In higher ed, it can sometimes feel like we are either drowning in data and have no idea what each data point means, or we are in patches of a desert between systems where there are no insights into user behavior. Whether this is because of a lack of resources, overly complex setup, or lack of expertise, it can sometimes feel like the data we need for actionable metrics is just slipping through our fingers. Therefore, when the time comes to create those dreaded annual reports, the panic is real. Worse, if you haven’t had a strategic data plan in place for the past 12 months, you can find yourselves unable to calculate the ROI of your outreach efforts. With this in mind, we thought it would be helpful to host a workshop at Converge 2019 that focused on actionable efforts to wrangle these data efforts together.
Taylor Gehrls and I put together a presentation that focused on educating the audience about multiple aspects of analytics; as well as arming the attendees with actionable steps they could take home and influence their current reporting. We started by discussing Google Analytics and Google Tag Manager from a 10,000 feet level:
Since this was a more intimate setting, we were able to answer questions and tackle topics on the fly. What I found the most interesting was the similarities between the attendees. Even though most of them had different setups, experience, and technical expertise, the discussions all had the same general thread. It turns out, everyone is concerned about their data’s validity, setup, and management. If you feel like you are having trouble or that you don’t totally get the whole analytics thing – you are not alone.
The section of our presentation that went over the best practices for implementation of these tools was easily our most engaged. Many teams in Higher Ed, if they are implementing Google Analytics/Google Tag Manager at all, will simply get the system in place and “set it and forget it”. GA/GTM can even lend itself to this sort of engagement, since it takes manual data work to get working correctly and it feels like it should simply work after that. It’s getting the data that you want to Google Analytics so your job is done, right?
However, good data management doesn’t end there. Once you have a measurement plan in place, and you implement that plan, then you need someone on your team to be consistently editing and optimizing the system. Are we capturing the right audience segments and filtering them correctly? Are our UTM parameters setup correctly and used consistently? Are there other segments that we should consider? These are the sorts of questions you should be asking yourself at least on a monthly basis. A GA/GTM setup is not an “As Seen On TV” wonder product. It’s a system, like any other, and it needs maintenance. The process for that maintenance should look something like this:
Using this circular revision model to constantly check and re-check the current systems allows you to go back with a clean pair of eyes, and look at what could be implemented better, or cleaner. As soon as you start running reports with these new metrics, you’re inevitably going to have more questions yourself, but also from your department heads and deans. You’ll soon find yourself running reports on the current data and seeing if you’re capturing what you want. If it’s not there, then you’ll begin pushing things around to make sure that when that annual report comes calling, you’ll be ready with actionable data, and the confidence to present it.
Taylor and I walked away from the workshop happy to have shared some of our knowledge in GA and GTM and to have such an engaged audience. The biggest takeaway that I hope was gained from our workshop is that these systems are engines, not hammers. They take time and effort to build, and once you have them running, you need to take care of them.