Google Analytics
Like many analytics practitioners, I’ve been toying around with Google’s latest version of Google Analytics: App+Web. There are a lot of great new features (and many missing ones! It’s in beta.), but one thing that struck me was that there was no clear path for Universal Analytics users to migrate to App+Web. According to builtwith.com, there are 29 million installations of Google Analytics and 4.9 million GTM installations out there. That’s a lot of potential migrations!
That’s why I built migratega.app, a tool that allows Google Analytics and Google Tag Manager users to easily migrate their Universal Analytics tags to App+Web.
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Google Analytics
A few topics I’ve been following have all converged into a single project that I’ll describe here and also demonstrate. I’ve blogged quite a bit about the googleAnalyticsR package which powers a number of my latest projects. The creator of that package, Mark Edmondson, has recently evangelized the use of Google Cloud Run and Google Cloud Build as cheap and effective ways to move your R projects into the cloud. I’ll be honest, until about 3 days ago I had no idea what these products did. I see the light now and am excited to share.
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Google Analytics
Google recently announced an upcoming shift in how website performance will affect search ranking starting in 2021. Moving forward, there will be additional emphasis on a series of metrics called the Core Web Vitals which focus on how users perceive the responsiveness of your website. These vitals address a major flaw in how website performance was measured historically. Older metrics like “page weight” described specific networking or computing resources that might be taxed, but didn’t directly describe the degree to which those bottlenecks affected users. Consequently, it was difficult to ascertain what a “good” page weight might be, beyond the conventional wisdom of smaller is better.
In this article, I’ll show you how you can incorporate core web vitals into Google Analytics. If you’re just looking for the GTM tags and triggers to accomplish this, you can download them here.
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Google Analytics
Ever since I learned about Market Basket Analysis, my head was spinning with ideas on how it could be applied to web data. To back up for a second, Market Basket Analysis (MBA), is a data mining technique that catalogs the strength in relationships between combinations of items placed together during a transaction. Applications often include:
- Recommending content in the form of “Users who view X and Y also view Z”
- Offering promotions for combinations of items to increase revenue
- Better understanding of user behavior and intent
- Updating editorial decisions based on popular combinations of items
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Google Analytics
Google Analyitcs segments are a fantastic way to organize the results of an analysis. There are, however, a few limitations of using segments in GA:
- They cause reports to become sampled after 500,000 sessions (or 100M with GA360)
- Only 4 segments can be compared at one time
- Segments are saved under your Google account which makes sharing them a pain
- When comparing segments, it’s hard to tell how much they overlap
All of these limitations can be resolved by bringing your Google Analytics data into R with the googleAnalyticsR library, but this post will focus on #4 above: Understanding segment overlap. The code generating this blog post can be found here.
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Google Analytics
The CausalImpact R library measures the effects of an event on a response variable when establishing a traditional control group through a randomized trial is not a viable option. It does this by establishing a ‘synthetic control’ which serves as a baseline under which the actual data is compared.
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Google Analytics
One of the benefits of being a freelance analyst is that I have access to dozens of different client instances of Google Analytics and Google Tag Manager. One common implementation I find is scroll tracking. Whether through a custom plug-in or GTM’s out of box tracking, clients often implement events that look like this:
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Google Analytics
**Update: Google Data Studio now includes native support for GA Segments. The post below may still be relevant if you are looking to combine data from multiple sources into a single Data Studio report /Update**
Ever since Google released Data Studio in mid-2016, I’ve received a lot of interest from clients who find its data visualization and data sharing capabilities much easier to grasp than the standard Google Analytics reports. However, anyone who has put together a Data Studio report has noticed that its simplicity is both its strength and weakness. You can easily create visually compelling reports in minutes, but it lacks the sophistication of more feature-rich tools such as Tableau. One missing feature that I’ve seen users complain about is its lack of support for GA Segments. Fortunately, with the Google Sheets connector and Google Analytics add-on for Sheets we’re able to work around this limitation. Note that this same process works (and is slightly easier) with Supermetrics, but I’ll demonstrate my solution with the GA add-on for Sheets because it’s free.
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Google Analytics
Google Analytics holds a trove of information regarding the path that each user takes on your website. It’s not a leap, then, to imagine using past user behavior to predict the path that a current user will take on your website. What if we could use these predictions to download and render assets before the user requests them? Thanks to the HTML5 prerender command, we can! In this post I’ll discuss how creative applications of Google Analytics, R, Google Tag Manager, and the HTML5 prerender hint were used to create a snappier browsing experience for users of www.targit.com.
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Google Analytics
On August 2nd, Google announced the release of an updated and much improved autotrack.js plug-in that solves many common challenges that people face when implementing Google Analytics. One major change is that the autotrack library is broken out into 9 different discrete plug-ins that can be included in your solution independently of one another through the “Require” command. While there is thorough documentation from Google, I couldn’t find a nice concise description of each plug-in so I’ve provided that here.
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Noise to Signal