From Interest to Fees: Fresh Strategies to Drive Credit Union Revenue Using Analytics
July 2015

The increasing reliance on analyzing large volumes of data is becoming more and more prevalent across a broad spectrum of verticals that seek to maximize profits and deliver better services to their customers.

The choppy waters of today’s global economy mandates that financial institutions stay operationally nimble, which is why analytics are becoming more of a necessity rather than an option to drive revenue.

This is especially true for smaller credit unions that may have limited strategic resources but strive to increase membership, improve ROI and grow their business.

And the numbers pointing to the sheer brawn that big data can bring to a credit union’s bottom line is compelling and makes its strategic implementation a no-brainer.

A recent Nucleus Research study showed that an incremental 241% ROI can be generated by applying data to business decisions. That’s a huge boost for credit unions that know how to use a vast wealth of customer data to make profitable decisions.

But large aggregates of data can sometimes be overwhelming and confusion. It’s akin to sifting through large amounts of dirt and rock in order to find the gem-quality stones.

The best way for credit unions to harness the power of analytics is to have a great strategy in place and stick with it.

1. Start with a holistic view of your goals

In the same way that small organizations begin with a solid business plan, start with your end goals. Where do you envision the credit union to be in 1 year, 2 years or 5 years? For most credit unions, the goal is to grow membership and services. Imagine what tools and processes are needed to manage all the data you’ll be analyzing. What data points are most important to the financial institution and how can they be leveraged on a larger scale? Look at metrics like cross selling, using interest and fees to best advantage and identifying the most profitable products to offer your membership. Then start investing in the tools and personnel needed to reach those goals.

2. Know how to cull relevant data from the herd

The essence of analytics is to capture large amounts of aggregate data from which useful patterns of membership data can be gleaned. With big data, you’re likely to have more information than you need which is when analytic solutions like Visible Equity are highly useful in collecting and whittling down large aggregates into useful subsets and key data points that eventually can be used to reach the right members.

3. Seek out the right personnel

As more credit unions realize the importance of leveraging data analytics to meet the needs of their members and increase profitability, a number of hurdles in the way of adopting data analytics can arise. Credit unions may find a shortage of skilled in-house experts trained to use big data to the organization’s benefit. And depending on the size of the credit union, it may be tempting to train a credit union employee on how to analyze data rather than seek out and attract the right talent to provide analytical insights. However it’s done, the main thing to concentrate on is to cultivate a data-driven culture at your credit union.

4. Package data into easily digestible formats

After all the data mining is complete, it’s going to be packaged into some sort of collaborative format that can be shared with other credit union management. Some of the data will be used to evaluate the health of the current product portfolio or to open cross-selling opportunities across a product line. More importantly, it should be presented to support predictive analytics which can be used to facilitate huge bumps in lending for example.

5. Optimize

Whether you goal is to devise credit and debit card strategies to increase card penetration or achieve greater return on new promotions and account plans, analytics can be best utilized through constant evaluations of the credit union process. The information gathered is meant to make better decisions and deliver improved value to members.  Rigorous testing and predictive modeling are just a few ways to make that happen. When analytic strategies are applied correctly, the resulting improvement in performance and reduction in costs equates to a win-win that helps drive revenue .


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