By Ray Birch
BROOKFIELD, Wis.—Too many credit unions are “wrestling” with their member data and often think a data warehouse is the answer to their problems.
Those are two key issues getting in the way of credit unions effectively leveraging the wealth of data they have on their members, according to several people.
“Much of the industry is wrestling with data—how do I make it relevant? How do I use it in a way so I can achieve my goals that and ultimately help my members?” said Becky Summers, leader of marketing solutions for Raddon, a Fiserv company. “I think so many organizations are thinking in terms of ‘I need a data warehouse,’ or ‘I need to bring data into one place.’”
Instead, what those credit unions should be thinking about, according to Summers, are the underpinnings of “clean data.”
Bill Handel, SVP-research with Raddon, agreed.
“I think that's where more people in the industry are struggling, to really make a difference for the organization,” said Handel. “People immediately purchase something like a data warehouse and say, ‘OK, there's the answer.’ That's probably a really dangerous thing to do, because a data warehouse is a tool, it's not a solution. The data warehouse is something that's built by the technical side of your organization. It's an important piece, but it doesn't really solve the problem.”
Becky Summers
Summers emphasized the goal is to use data in more relevant ways.
“It’s taking big data and translating it into more usable data,” she said. “We can use the transaction-level data and contextualize it after it's cleansed and make a much bigger difference, make the data more organized. Because we know what's happening in the life of the member, we're able to channel that and get accountholders engaged. That's the crux of the conversation.”
Breaking Through
Handel said credit unions taking that approach are better able to break through the tsunami of information bombarding consumers and talk to members about the things they’re interested in.
“It’s the ‘Amazonification’ of banking,” Handel said. “How do we become much more targeted in how we communicate to the membership, so that every time we communicate to them we're coming to them with a message that has some relevancy to them?”
Handel reminded credit unions have significant amounts of member data available to them.
“When you think about the information that comes across, say on a checking account, it's really meaningful and powerful information we can use,” he said.
Acting in a ‘Relevent’ Way
But using that transaction-level data correcty is critical, said Summers.
Bill Handel
“We can see what the members are doing, how they are spending their money. But we have to effectively contextualize that information,” she said. “For example, we talk about recency and frequency of spending. Just because you make one transaction to buy a golf club, that does not make you a golfer. Now, if you also have greens fees and you buy really cool golf outfits and you visit the golf shop, that probably makes you a golfer. Understanding those types of activities will help you personalize your communications to members and make them much more relevant.”
Handel said a deeper understanding of member data will also help the credit union better understand why members make their choices of financial institutions and products.
“For example, how many credit unions are actually looking at the payments behavior of their members off of the checking account, and bill pay activity?” asked Handel. “You find members may be making credit card payments to Chase or Wells Fargo. What credit unions need to do is look closely at that data and try to understand why a member is using Chase as opposed to the credit union’s credit card. And then, explore that little bit more deeply. This becomes a really powerful thing for an organization to understand—it's not just about the things your members are buying, but it’s about who they're doing business with. That really becomes a critical piece of this whole effort.”
More Than Technology
Gaining a deeper understanding of the data requires more than just technology, it takes skilled staff to make the analyses, added Handel.
“The interpretation of the data is very difficult on two levels,” he explained. “First of all, simply reading what the data string says…and then determining what it actually translates into, what it means. And third parties can help. Fiserv has a solution that’s helping a lot of credit unions better understand and use their data—Predictive Analytics.”