9 minutes
Credit unions should start their data journey by defining what they hope to achieve.
Data analytics is playing an increasingly important role in helping forward-thinking credit unions grow and become more sophisticated: CUs are using the power of analytics to maximize strengths, overcome deficiencies, devise marketing strategies and enhance the member experience. But the first step in any analytics journey must be to define the organization’s goals.
“Any kind of data analytics transformation must begin with development of a well-defined strategy,” says Shazia Manus, chief strategy & business development officer for CUESolutions provider AdvantEdge Analytics, the analytics division of CUNA Mutual Group, based in Madison, Wisconsin. “Some credit unions may be farther along on their journey, while some are exploring how to get started. Our goal is to help them and meet them wherever they are in their journey.”
Manus explains that a good strategy is accompanied by a clear road map featuring three key components: prioritization of objectives, calendared milestones and a budget. “What we have discovered in working with credit unions is that different organizations are on different tracks,” she says. “There is no one path that fits all, but there must be a defining strategy that drives the analytics journey.”
Launching a data analytics program requires an organization-wide commitment to the process. “The key point I would make about data analytics is to think about it culturally as opposed to technologically,” says Bill Handel, general manager and chief economist at Raddon, Schaumburg, Illinois. “It’s not just about going out and buying a piece of software. It’s about creating a path. From our perspective, the first step is to build a culture within the organization and an understanding of the importance of data and how to use it.”
Propelling Growth
Suchit Shah, COO of CUES Supplier member CU Rise Analytics, a CUSO based in Vienna, Virginia, reports that over the last decade, data analytics has played an expanded role in propelling the growth of credit unions.
“If you look at credit union growth numbers, the picture post-2010 is altogether different than the picture from the decades before,” he says. “That’s when the genesis of data analytics-driven strategies came in. ... As a result, credit unions are growing more, they’re cross-selling more and they’re becoming more aggressive in the marketplace.”
Shah reports that some credit unions turn to CU Rise to address a specific problem, such as improving credit card performance or reducing member attrition, while other organizations are looking for help in devising an overall business intelligence strategy.
The key to using analytics effectively is to then arrive at a reasonable course of action supported by the data. “We call this analytics-driven action,” Shah reports. “After those actions are taken, we monitor to see the success of that action. Did it do what it was supposed to do? Did it succeed? If not, what could have been done differently? What did we learn?”
Credit unions can use data analytics to facilitate growth both in membership and the bottom line. “One thing that everyone wants to know is: How do I retain my profitable members—or perhaps more importantly, how do I retain my highly engaged members and help them become even more engaged?” Manus observes.
Data analytics is also useful in the lending arena, providing credit unions with loss mitigation models that can identify the impact of charge-offs on loan profitability. Analytics can also help better define loan risks in correlation with FICO scores so that CUs can price loans accordingly.
Such data also can be used to answer any number of “what-if” strategic planning scenarios. “For example, we might say, ‘What would happen if I grew a certain type of loan by this much—what does that do to my net worth or profitability?’” Manus explains.
An Analytics Progression
The best way for credit unions to get started with data analytics, Manus advises, is to target small successes. After achieving a good return on investment and getting comfortable with the process, they can then expand their efforts.
“You don’t want to boil the ocean and take on everything at once,” she says. “Data analytics is a journey, and it requires a cultural shift within the organization to bring everybody along on not just how to look at information but how to contextualize in a way that drives business action. It is the credit union leadership—the key C-suite executives—who will have to drive the functional areas forward with their respective vision and take the organization where they want it to go.”
Handel concurs that organizations are better off starting small with data analytics and progressing as they become more adept at it. “At Raddon, we’re firm believers in the ‘crawl, walk, run’ theory of data analytics,” he says. “Our experience in the data analytics space has been that many organizations go for the whole elephant. They try to bite off too much. If you start small—getting wins, piece by piece, while building competencies and capabilities—you’re more likely to be successful.”
When credit unions reach running speed, they’ll often have multiple analytics programs or initiatives occurring simultaneously. “It will be a broad spectrum of always-on campaigning … and the ability to continually feed data and analyze it,” says Becky Summers, leader of marketing solutions for Raddon. At that point, the everyday use of data analytics “becomes much more of a strategy discussion. That’s really when the organization is able to run—when they have a collaborative culture of learning and using data.”
