Archive for the ‘Google Analytics’ Category
If you’ve never heard of Enhanced Ecommerce, the documentation and information available might seem a little overwhelming or overly technical and focused on implementation. But don’t let that be an excuse to dismiss this great new feature and miss out on one of the most important updates to Google Analytics this year!
By the end of this post you will be able to answer the big questions about what it is and why you should use it.
Firing a Google Analytics Virtual Pageview with Google Tag Manager is easy, and far more powerful than ever before.
When our clients or training attendees upgrade from classic Google Analytics to Universal Analytics implemented through Google Tag Manager, we often get questions about how to transition these Virtual Pageviews from inline code to being implemented through GTM.
There are a number of different ways you can implement Virtual Pageviews, and hopefully this will provide one solution to help ease the transition to a GTM implementation of Google Analytics.
UTM campaign parameters. We love them. We hate them.
They make it easy to track both online and offline marketing efforts. But they aren’t very pretty to look at, and they’re difficult to implement reliably, especially for a layperson (i.e. non-technical person).
Often, there’s a situation where we want to track a number of different approaches or people contributing to a campaign. Imagine the pushback you’ll get when you suggest each person modifies their UTM parameters to personally identify themselves or the approach they’re using.
Fortunately, there’s an easier way to track certain types of activities without having to resort to including all those UTM parameters. We can use a simple URL hash and some Google Tag Manager magic to uniquely identify each person.
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…)
As we look towards the end of this year and the beginning of 2015, consider how a training in Google Analytics, Google AdWords, or Google Tag Manager may help your career! Choose from seven different cities in the first quarter, ranging from Boston to San Francisco, with stops in Chicago and Denver along the way.
With trainings in cities around the country, we hope you can find a location that is easy to travel to and fun to explore!
Whether you’re just starting out in a new field or looking to get a deeper understandable of the tools you’re currently using, we have a class for you.
Learn how to better collect and analyze your data with our Google Analytics series, futureproof your website with the flexible Google Tag Manager, or drive qualified traffic to your site through paid search with our Google AdWords trainings.
Choose an option below to learn more about the specific topics we cover and decide which trainings would be right for you!
Google Analytics Google AdWords Google Tag Manager
As analysts and marketers, we always want to track positive performance metrics and conversions in Google Analytics. However, tracking errors is also important to monitor the health of your site and keep track of signals indicating a negative user experience.
Accessing this data gives us a better idea of what’s causing users to get lost and wander into the dark, unattached voids of your domain. Knowing where these problem spots are makes it easier to fix internal links or set redirects.
I’ll show you different ways to view where people are hitting these error pages and where they are coming from, either through your existing setup or by using Google Tag Manager to fire events or virtual pageviews. (more…)
On July 30th, 2014, Google Analytics announced a new feature to automatically exclude bots and spiders from your data. In the view level of the admin area, you now have the option to check a box labeled “Exclude traffic from known bots and spiders”.
Most of the posts I’ve read on the topic are simply mirroring the announcement, and not really talking about why you want to check the box. Maybe a more interesting question would be why would you NOT want to? Still, for most people you’re going to want to ultimately check this box. I’ll tell you why, but also how to test it beforehand. (more…)
It’s now easier than ever to track and compare performance between articles and blogs. While Google Analytics shows you pageviews and other key metrics, frequent content comparisons are made difficult by the shifting time frames.
How can I compare a blog post that was published this month vs. a blog post that was posted last month? Sure, we can run two different reports, pull it into Excel and start crunching the numbers, but there’s gotta be a better way!
Enter Cohort Analysis. You may have heard this term thrown around before, usually in relation to users on your site and when they first became users. The idea here is to group users or sessions into common groups, like who first visited in January or first-month visitors. Avinash and Justin Cutroni both love cohorts, so obviously we should, too!
In this case, we’re going to use Google Tag Manager to put content into cohorts so we can analyze how they performed in similar time frames. We’ll pass these into Google Analytics as Custom Dimensions so they’re available for analysis. It’s actually much easier than it sounds! (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.
In this blog post, I evaluate several of the numerous (and potentially overwhelming) options for the processing and reporting of Google Analytics data. The default Google Analytics web interface is great for quick ad hoc data exploration, but limited for deeper analysis and the development of automated reports.
Whether we’re mining for hidden trends or trying to report on hard-to-extract dimensions, there are a number of third-party tools out there can that help ease the burden.
In the first half of this article, I explain the difference between the two types of Google Analytics data: what’s available from the standard interface and what’s available through the BigQuery export.
The second half of this article is an evaluation of three different solutions for processing, visualizing, and reporting on Google Analytics/GA BigQuery data. I evaluate these three solutions (ShufflePoint, Tableau, and R) based on objective features and my subjective scoring of performance.
I only evaluate three data processing solutions in this article. Think I missed a good one? Let me know!! We all have a different background in data analysis tools, and I would love this conversation to continue in the comments section.