Predict Post-Pandemic Banking Behaviors With CU Transaction Data

Seeing around the corner to members’ future needs has never been easy. Heap on COVID-era complexities and positioning a credit union for long-term growth gets even more complicated.

What post-pandemic consumers will want from their banking institutions is anyone’s guess. Or is it? Credit union leaders have more predictive tools in their strategy toolbox than ever before.

One of the most effective, yet often illusive, tools is data analytics. Of course, that illusiveness is a problem. Because the discipline of analytics is so broad, credit unions often find themselves not knowing where to start. Getting a handle on what members need most to navigate COVID-19 recovery is an ideal starting-point use case. Not only does the analysis address an urgent need to serve members well, it presents an ideal opportunity for taking early steps forward on the path to data maturity.

The Accelerating Race to Analytics Proficiency

Now is an especially important time to build strong analytics muscles and the data-centric habits to support them. That’s because national data on post-COVID behaviors is painting a ­confounding picture. Some surveys indicated banking behavior has changed forever, with consumers now relying almost exclusively on digital channels to get their financial jobs done. Others suggest consumers long for the good old days of banking and can’t wait to get back to them. Take a recent J.D. Power survey, for example, which revealed nearly half (46%) of consumers expect to resume pre-pandemic banking activities, like regular visits to a branch.

Credit unions feeling disoriented by conflicting forecasts can achieve strategic grounding by investigating national hypotheses with local data. Even rudimentary analysis of the first-party data that flows through a credit union every day can reveal meaningful insights. With this information, leaders can make decisions on everything from staffing to cash management with confidence.

Where to Look for Local Insight

Branch transaction data can be particularly illuminating for credit union leaders attempting to position physical banking for success post-pandemic.

During the height of the pandemic, there was a significant drop in branch transactions due to mandated closures. Interestingly, though, national branch volume has remained low even as many locations have re-opened. This may be due to changes in traffic patterns, as more people work from home, and/or to an increased member comfort with digital transactions.

Credit unions can look within their branch data to uncover local patterns. Pulling a report of members who first began using digital tools within the last year is a good place to start. How have their branch behaviors changed now that locations are opening? Are they dropping back into the branch or continuing to use digital? Comparative behavior intelligence may indicate members are not interested in going back to branch banking for as many transactions as they did pre-pandemic.

Another place to look for indicators of future in-person banking behavior is the large employers and Select Employer Groups closest to a credit union’s branches. What are their return-to-office policies and timelines? This information can supplement insights from local data, helping branch managers anticipate future volume.

ACH as a Strong Source of Local Insight

One of the richest sources of first-party data is a credit union’s ACH file. Credit unions can identify meaningful patterns by sorting ACH deposits and debits. Here are a handful of ideas:

• Identify current and historic child tax credit recipients and other stimulus payments from the past year. Once identified, sort by balance and frequency. Which of your members are using this money immediately as it comes in? Might they need financial coaching to prepare for the end of economic stimulus? What about those who are saving the money? Would they be better off rolling the funds into an interest-bearing account, such as a 529 college savings plan or traditional CD?
• Segment credit card payments by round or exact ­payments. A member who is making round payments like $50 or $100 to a credit card company is likely paying down debt and carrying a balance month-to-month. These members could have a need for a lower-interest balance transfer or debt consolidation loan. Conversely, a member who is making a payment like $685.62 is potentially charging daily purchases to a credit card and paying it off in full each month. These members may be looking for rewards and could be incentivized to change cards with innovative loyalty offers and/or better digital options, like online payment.
• Investigate home improvement investments. Are Lowes, Home Depot, Ace Hardware and True Value credit card payments popping up in the ACH file? If those payments are round, might the member be carrying a high-interest balance on one of these home improvement credit cards? What could your home equity lending team do with a list of members who are fixing up mortgaged real estate in your area?
• Look into peer-to-peer (P2P) app usage. Are your members more frequently relying on Apple Pay, Google Pay, Venmo, Zelle, Cash App and other P2P apps to move money? What are the shared characteristics among members who love these tools? This kind of insight can kickstart strategic conversations around attracting new high-contributing members and keeping them engaged with your products and digital services.
• Search for airline purchases and other travel transactions. Are national expectations for pent-up travel demand coming true within your field of membership? If so, how can the credit union help? Could you revamp rewards to incentivize travel purchases on credit union cards? Or could you add or promote digital travel alerts within a mobile app?

Taking Action on Local Insight Into Real Member Behaviors

If there’s one thing the pandemic taught us, it’s that local matters. From political climates to daily positivity rates, the severity of COVID-19’s impact varied wildly from state to state and often county to county. This disparity is likely to continue, affecting the behaviors and expectations of local populations uniquely going forward. By keeping a finger on the pulse of member preferences with data analytics, credit union decision-makers can more confidently take action – not on global news of the day, but on the actual stories local data is telling.

Emily Engstrom Director of Customer Success AdvantEdge Digital, a CUNA Mutual Group company Madison, Wis.