Your Analytics Maturity Level: An Important Compass for Your Analytics Investments
In these times of transformation, many organizations are questioning or adjusting their investments in marketing analytics. This reaction is normal and encourages managers to reflect on why they invested in these grassroots analytics initiatives and how they will impact their performance and business objectives in the future. In this article, I will try to illustrate the link between marketing analytics maturity and the investments that need to be made.
Have you ever heard this, in a discussion with your peers or colleagues:
“We've invested $XXX,XXX in our analytics platform, but we don't see any value in it, we barely use it. »
While sometimes it's the plain truth — the platform isn't really being used to its full potential — more often it's a sign of a misunderstanding of the analytics initiative.
Before I get into the nitty-gritty of the relationship between analytics maturity level and analytics marketing investments, let me highlight a few principles (also available in several good books and articles about analytics):
- Business issues generally require business solutions — they are usually not solved by technology or platforms alone, although technology and analytics can be part of the solution;
- The impact of processes, people (skills/teams), culture and leadership (the C level analytical vision ) is often underestimated and is one of the root causes of the failure of analytical initiatives ;
- Analytical marketing involves marketing performance, which should be equivalent to business performance (ultimately, we are always looking for business results; not recognizing this could cause you long-term problems);
- The success of an analytical initiative is often linked to its proper integration within a specific business process (the optimization of this business process);
- Analytics can't make up for a bad digital marketing strategy (analytics can't solve your problems if it's not part of a well-thought-out business strategy); furthermore, your analytics strategy should be aligned with your digital or business strategy to maximize its impact.
Now, let's take a simple example to illustrate a few key components of an analytics program:
Your conversion rate is down 15%, which impacts your income (loss of $25,000 in one week). Obviously, Google Analytics (platform/technology component) can diagnose the symptoms (the drop in conversion rate), but cannot give you the cause or why. The why often comes from an analyst (human component) spending a good part of his time in search of insights. It will start with a double check, based on internal best practices (process side), to find out if the same drop is observed in your internal systems. Assume the drop is attributed to the new web form integrated into the checkout flow during the same period; the analyst shares his discovery with the product team, who will then correct the web form after examining and cross-checking the results (culture component). The Vice-President of Marketing (Culture and Leadership) hears this story and decides to sponsor a real-time alert system to solve this type of problem quickly, since it has a direct impact on the performance of the company. Thanks to the work done on the web form, the conversion rate is back to normal.
In this simple example, analytics played a role in solving this business problem (loss of revenue). That said, as you may have noticed, analytics (platform/technology side) cannot solve the problem alone. Several aspects were involved, from identifying the problem to solving it. Analytics was just one of them.
The concept of the analytical maturity model was heavily democratized by Thomas H. Davenport around 2007 in his book, Competing on Analytics . Several well-known companies also have their own analytics maturity model: SAS, Gartner, Adobe (digital-focused), Google (digital-focused), etc.
Most analytics maturity models consider these key components:
The objective of these models is to position, after evaluation, your organization on a concept map illustrating your current level of maturity. You will usually need to define your roadmap to move to the next level of the maturity model. In short, the more advanced you are in analytics (the higher your level), the greater the impact on your organization's business results.
Going back to our previous example (key parts of an analytics program), without an analyst who knows how to use Google Analytics (or any other digital analytics platform) you wouldn't 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 the value of analytics, you wouldn't have had a real-time alert system in your roadmap to detect this type of issue, and reduce the risk of losing money.
For your analytics initiatives to be successful, it is essential to master the different components that make up a successful analytics marketing program.
Since these components take time to put together, you need an analytics maturity assessment to understand where you are today, and an analytics roadmap to plan where you need to be in two years.
Your analytics investments should follow this roadmap so that you can maximize your return on investment.
In summary, you can only invest $XXX,XXX in a platform and expect a good ROI if:
- You do not understand the business problems you are trying to solve with this investment;
- You don't have at least one or two people spending 50% of their time digging into data to share useful insights with the rest of the organization;
- You don't have an organization (culture) ready to take action on these insights to improve its business performance.
If you are currently considering maximizing the return on your analytics marketing investments or want to establish a roadmap or strategy to achieve your analytics goals, please get in touch !