**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.
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.
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.
Last year, Todd Belcher and I started the Boston Digital Analytics meetup in order to bring together our peers in the marketing analytics industry for networking and knowledge sharing. This month, we’re hosting our 5th Web Analytics Wednesday on 8/24 at Northeastern where Sharon Bernstein will be presenting on the topic of Data Storytelling. If you’re in the Boston area, come out and meet some local analytics enthusiasts!
RSVP here – https://www.meetup.com/Boston-Digital-Analytics-Meetup/events/232992703/
When I send out weekly performance summaries to my clients, I often focus on just a few key take-aways and insights. For instance:
Campaign A is providing leads at $5/lead while Campaign B is converting at $15/lead. I’ve shifted most of the budget from Campaign B to Campaign A, but started an A/B test on Campaign B’s landing page to see if its performance can be improved.
These reports focus on what happened and what is about to happen. What’s missing in these emails, and discussions around measurement in general, is what didn’t happen. In other words, what mistakes did we avoid because we had data pointing us in another direction? Read More
While listening to an interview with an analytics vendor the other day, I heard a phrase that is too often repeated and can be summarized as “… we flag users as highly engaged when they type your website address directly into your browser”. The problem with this statement is nuanced but will become clear in a second.
*autotrack.js has been updated! See this post for more information*
Just 2 days ago, the Google Analytics team released a plug-in called autotrack that packs a lot of new functionality into Google Analytics. The thinking behind autotrack is that far too many clients deploy the default GA snippet and stop there. By packaging up advanced GA tracking capabilities into one, easily deployed plug-in, clients will gain more value out of their GA account. These tracking capabilities include:
- Event Tracking – Track when users click on any HTML element with a certain data attribute
- Media Query Tracking – Track breakpoints, orientation, and resolution
- Outbound Form Tracking – Track when users submit forms that land them off-site
- Outbound Link Tracking – Track when users click on an outbound link
- Session Duration Tracking – More accurately track the duration of sessions by firing an event when the user closes their browser window
- Social Button Tracking – Track when users click on social sharing buttons
- URL Change Tracking – Track when the URL changes but the page does not refresh (important for single page applications)
While utilizing these enhancements goes beyond a “beginner” understanding of GA, by packaging them up in this easy-to-use plug-in it brings them from the clouds and into the hands of anyone with a basic understanding of GA and HTML.
For the many customers of Brightcove’s video platform, understanding user engagement (ex: play, pause, percentage watched) with their videos is key. And while Brightcove offers reports showing user engagement, there are many advantages to tying that data into a broader analytics platform such as Google Analytics. One particular advantage of this approach is that AdWords remarketing lists can be generated based on video engagement. Did a user watch your video but not convert? Remarket them!
I recently completed a Brightcove/GA integration and learned some things along the way worth sharing.
I recently completed a project with MIT Press Journals (MITPJ) as part of the Analysis Exchange, an online marketplace that connects mentors and students in order to provide free web analytics services to non-profits. The program is intended as a vehicle for providing recent entrants into the web analytics field with real-world experience while assisting non-profits with services that are typically out reach due to budget and staffing considerations. It’s a great program that I can’t recommend enough so I’ve put together the following case study that can hopefully inspire others to get involved. With permission from MIT Press, I’ve released all the deliverables for the project on Google Drive so that others can borrow/steal from our work as much as possible.
The project spanned 4 weeks and can be broken out into the following 4 phases:
- Week 1 – Understand
- Week 2 – Educate
- Week 3 – Analyze
- Week 4 – Recommend
Anyone responsible for measuring campaign performance has likely run into either incomplete or missing data due to incomplete campaign tagging. This can show up in the form of (not set) values in Google Analytics or in artificially inflated (direct / none) traffic.
The typical solution is to ask that everyone in your organization generate campaign links with UTM parameters such as:
However, these links and parameters are often cumbersome to generate and are prone to human error. Any typos will result in misleading data in your reports.