bank analytics, credit union analytics

Analytics in the Credit Union and Banking Industry: A Competitive Necessity

Analytics in Credit Unions and Banks

The word analytics now tends to bring companies like Google, Amazon and Facebook to mind. What most people don’t realize is that analytics – and the benefits it brings – can be achieved by organizations of nearly every shape, size, and industry. In the banking industry, most would tend to equate analytics with huge financial institutions like Bank of America, JPMorgan Chase, or Citigroup. But the $500 million banks and credit unions as well as the $5 billion banks and credit unions can achieve wildly successful results through the adoption of analytics and business intelligence programs.

For a variety of reasons, analytics in the banking industry tends to be treated as a “nice-to-have” innovation – ripe with risk without yielding significant returns – as opposed to a competitive necessity. Increasing regulations that place a greater strain on fee income and operating costs along with a low interest rate environment means banks and credit unions must become more innovative to stay relevant and competitive. Factoring in the advent of non-traditional financial institutions and competitors like LendingClub (a peer-to-peer lending platform) and Ally Bank (a 100% digital bank with over $100 billion in assets) places even more competitive pressure on traditional banks and credit unions.

Want to know how to combat this changing environment? Invest in analytics and business intelligence.

Over the next few weeks, we will be releasing several posts that identify specific ways financial institutions can benefit from analytics. We will discuss several areas in which analytics can be applied including member segmentation insights, profitability and lifecycle analysis, rate risk modeling, internal operation efficiency gains, channel analysis and alignment, and strategic planning and decision making.

Our hope is that through this education on analytics and business intelligence, your organization can adopt a more analytical mindset and take the first steps towards becoming data-driven

Banking in the year 2030

It is now common knowledge that purchasing airline tickets at different times relative to your departure impacts the price you pay for the seat. Hotels use a similar technique to adjust room rates. This technique is known as “yield optimization” or “revenue management” to maximize revenue based on anticipating demand and supply (either airline seats or vacant rooms). This is a highly analytical technique and represents a fantastic use of data to increase profitability. Yield optimization was first introduced by American Airlines in the 1980s which led to bringing in $1.2 billion over three years and even eliminated some competitors. Prior to this, the airline industry had no such analytical technique.

For several years, American Airlines enjoyed this competitive advantage – but not forever. Soon, other airlines adopted this analytical pricing methodology making yield optimization a necessity for survival instead of a competitive advantage. A similar trend happened in the hotel industry after Marriott introduced its own revenue management techniques. What first existed as a competitive advantage soon turned into a requirement for remaining relevant in the industry. This is discussed in quite a bit of deal in Thomas Davenport and Jeanne Harris’ book Competing on Analytics: The New Science of Winning – a book I highly recommend.

We believe that the banking industry will follow the same path as the airline industry and hotel industry. As banks and credit unions become more analytical, the early adopters will yield significant benefits in the form of operational efficiency, profitability increases, cost reductions, reduction in risk profile and several other benefits of analytics that we will discuss in upcoming posts. Soon after, all banks and credit unions will be required to develop their own analytical capabilities if they wish to remain relevant and stay in business. Those who refuse to adopt these techniques will be eliminated by competitors or absorbed through acquisitions by more analytically-driven financial institutions.

What does it all mean?

It comes down to three choices for banks and credit unions over the next several years:

1. Embrace analytics and adopt early. Early adoption will lead to significant competitive advantages in their operating regions. Market share, profitability, and member experience will all rise as a result of this early competitive advantage.

2. Wait until analytics seems less risky and adopt later. This late adoption will mean that the organization will not yield the significant gains seen by early adopters, but it will at least ensure their survival in a new data-driven industry.

3. Hold on tight to the old way of doing business, fail to embrace analytics, and – in all likelihood – lose market share, lose profitability, lose members, and eventually be acquired by a more analytical organization who can revitalize and re-engage your remaining membership.

Every bank and credit union can build an analytically driven organization with the right help. Over the next weeks, we will release posts that will highlight some very specific examples in which analytics can help your bank or credit union.

To learn how The Knowlton Group can help you define the proper business intelligence and analytics strategy or if you need help implementing an analytics solution, contact Brewster Knowlton at or call 860-593-7842 to learn more!

Posted in Banks, Credit Unions, DW/BI.