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.

Every data analytics strategy contains the 4Cs: Collect, Cleanse, Compute, and Consume.  Collect and Cleanse are the focus of a data warehouse – aligning data and addressing disparate data silos.  Consume pertains to the user interface whether it be Oracle BI, Power BI, WebFOCUS, Tableau, etc.  Yet it can be argued that the Compute layer is the most important and is frequently misunderstood from three perspectives.

#1: The Value of Calculated Variables or Data Elements. 

As an example, it’s less important to know that an RDI occurred (return deposit item or charge back check), it’s more important to know that this was a first RDI on an account following a recent mobile deposit.  Such a scenario may trigger immediate action to place a hold and investigate potential fraud on the account. However, without an algorithm or model in place, this scenario often goes unseen.

Another example: consider the number of debit card transactions or ACH transactions on an account.  It’s more critical, for analytic purposes, to know that, by mid-month, an account has a 50% decline in their transaction volume compared to prior months.  This is a potential indication of an account about to close.

#2: What about Machine Learning and Artificial Intelligence?

The second perspective has been recently elevated by many credit unions wanting to define a strategy for Machine Learning (“ML”) and Artificial Intelligence (“AI”).  In this frequent conversation, I ask what is the dependent variable you’re trying to:

  • Take action on 
  • Improve or increase 
  • Measure 

If we dump large quantities of data into ML and AI models, be warned, you’re likely to get some statistically meaningful results.  But, you’ll waste significant resources running around and trying to build a model that does what?  

ML and AI are best applied to data already aligned correctly that includes numerous calculated fields, and, ideally, used to improve upon an already proven model or equation.  In other words, data for the sake of data is meaningless.  What is your desired outcome?  Focus on that first and the data second.

#3: The Right Messaging

The third perspective revolves around where we all really want to be!  We want to KNOW what message works best, to which member, using which channel, at what time, to help drive [insert your CUs goal here!].  That is, we want to move from predictive models to prescriptive action.  Doing this properly yields outcomes like: do this and our desired out come will occur 40% of the time.  Or, immediately do [action here] because 60% of the time this series of events is related to fraud.  These are the action lists the line-of-business leaders, branch and risk managers need to succeed.

Almost always, the calculated fields are the most predictive, that have the highest correlation coefficient and are the most valuable for predictive and prescriptive models.  It is the results of calculated fields, algorithms, and models, within specific segments, that direct the actions to generate the data analytics journey return on investment.


Interested in discussing these ideas further?  Drop us a note!