Most people will most likely recognize a few or all three of these challenges, and for some, the thought of working with web analytics will cause a little sweat on the forehead because you have stared blindly at bounce rates, sessions, and pageviews. The solution, thankfully, is pretty straightforward — it's all about keeping it simple.
In order to simplify your setup, it is first and foremost a matter of asking yourself what the purpose of the collection is. What do we need to know and why do we need to know? The answer to these questions will guide all subsequent decisions regarding tracking. With that guideline at hand, it's about cutting out unnecessary and irrelevant numbers. For example, does it make sense to track all clicks on all elements of a website, or do you only need to know the number of clicks on the most important CTAs? If you cannot answer what a measuring point should be used for, then there is no need to collect it.
The next point is about prioritizing and simplifying the reports you work with, so you don't have to try to find meaning in an out-of-the-box Google Analytics interface (hint: it can be quite difficult). Here you can benefit from the new "Explore" functionality in GA4. This is a tool for putting together tailored reports, where you can create tables, segment and cohort analyses, funnels etc. It can be a bit of a hassle to set up if you're not used to it, but it's a really good tool for creating data views that give you exactly the insights you need. Alternatively, you can also make use of other data visualization tools, such as Power BI or Data Studio. The latter is the most user-friendly and intuitive system of the two, so it would be our recommendation in most cases.
Lastly, it's all about getting all the hard work of setting up tracking and reports to provide value. If tracking is allowed to live in a vacuum outside of daily workflows and decision-making processes, it doesn't matter how well it is thought out and set up. How to succeed in this, however, there is no unequivocal answer, because it is important to find solutions that fit your ways of working. However, there are a few good places to start if you want to start making more active use of your data. Working data-driven boils down to two simple principles:
- Quantitative qualification of hypotheses
- Execution-evaluation flows
Put another way, it's about substantiating the things you think you know with quantitative methods and making sure that you always evaluate projects based on measurable results. The hard part about working data-driven is not declaring that you want to do it going forward; the hard part is actually doing it. Here my recommendation is always to establish clear agreements and processes that pass the "video test" from Morten Münster. If you have not heard of the test, it is simply that if someone observed you working using a surveillance camera, they should be able to see you doing the task from the recording. More specifically, this means actually gathering all stakeholders and setting them down for the purpose of evaluating. One can rightly be inspired by Scrum and use the retrospective model for evaluation, but it does not matter — the most important thing is that you actually evaluate.
Now, of course, I've spent a lot of lines trying to make strategic data collection and evaluation seem like something easily accessible that you just throw yourself into, using a few simple rules of memorization, but unfortunately, that's not the case. You can be thrown into strategic, organizational, and technical challenges, so don't underestimate the enterprise. But if you put on the work gloves and work with it in a structured way, it can in turn provide tremendous value and make your life easier.