Data Analytics: A Use Case for Payments

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By Lou Grilli

3 minutes

Monitoring a variety of transactions can help credit unions determine their most valuable members.

Sponsored by Trellance

Data analytics was once the domain of giant tech companies. Amazon’s suggestions—“If you bought that you might like this”—or Facebook’s algorithms of which of your friend’s posts you most want to see on your timeline are just two examples. With the proliferation of data across multiple systems, the increase in computing power at a decreasing price, and tools to extract and harness data, the science of data analytics is being used by credit unions to make better decisions. And it’s not just the bigger credit unions that are introducing business intelligence through data analytics to their staff; credit unions with under $500 million in assets realize that data analytics create better member experiences, increased product penetration across their member base and higher ROI. Smaller credit unions, in particular, need to embrace the use of data analytics to remain competitive. 

Still, it’s important to keep in mind that no company, regardless of the industry, should invest in data analytics just for the technology. The cost of data tools, hardware, cloud storage and experts (business analysts, data scientists and/or consulting services) can represent not just a significant up-front investment, but an ongoing cost that must be justified. The justification sometimes comes in the form of use cases—individual examples of data-driven decision-making that improves how members are targeted, acquired or kept connected to the credit union through customized, meaningful campaigns and reward programs. 

A credit union that is just embarking on the data analytics journey should start with the end goal in mind. Choose a pain point or problem to solve, ideally one that has a reasonably high payback if addressed correctly. If a credit union’s payments data, including credit and debit cards, ACH, bill-pay, and account transfers and balances is used optimally to inform business decisions, it can yield successful data analytics use cases. One such use case is member segmentation to determine a credit union’s most valuable members.

Identifying Your Most Valuable Member

All your members are special and add value to your credit union in some way. However, some members are more profitable for your credit union than others. These members deserve to be recognized for being the most valuable members. A rewards program is a great initiative to encourage members to be MVMs, but first the credit union needs to define what MVMs look like. This can be done segmentation, which begins with identifying MVM attributes. For example, high spend is a potential attribute of a MVM credit cardholder, so long as he or she doesn’t constantly take advantage of cash-back bonuses. A large direct deposit is another potential MVM attribute, so long as there are not equal amounts of corresponding ACH-out payments. 

The best segmenting approach looks at multiple attributes simultaneously, which requires analyzing vast amounts of data from various sources and normalizing it to get a member-centric view. Then the defined attributes (for example, three months of revolving balance, at least 10 credit transactions in specific merchant categories, and at least five different products) will help to determine the MVMs. Customized marketing communication, point-specific promotions, and VIP gifts can then be sent to members in that segment, to make them feel truly special.  cues icon

Lou Grilli is AVP/product development & thought leadership at CUES Supplier member Trellance, Tampa, Florida. If using data to make informed decisions and enhance the member experience are on your list of priorities, ask us about IronSafe, our data analytics solution. The Trellance team will be happy to share the capabilities of this data analytics software with you. E-mail us and get the power to use rich data to guide your business decisions. 

Check out Payments University, slated for Sept. 11-12, 2019 in San Diego.

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