For some credit unions, taking the plunge into data analytics may seem like a daunting task. For those who are hesitant because they don’t know where to begin, Summers has two words of advice: Get started. “Understand what your vision is long-term, but get started wherever you decide, and add to what you are doing over time. It’s the one bite of the elephant.”
The term “predictive analytics” is often used in conjunction with data analytics. Handel notes that these terms are largely interchangeable when formulating strategy, in that they both refer to using data to understand the behavior or needs of an individual.
“The predictive aspect refers to: Based on what somebody has done or is doing, can we understand what they’re likely to need next?” Handel explains. “That’s something that Amazon does very well, and it’s why they’re so successful. With predictive analytics, you’re able to go beyond traditional marketing campaigns and take your marketing … to the next level.”
Shah stresses that predictive analytics is not a crystal ball, but it does enable organizations to infer what certain segments of their membership are likely to do based on past actions. “The job of data analytics is introspection,” he says. “You analyze where you are and try to understand how it could relate to the future.”
In the case of member attrition, for example, data analytics will allow the CU to identify a subset of individuals that have a higher likelihood of leaving, based on behaviors or characteristics they have in common with each other and members who have already left, and then take corrective action to reduce that possibility.
“With this data, what you have done is effectively reduced or squeezed your population from a big group to a smaller group,” Shah says. “You’ve identified the group for which you are better able to predict an outcome, which will move you toward an optimized action.”
The Impact of COVID-19
The coronavirus pandemic has added a new dimension to how credit unions are using data analytics in 2020. “COVID-19 has had a clear impact on credit union priorities, such as keeping branches and essential services open, enabling remote staff and helping members adapt to changes in channel usage—such as members experiencing the mobile app or remote deposit capture for the first time,” Manus observes. “Credit unions also are meeting the needs of members with loan modifications or skip-a-payment. These are all data-driven issues. Each of these activities requires you to track them and manage the data associated with them.”
Meeting the changing needs of members during the pandemic has become a top priority for credit unions, and data analytics have aided in identifying those needs. As an example, $11 billion Randolph-Brooks Federal Credit Union, Live Oak, Texas, had intended to use its new predictive analytics solution from Raddon to assist with hyper-targeted marketing and communicating more effectively with its nearly 1 million members. RBFCU was set to launch the tool in March—exactly when the pandemic hit the United States. The CU had to pivot quickly to address the needs of members who were losing jobs, having their work hours drastically reduced and struggling to make their mortgage or car payments.
Raddon also pivoted to address the pandemic. Conducting extensive research with its clients, the company developed a new analytics tool—called the Vital Signs Report—that credit unions could use to identify the impact of the pandemic on their members’ needs.
“The Vital Signs report enables credit unions to see changes in direct deposit, deposit balances, and the addition of an unemployment benefit,” Summers explains. This data helps CUs to prioritize member outreach and service based on need.
RBFCU’s VP/Marketing Blake Lyons found the Vital Signs tool to be extremely useful in gauging the pandemic’s financial impact on members. “We were able to look at key data sets—such as seeing those recurring direct deposits disappear or identifying accounts where there was a huge depletion in savings—and take that into consideration as we reallocated resources,” he reports.
One action that RBFCU took was to reposition its auto loan program messaging from promoting low rates to a message of “refinance and save.”
“We wanted to get the word out to our members that they could: a) refinance and defer payment for a couple months to ease the strain on the household, or b) lower their payment,” Lyons says. “Those were quick wins for our members who were under duress, especially at the onset of the pandemic.”
The data also guided RBFCU in retooling its collections strategy. “To relieve some of the stress and burden during the pandemic, our collections department isn’t collecting,” Lyons says. “Instead, they’re consulting and having one-on-one conversations with members, leveraging key data sets to identify those in need and what we can do to help them. Since the start of the pandemic, we found that tailoring our information to fit a more utilitarian style of marketing has better suited the needs for our membership and consumers at large.”
Lyons has now been able to move forward with using predictive analytics for micro-targeted marketing as originally intended while continuing to monitor member needs during the pandemic.
“As a larger financial institution, we have to stay relevant, and we’re using the products and tools at our disposal to help us maintain that relevancy,” Lyons says. “The future of banking could be a completely customizable experience. Our goal is to leverage data to help make that possible.” cues icon
Based in Missouri, Diane Franklin is a longtime contributor to Credit Union Management magazine.
