Accelerating analytic learning—it’s one of the top challenges my team is working to solve with many FICO clients. No wonder it’s been a running theme on this blog, and of my own posts.
Our most successful clients are accelerating analytic learning by pushing the boundaries beyond traditional champion/challenger testing. Here’s how:
- Conducting designed experiments, whose results can be accurately analyzed and causes of variations understood. A well-designed series of experiments tests carefully to explore as well as confirm key action-effect relationship hypotheses. In other words, the relationship between how the bank treats their customers and the consequences of such treatments that drive the P&L metrics for the bank. This is a powerful framework for learning that complements the traditions of “champion” vs. “challenger” testing.
- Probing beyond the edges of business as usual (BAU). An action-effect framework for creating experiments is a more surgical way of testing and learning. By mathematically tying the P&L drivers to consumer behavior, banks have the ability to more carefully venture design of some challengers outside of these the BAU bounds and introduce variation into the production data, thereby expanding what they can learn from it.
I’ve been blogging about accelerating learning via an analytic learning loop. It's important to point out that an analytic learning loop provides only the mechanism to accelerate learning—the degree of success achieved depends on how banks use it. The biggest winners will be companies that employ their analytic learning loops to push boundaries in the ways I describe above.