Data is the new currency for Big Tech, business, banking and beyond.
“All data creates a competitive advantage. Google is not in the search engine business for the money; they are in it for the data,” says Tina Giorgio, president and CEO of ICBA Bancard. “Knowing what transactions are being performed and how your customers are performing them is invaluable information.”
of banks have a data scientist on staff
Source: Bank Direct
But having the data and knowing how to draw accurate information from it are two different things. According to a recent Bank Director survey, nearly half of financial institutions report not effectively using their available data, which leaves potential strategies untapped.
“One of my favorite quotes says data is only as good as the insights it provides and the leaders willing to put the action behind it,” says Chad King, director of payments at $3.8 billion-asset First State Community Bank in Farmington, Mo. “Most places have more information than they know what to do with, and they’re not understanding the insights that it is actually providing, and they’re not putting the action behind it.”
That may be because data analysis is complicated. While it provides line of sight into customer actions and behaviors, how it’s interpreted and applied matters, and there are ways to approach its review to inform payments strategies and ensure an accurate picture of trends.
“You’ve got to zoom in and zoom out on the tapestry,” says Kari Mitchum, vice president, payments policy at ICBA. “Yes, there are going to be individual threads that are making up your whole picture, but you also need to make sure that you’re not stereotyping.”
To use data effectively, community bankers need to balance the information with what they know to be true about their customers. Applying it will take some finesse, but a few guideposts exist to help navigate this slippery slope and unearth a goldmine of potential. The dos and don’ts of data analysis can make the difference in a bank’s payments strategy (see sidebar below).
Data can support community banks in helping their customers better manage their finances. Mitchum shares an example of a bank that monitored customer credit card activity, homed in on those customers who were making minimum payments each month, and then created a targeted campaign that showed the value of adding just $5 to the minimum payment to pay down the balance sooner.
The results? Customers made an average addition of $20 to the minimum payment, supporting a better payoff strategy.
Data analysis can also help community banks track where there are opportunities to cross-sell or reposition offerings.
For example, if a customer’s payment activity shows loan payments to outside firms or Venmo or PayPal payments, perhaps it’s time for their bank to discuss its loan and P2P payment options with them.
“We’ve got this massive amount of data, and we have to do something about it,” says Greg Ohlendorf, president and CEO of $207 million-asset First Community Bank and Trust in Beecher, Ill. “Once you determine what your transactions look like, then strategically, you can decide if you want to be in any of those businesses. Or if we’re in those businesses, we need to discover why our customers haven’t chosen to get that service with us, rather than competitors.”
Ohlendorf speaks to data as a route for solving deposit leakage, or the migration of deposit account funds to other providers. For example, as PayPal, Venmo and similar payments platforms encourage clients to leave balances in their holding accounts, funds that would have traditionally been in a bank account are in these outside environments, disintermediating the bank.
In addition, funds may be leaving the demand deposit account (DDA) to pay an outside loan service or investment fund, removing resources that may have stayed within the bank if the customer had used its services.
“I have to look at where your spend is going, and the question is, ‘What do I do about that?’,” Ohlendorf says. “That’s what that data is about.”
Avoiding data pitfalls
Data serves as a great resource, but as community bankers dive into it, they risk going down a rabbit hole of findings and subjecting themselves to analysis paralysis where the continued evaluation of data leads to inaction. King advises staying true to the original goals.
“Don’t allow the data to force you to make assumptions about your customers,” he says. “Prioritize what’s most important to you, what’s going to give you the biggest return, and build your payments strategies around them.”
Mitchum agrees. “You’re never going to have perfect data, and you want to be able to make decisions and move forward. Data is always going to be coming in, and you’re constantly making sure you’re on the right path. Don’t be afraid to change if you need,” she says.
Experts caution that when data is used to label behaviors, it introduces stereotyping. Referred to as confirmation bias, this approach runs the risk of surfacing false assumptions about customer needs. Tapping into the relationship banking model and aligning what the bank knows to be true about its customers with data points will support the right combination of data and personal connection.
“If all you do is study the data, you will develop confirmation bias,” King says. “You automatically assume that you know what customers need, as opposed to using that data to open up and have great conversations with them. We avoid that by using the data upfront to guide who we’re going to talk to and what we’re going to talk to them about, and then have a good conversation.”
Where to start
Today, only 14% of banks report having a data scientist on staff, which means most community banks need to be considering where they can find support. Resources exist to provide varying degrees of data review, starting with core providers and other third-party partners, including fintechs that specialize in data analytics and industry consultants who are familiar with both banking and data analysis.
“If a bank has access to its data through a data warehouse, ad hoc reporting is the fastest way to access the data.” Giorgio says. “If the bank does not operate in a data warehouse environment, there are providers who will ‘scrape’ the data from existing reports.”
And no matter what steps community banks take to get there, harnessing data for greater insights will help them in identifying next steps for deepening customer engagement and launching new products and services.
“The data tells the story,” King says. “The question is, ‘Are you going to do something with it?’”
A short guide to data usage
Where data is concerned, fixed rules are hard to come by, but the following list offers steps to execute data analysis with discernment.
Have a data use policy. Make sure all data research is in accordance with your bank’s policy and all applicable regulations.
Use data to help customers make better financial decisions. The data can help community banks extend the relationship banking model into targeted consultations with customers.
Track where customers’ payments are going. Through demand deposit accounts (DDA), community banks have access to customer payment transactions. Leverage that information to see where there may be opportunities to educate customers on the bank’s existing products and services.
Mine for opportunities to cross-sell other products and services to meet a need found in the transactional data.
Fall victim to analysis paralysis. Data begets data, so ensuring an unclouded vision of a specific goal is imperative to both acting on the data and evaluating the effort’s success.
Allow preconceived stereotypes to drive data review. For example, not all baby boomers are technologically challenged. Don’t let outside research overly influence internal review.
Succumb to confirmation bias and automatically make assumptions based on demographics or age. This could lead to disparate impact. Let the data guide the approach, but ensure that customers remain individuals with unique stories and needs.