“It's about getting things down to one number. Using the stats the way we read them, we'll find value in players that no one else can see. People are overlooked for a variety of biased reasons and perceived flaws.”—Moneyball
As an unapologetic data geek, I found the movie "Moneyball" less about baseball and more about advanced analytics. To me, the real message is that data is an extremely powerful way to align your business with your true objectives, regardless of what industry you are in.
You can crunch all the numbers in the world, but until you identify precisely what your organization values, you can be easily misled. How do you know what to analyze if you haven’t figured out what to measure?
In the analytics game, we use the scientific method to identify opportunities for action. The first step is to articulate the problem for which you are trying to solve.
As a case study, let’s take the problem of member attrition: What opportunities exist to reduce member churn?
This is a fundamental business question every organization is trying to answer. There is inherent value in reducing the number or percentage of members who leave annually, and it’s a lot cheaper to keep current members than it is to acquire new ones.
Now, you need a hypothesis:
“Member attrition may be reduced if we identify members who are likely to leave, explore the reasons why, and proactively engage them.”
With more focus on the goal, we can now brainstorm with the business to examine the leading indicators of attrition. For example, you might look to data in the system that houses your direct deposit information.
After performing some exploratory analyses, it becomes clear that members who discontinue direct deposits are much more likely to churn.
You might pull additional data from other systems that reflect other signs of decreased engagement, such as decreasing account balances or discontinuation of bill-pay services.
With all of this data, we can build a robust model that predicts the probability of churn for each member. You can then combine these probabilities with a relevant financial metric, like member life-time value, to create a prioritized list.
Now that we know who is most likely to leave, we can decide on a unique action or program to increase the likelihood of retaining each member. The business could, for example, take the member list and turn to the call center to engage customers more deeply.
They could create a campaign to increase member satisfaction and even identify cross-sell opportunities.
Actions like these not only take the problem of attrition head on by going directly to the source. By increasing profitability, this approach takes a weakness in the organization and turns it into an opportunity to engage the member.
Uncovering value no one else can see. Questioning assumptions. Using your resources effectively and efficiently. Identifying members at risk. Devising strategies and actually executing them.
These are the real value of analytics. Is it time for credit unions to have Moneyball moments of their own?