Significant time and resources are focused on data collection and cleansing steps.  While a single-source-of-the-truth in the form of a data warehouse are both foundational and necessary goals, often the biggest immediate ROI contributor lies within the computational layer.  These calculated fields, or synthetic data elements, are the most predictive, action-oriented elements of models and equations.  Specifically, what are the “most important” data elements that drive action and result in immediate alerts at your credit union?

When we ask financial institutions what specific outcomes they seek to use data to make better decisions on, the responses tend to include:

  • More Members
  • Increase Loans and Deposits
  • Increase Member Engagement
  • Better manage risk
  • Improve bottom-line results

We’re all pretty capable of effectively measuring the first two; at least top-line measures by product, sub-product, region, branch, etc.  There is an art to measuring attrition and some science is involved when measuring within different member segments . . . but we’ll leave these discussions for a later post.

But how do you measure member engagement?  We’d likely all agree that strong new member on-boarding is key to engagement.  But, how do we measure on-boarding?  What is “success”?  Which branch does it best?  Which team members stand-out?

Even if we start improving on-boarding, do we know how it impacts our bottom-line results? Most credit unions don’t or struggle to calculate member contribution (or profit) by member.  Most cannot tell us the average profit per product much less their “ideal product mix”.

Consider that the “most profitable bundle” of products has an average-line with some members above and below that average.  The challenge, of course, is how to add more members above the average-line to preserve, or ideally increase, the average and total contribution?  However, without these critical measures it’s likely that we can land more members into that “ideal product mix” while we drive down the average-line and reduce the average member contribution of said bundle.

Consider the importance of segmentation.  Members choose how to engage, and, therefore, member segments vary significantly.  Consider how differently members choose to engage within the following segments: 

  • Indirect vs. direct members
  • Deposit-only versus loan-only members
  • Digitally-focused members versus traditional banking members

Because of these differences, the next action (i.e. the next-best-offer), also varies.  The goal is to have a path for each member segment towards the ideal product, transaction and service bundle.  The contents of the message, channel, and timing will all vary based on the segment; and their maturity level within that segment.

In conclusion, executive management meetings, board room discussions and strategic plans often include goals related to improving member experience, member engagement, cross-sell success, better on-boarding, and improving bottom-line results.  Yet the vast majority of credit unions do not have quantified metrics to measure if things are improving, which branch does best, which team-members succeed most often, that generate the next best-action to move the meters that matter most.

I’m sure we’d all agree: What Gets Measured Gets Done.

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