As a result of this pandemic and its impacts on the global economy, many organizations are questioning, reviewing and challenging their various marketing analytics investments. This process is to be expected and should be an opportunity to return to the rationale behind your investments: why you invested in this analytics initiative in the first place, and how the outcome will impact your business performance or goals. This article tries to shed some light on the relationship between your current marketing analytics maturity level and your investments.
Have you ever heard this, during a discussion with your peers or colleagues:
“We invested $XXXK in this analytics platform but we don’t see any value in it, we barely use it.”
Though sometimes it’s the plain truth — the platform really isn’t being used to its full potential — more often it’s the sign of a poorly understood analytics initiative.
Before talking about the relationship between analytics maturity level and marketing analytics investments, let me start by highlighting a few principles (also available in many good analytics books and articles):
Now let’s use a simple example to illustrate some of these key components of an analytics program:
Your conversion rate is down by 15%, impacting your revenue ($25,000 lost in a week); obviously Google Analytics (platform/technology component) can show the symptoms (the drop in conversion rate), but it can’t tell you why. The why will often come from analysts (human component) who spends much of their time searching for insights. They will start by double-checking, based on their internal best practices (process component), whether the same drop is recorded in your internal systems. Let’s assume that the drop is attributed to a new web form integrated in the checkout flow during the same time frame; the analyst shares the insight with the product team, which swiftly makes a correction to the web form after reviewing and challenging the findings (culture component). The VP of Marketing (culture & leadership component) hears this story and decides to sponsor a real-time alert system to quickly raise this type of issue since it has a direct impact on business performance. Thanks to the patch on the web form, the conversion rate is back to normal.
In this simple example, analytics played a role in resolving a business problem (loss of revenue). As you might have noticed though, the analytics platform/technology didn’t resolve the problem alone. There were multiple components involved between the identification of the problem, to its resolution. The analytics platform/technology was only one of them.
The concept of the analytics maturity model was heavily democratized by Thomas H. Davenport around 2007 in his book, Competing on Analytics. Many well-known companies also have their own analytics maturity models: SAS, Gartner, Adobe (focused on Digital), Google (focused on Digital), etc.
Most analytics maturity models take into account these key components:
The purpose of these models is to position, after an assessment, your organisation in a conceptual map (your maturity level today). You will generally need to define your roadmap to get to the next level of the maturity model. In a nutshell, the more advanced you are in analytics (the higher your level) the bigger the impact on your organisation’s business outcomes.
Going back to our previous example (key components of an analytics program), without an analyst who knows how to use Google Analytics (or any other digital analytics platform) you would not have found the root cause of the drop in conversion rate. Without a process to share these insights quickly, you would have lost more money. Without a leader who sees value in analytics, you would not have a real-time alert system in your roadmap, to detect these kinds of issues and reduce the risk of losing money.
Mastering the different components that make up a successful marketing analytics program is critical to making your analytics initiatives successful.
Since these components take time to build, you need an analytics maturity assessment to understand where you are today and an analytics roadmap to plan where you need to be in one or two years.
Your analytics investments should follow this roadmap so that you can maximize your return on investment.
To summarize, you can’t invest $XXXK in a platform, and expect a good ROI, if:
If you are currently thinking about maximizing the return on your marketing analytics investments or you want to build a roadmap or strategy to achieve your goals in analytics, feel free to contact us!