The old saying about assumptions may be applicable when it comes to how well credit unions know their members.
Three years ago Jim Norris, then, incoming president/CEO of the $97 million Montgomery County Employees Federal Credit Union in Germantown, Md., was told by staffers that there was no way to grow loans with existing members because everything had been exhausted.
That stall was puzzling given that the credit union served employees of local government agencies.
The first step was getting Raddon Financial Services do a financial analysis of the credit union to determine the usage of current services, services per member and their profitability, said Norris, adding he has always been a firm believer in gathering meaningful data.
“I’m not an analysis paralysis guy. Get 70% of the data and you can develop actions based on that. You don’t need to get to 100% to do something.”
The research revealed that members indeed had loans – just not at Montgomery County Employees FCU.
“That told me we were missing a mark with products not matched to member needs,” Norris said. “In looking at the data, it was clear that our current members had everything we needed to improve our market share.”
For example, Norris said auto loans served the credit union well but when auto sales sank, surprisingly, a flat credit card portfolio revealed opportunity.
“Again, staffers said ‘no way can we push credit cards, we’ve tried it all, can’t do anything,’” Norris recalled. “(We) got information on our members and they had lots of credit card balances, just not with us, so we did a balance transfer promotion and we grew our credit card portfolio like crazy.”
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From that promotion, further data showed that of the 75% of members who had a credit card, 35% had reward cards. So in April, Montgomery County Employees FCU began offering its own reward credit card. Over the past few years, the credit card portfolio has swelled from $3.9 million to $8 million, according to Norris.
“That rewards initiative is a money maker for us. Even if they pay off the credit card, just using it will generate the interchange,” Norris said. “It’s hard work but satisfying.”
Next Page: A Common Misdiagnosis
Between 2004 and 2008, growth at the credit union was zero, Norris said. The financial institution is currently at 40% to 45% growth. He attributes the increase to reaching members with products that are a match for their needs.
According to Russ Prettitore, chief revenue officer at Saylent, a Franklin, Mass.-based business intelligence and analytic solutions provider, this misdiagnosis of the membership is more common than credit unions think.
“What I often hear from many credit unions is ‘this idea that data analytics are a big bank product because we know our member base,’ but the reality is they may not know them as well as they think,” Prettitore offered.
Instead, credit unions should be asking questions, he continued, ranging from how to segment their business and what different areas they can focus on, to reevaluating where they are underperforming.
“When you look at wanting to reach those younger consumers under age 35, the data tells a great story of who they are, where they are shopping and how often. That can be used to make better decisions,” said Prettitore. “Rather than following what big banks are doing, credit unions can use the data to make informed decisions based on their own members.”
For the past five years, the $736 million San Mateo Credit Union in Redwood City, Calif., has also been proactively using analytics, rather than relying on intuition, to make decisions with a basis in reality, Stephen Tabler, vice president of marketing, who has worked with his team on the effort. This was especially critical during the economic downturn because San Mateo CU, like many others, were carefully watching the bottom line as it kept an even keener eye on where the expenses were.
“Early on, a board member spoke up and asked, ‘when things get better, are we going to be ready for opportunities as they arise,’” Tabler said.
To that end, the executive team invested in conducting a market area analysis, which provided two hierarchy rankings of zip codes. The first was member growth propensity in order of best opportunities for growth overall and member loan growth. The second provided zip codes for potential in auto loan volume.
“We got the report thinking it was good information to have,” said Tabler. “Then, four months later, a bank in an underserved area in East Palo Alto that had opened nine years ago, announced it was closing because the branch was ‘only marginally profitable.’”
San Mateo CU CEO Barry Jolette and an executive assistant happened to be attending a meeting. In passing, leaders from a community group in the underserved area asked if the credit union could open a branch there. The cooperative eventually moved in, branded the previous bank branch location and within a few months, opened its doors in a community that has a high Hispanic population. “That zip code was ranked sixth in auto loan and overall growth propensity, yet the entire nine years the bank, which specialized in small business, was there, they had not a single mortgage or auto loan” said Tabler.
With branches, a credit union may usually aim toward a five-year goal of profitability, Tabler explained. San Mateo CU’s new branch reached that goal within two years. It helped that because the existing location belonged to a bank, there weren’t the typical costs associated with building a branch.
“It was economically feasible for us to make the right decision within a short period of time,” Tabler said. “The only reason we have a fast-growing branch in an underserved community is because we already had the research at our fingertips.”
When it comes to data, some expert say the analysis should strive to be working, living document that helps empower teams across the organization to gain an understanding of how to best serve the membership.
“There is no silver bullet. “It boils down to know thy member and it’s a continual process of getting to the next level of the data and what else can you pull out to generate some results,” Norris said. “You’ve also got to do ROI analysis before and after every campaign. We aim for more hits than misses but I don’t mind the misses because what we learn from them gets us to the next level.”
Once the data is gathered, the process is dictated by information revealed. Tabler said focus and simplicity are key to not being overwhelmed by the sheer volume of data available.
“We try to keep it as simple as possible in the context of primary products and drill down from there to build and apply profile reports in different ways,” said Tabler. “Yes, pulling data generates huge reports, but what happens is once you find it’s successful and repeatable, you keep searching to find new opportunities with the data you have.”