Credit union success stories involving big data triumphs are few.
But more are beginning to trickle in, and a for instance is Congressional Federal Credit Union, a $735 million institution headquartered in Oakton, Va., with branches in the Capitol Building, the Rayburn Building, the Longworth Building and the Ford Building.
The way Chief Operating Officer David Hufnagel tells it, Congressional FCU had a problem. It knew it had the information it needed to better sell its 45,000 members on the financial products and services it offered. But it could not quite wrap its arms around the data.
That’s because the data was scattered across multiple silos that simply did not communicate with each other. Share draft, credit card and loan information were in some buckets, member information was in other buckets, and none shared with others. That is a financial services norm, experts said, but, little by little, institutions are taking steps to tear down the artificial data walls.
Congressional Federal said it did exactly that when it implemented a software platform called COA provided by ASA Corp., a Bridgeville, Pa., analytics company. The basic promise of COA is that it is designed to link together information from a range of sources so that a financial institution can better sell its customers by putting offers in front of them that they actually want to see.
The principle is simple. Members have no interest in offers that don’t pertain to where they currently are. But they have a keen interest in offers that do. And ASA’s aim is to swiftly gather together the data needed to analyze the present situation of members. Its COA is a species of what, broadly, is called enterprise opportunity management, and it sews together customer relationship management, sales force automation and marketing automation. Built in are tools that are intended to intelligently mine accumulated data for new opportunities.
Does it work? Hufnagel, with considerable glee, related a recent Congressional effort to sell members on auto loans that got a staggering 9% response rate. That is perhaps three-fold greater than what Congressional had been experiencing using old-fashioned tools.
With COA, however, Hufnagel’s group pulled together strands of disparate data. It knew which members were nearing the payoff of a car loan. And what their credit scores were. “So we knew they probably would soon need a car loan and that they were likely to qualify,” said Hufnagel.
“COA has become the center of our universe. It is the platform that connects the data and knowledge from across our systems and teams to deliver a complete view of our members, opportunities and service levels. It is delivering increased efficiencies and improved results,” said Hufnagel in an ASA white paper.
In a Credit Union Times interview, he was similarly enthusiastic. “The system lets us load the data we want,” said Hufnagel who elaborated that increasingly Congressional is blending data from its core, from third-party vendors, from third-party databases (credit reports, for instance), and it is beginning to look at meshing in social media sources. “We are mindful of being very respectful of our members’ privacy,” added Hufnagel.
What particularly excited Hufnagel is that ASA’s COA platform is geared at fine tuning opportunities for presentation to members. That is, this is highly focused big data. “With this, we know we can present the best offer to this member now. We have that much insight,” said Hufnagel, who suggested that even his harried, time-pressured membership welcomes sales calls if they are targeted to the member’s needs.
Russ Johnson, director of development at ASA, added in an interview that “what we do is data agnostic. We can pull data from many sources.”The COA tool is indifferent to the form of the data. Its sole aim is to analyze it in search of the right offers for this member.
Full implementation of the system took two to three months, said Hufnagel. He declined to put a price tag on what Congressional paid, but he said it was not expensive.
ASA in an interview indicated its analytical tools can be deployed on a software as a service model or hosted in an institution’s own data center.