The Trellance Data Blog

Credit Union Analytics Comes of Age: 9 Essential Guidelines

Posted by Peter Keers, PMP on Jun 6, 2017 12:04:00 PM


A fascinating new report by McKinsey & Company highlights that credit unions can drive organizational value by creating an analytics culture.

The report recognizes that financial services analytics has reached a point where marketing was in the 1970’s for the banking sector. Prior to that time, sales and marketing initiatives for credit unions and banks were rare. In 2017, a credit union would find it difficult to survive without some level of marketing effort.

Credit unions today are beginning to acknowledge the potential benefits of analytics. Some brave pioneers in the industry have already gained experience in this area. Now, analytics is poised to be a mainstream activity. To this point the authors admonish credit unions and banks to, “establish analytics as a business discipline”. The implication is organizations must make a serious commitment to building an analytics culture.

What can a credit union do to support such a commitment? The report lists 10 essential guidelines.

  1. Great analytics starts with high-value questions, not data

 While high quality data is absolutely essential, the first step is to carefully articulate the problems to be solved. Once the problems has been properly framed, then a realistic assessment of their value when solved is key prioritizing analytics projects.

  1. Prioritized use cases

 Problems to be solved must be translated into use cases. Strict prioritization by dollar value may miss some opportunities where, “scale can be increased quickly” for quick wins. Quick wins can then build confidence and drive program momentum.

  1. Set out a vision for use of analytics applications

The report notes five steps for matching use cases to analytical technique.

  1. Identify the source of value for the solved problem that the use case represents.
  2. Consider the available data – Does it adequately support the use case?
  3. Identify the analytics technique that will respond to the problem and probably produce insights.
  4. Considering how to integrate the analytics technique into the workflow of the business.
  5. Anticipate the risks of adoption issues to use of the technique.
  1. The smallest edge can make the biggest difference

Rather than taking on a few large analytics projects, the report suggests building a capability to solve, “hundreds of small ones that all add up”. It is the cumulative effect of many small solutions that can bring significant value.

  1. Loops beat lines every time

 Taking an iterative approach with many smaller initiatives can make for shorter, faster feedback loops. Quickly learning about what works versus what doesn’t can bring value faster.

  1. Insights live at the boundaries between data sets

Breaking down data silos is a prerequisite to unlocking the potential of analytics for credit unions. Insights that would have been otherwise impossible can be revealed as a result.

  1. Analytics isn't enough; adoption is essential

A crucial part of designing an analytics culture is planning for user adoption. Communications and training must be well thought out as part of the overall rollout.

  1. Design matters

How analytics is delivered is an important component in encouraging adoption and getting the maximum value out of information. For example, attractive and intuitive dashboards can motivate decision makers to use these analytics tools.

  1. Analytics is a team sport

Building the analytics team takes careful forethought and a realistic budget. Credit unions may start with one or two resources dedicated to analytics but to successfully grow the effort, a more comprehensive team of specialists needs to evolve.

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Topics: Data Analytics, Leadership