By Tom Nawrocki, Payments Journal
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Financial institutions are sitting on vast reservoirs of customer and transaction data—but for many, that data still behaves more like scattered archives than a strategic asset. As banks look to compete on personalization and speed, data strategy is shifting from a back-office IT concern to a core driver of business value.
They have learned to reduce operational silos so they can share data more effectively, creating a more complete and actionable view of customer behavior. While many banks have taken advantage of these new capabilities, others still have not, or are only analyzing a small subset of the available data.
In the Debit Payment Data: A Business Strategy, Not Just an Initiative report, Ben Danner, Senior Analyst of Debit at Javelin Strategy & Research, examines how financial institutions can maximize the enormous amounts of customer and transaction data they hold.
“Large institutions are already making moves to bring customer data into their apps through services like Plaid and budgeting and forecasting tools, which used to be only in the domain of third parties,” Danner said. “Mid-size banks and community banks cannot wait with data strategy.”
A Wealth of Information
Product managers for offerings like debit and credit cards are familiar with key performance metrics such as the percentage of deposit accounts that have a debit card attached, how many of those debit card holders are actively using the card, average ticket size, and so forth. These are important transaction data points that anyone in product management should tracking for card products, even though each bank may analyst its data somewhat differently. However, the best banks are moving beyond these basic metrics.
“That’s a good first step,” Danner said, “But if you’re really looking to understand the market better, you’re going to have to go deeper than just looking at basic performance metrics on your card program.”
Deeper segmentation is where product managers and analyst teams can really excel, drilling down into areas like merchant categories, transaction types, and ATM usage. Every bank is sitting on a gold mine of transaction data that describes the habits of its customers. Historically, the challenge has been that banks haven’t had the right tools or enough time to dig deeply and make sense of all that data.
“You can have terabytes of servers worth of processing data, but it’s meaningless until you start digging in and doing the analytical work and interpretation of it,” Danner said. “This isn’t mind-blowing stuff, but there are important segmentations to look at, like breaking down your spend types.”
Challenges for Smaller Banks
Large institutions are already integrating artificial intelligence agents directly into their analytics and intelligence platforms. That leaves smaller banks needing to accelerate their modernization efforts to remain competitive.
Many of these smaller banks simply don’t have the resources or analytical tools to fully exploit this data. While organizations like Chase or U.S. Bank can dedicate teams to these initiatives, mid-tier and smaller banks don’t necessarily have an easy way to achieve the same outcomes. A smaller bank might have one product manager overseeing multiple offerings, or a single card lead responsible for the entire debit and credit card portfolio.