through a web interface or programmatically.
If you use Google Tag Manager or another tag management tool, you’re probably already familiar with the idea of a data layer. It’s basically a centralized place for information about the page to be passed to analytics and other measurement tools.
Up to now, there have been some informal conventions in tools like GTM. But it would help us all to have some standard guidelines, for interoperability between tools. So, if you need to switch from one tool to another, you can easily do that without rearranging the data. Or, if you build a plugin for a content management system, you can build to the standard and not worry about which tool it will be used with.
So a W3C Community Group was assembled to tackle this problem, including 56+ organizations (including Google Tag Manager) providing input on a specification that is standardized enough to provide interoperability, without being too rigid to represent many different industries and websites. (LunaMetrics also participated in the development of the specification.)
After much deliberation, version 1.0 of this specification has been published. Let’s take a look at what it says and does.
If you pay attention to developments in Google Analytics, you were probably glued to the live stream of the Google Analytics Summit opening presentations. GA made a number of announcements about forthcoming features. One of the most exciting is about automatically tracking events in Google Tag Manager. It’s a feature that’s been highly requested ever since Tag Manager was released, and it’s especially exciting because it’s available NOW (unlike a number of the other announcements, which are only “coming soon” — such as a forthcoming SLA for Tag Manager for Google Analytics Premium customers).
But, if you go and take a look at Tag Manager trying to figure these out, you might find yourself scratching your head over documentation that is mostly “coming soon”. Not to worry: I’ve banged on the pipes, and here’s a guide to how it all works. (more…)