How Credit Unions Are Building New Revenue Streams with Data Analytics

Blog: The Tibco Blog

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Many businesses across different industries have shifted their business strategies in the face of both the challenges and the opportunities the past year presented. For financial institutions, that has meant expanding their strategies to stoke growth. For many credit unions, their emphasis has changed from primarily focusing on member retention to an approach designed to grow revenue.

To facilitate this evolution, credit unions are increasingly mining data to identify profitable areas of focus. By using data analytics, credit unions can do more with less, making the most of their marketing resources and controlling costs while identifying promising new revenue sources.

Developing Data Analytics Strategies

With a multitude of data sources in the modern business environment, it’s important to take a big-picture look at the data your credit union is collecting to identify the best approach to maximizing that data. In doing so, you’ll likely see a variety of ways that you can use data analytics to build revenue streams, including:

Embracing Innovation

With fintech companies creating new financial products and changing the way existing ones are delivered, credit unions and other financial institutions must change to keep up with consumer demand. Many of these products are targeted at improving processes that are seen as inefficient and hard to use.

Credit unions can use data analytics to identify which products and services are most likely to benefit from innovation. By adopting the most promising innovations and offering them to members, credit unions can both enhance member retention and create new revenue streams.

Embracing New Revenue Opportunities

Lending: With restored capital requirements focused on deposits, credit unions may have opportunities to pick up the slack in areas such as mortgage lending, auto lending, and home equity loans.

Value added services: Credit unions are well placed to offer exclusive services, programs, and products designed to boost member loyalty and generate additional revenues. These might include: affiliate discount programs, loyalty reward benefit plans, commercial matchmaking fees, or car purchase programs.

Putting Data Analytics to Work

After selecting a data analytics strategy and potential products and services that could benefit from its implementation, it’s time to dig into the data and see what the numbers say. Some key indicators to evaluate include:

Lifestyle indicators: Lifestyle indicators, such as homeownership status, marital status, new large deposits, and other characteristics, are among the wealth of data captured by modern software apps and can. These indicators can be used to identify members who may have secured mortgages with other financial institutions and are therefore at risk or open up further conversations to investment options.

Predictive data: In addition to lifestyle indicators, predictive algorithms can ingest data related to member actions to help identify an interest in loan products. By tracking your members’ digital activities, you can have a better understanding of their needs and approach them with targeted offers.

Members in need of assistance: The pandemic caused some members to experience financial distress, while others reacted cautiously, increasing saving and paying off debt. By carefully evaluating their data, credit unions can identify customers who may need help and tailor programs to assist them. Some identifying data markers, such as unemployment benefits, no employer direct deposits, can indicate a member may have financial distress. Your ability to reach out and offer specialized services and education to these members can endear you to them as an advisor and partner once they are in better financial health.

Member balance sheet improvement opportunities: With many consumers in the U.S. lowering debt, credit unions can assist members by providing them with access to financial planning services and products. Another potential revenue source from this trend centers on savings applications based on incentives, which have been growing relative to traditional fee-based savings accounts. Such services can generate solid revenue streams in the form of ongoing management fees in addition to fees for related services.

Improve Revenue Streams from Existing Products and Services

Credit cards and revolving credit are examples of products that can benefit from insights gained from data analytics. By analyzing member behavior by segment, credit unions can more precisely target their marketing efforts. Understanding the patterns which characterize different member groups can help optimize your ability to generate increased revenue.

For instance, demographic information relating to the uptake of products by age can be helpful in determining what products are most likely to attract new members in various age groups.

By using data analytics, credit unions can do more with less, making the most of their marketing resources and controlling costs while identifying promising new revenue sources.
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Analyzing your data, whether for creditworthiness, product mix by age group, or any other relevant metric requires a data analytics solution that can mine the totality of your data for actionable insights. TIBCO provides organizations around the world with analytics that help them maximize the value of their data. If you are ready to talk, set a time with our Ask the Expert feature.

Check out the on-demand CUTimes TIBCO-sponsored webinar, Understand the Member Journey to Drive Growth for Your Credit Union, to learn more.

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