The old saying about assumptions may be applicablewhen 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 inGermantown, Md., was told by staffers that there was no way to growloans with existing members because everything had beenexhausted. 

That stall was puzzling given that the credit union servedemployees of local government agencies.

The first step was getting Raddon Financial Services do afinancial analysis of the credit union to determine the usage ofcurrent services, services per member and their profitability, saidNorris, adding he has always been a firm believer in gatheringmeaningful data. 

“I'm not an analysis paralysis guy. Get 70% of the data and youcan develop actions based on that. You don't need to get to 100% todo something.”

The research revealed that members indeed had loans – just notat Montgomery County Employees FCU. 

“That told me we were missing a mark with products not matchedto member needs,” Norris said. “In looking at the data, it wasclear that our current members had everything we needed to improveour market share.”

For example, Norris said auto loans served the credit union wellbut when auto sales sank, surprisingly, a flat credit cardportfolio revealed opportunity.

“Again, staffers said 'no way can we push credit cards, we'vetried it all, can't do anything,'” Norris recalled. “(We) gotinformation on our members and they had lots of credit cardbalances, just not with us, so we did a balance transfer promotionand we grew our credit card portfolio like crazy.”

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From that promotion, further data showed that of the 75% ofmembers who had a credit card, 35% had reward cards. So in April,Montgomery County Employees FCU began offering its own rewardcredit card. Over the past few years, the credit card portfolio hasswelled from $3.9 million to $8 million, according toNorris. 

“That rewards initiative is a money maker for us. Even if theypay off the credit card, just using it will generate theinterchange,” Norris said. “It's hard work butsatisfying.” 

Next Page: A Common Misdiagnosis

Between 2004 and 2008, growth at the creditunion was zero, Norris said. The financial institution is currentlyat 40% to 45% growth. He attributes the increase to reachingmembers with products that are a match for theirneeds. 

According to Russ Prettitore, chief revenue officer at Saylent,a Franklin, Mass.-based business intelligence and analyticsolutions provider, this misdiagnosis of the membership is morecommon than credit unions think.

“What I often hear from many credit unions is 'this idea thatdata analytics are a big bank product because we know our memberbase,' but the reality is they may not know them as well as theythink,” Prettitore offered. 

Instead, credit unions should be asking questions, he continued,ranging from how to segment their business and what different areasthey can focus on, to reevaluating where they areunderperforming.

“When you look at wanting to reach those younger consumers underage 35, the data tells a great story of who they are, where theyare shopping and how often. That can be used to make betterdecisions,” said Prettitore. “Rather than following what big banksare doing, credit unions can use the data to make informeddecisions based on their own members.” 

For the past five years, the $736 million San Mateo Credit Unionin Redwood City, Calif., has also been proactively using analytics,rather than relying on intuition, to make decisions with a basis inreality, Stephen Tabler, vice president of marketing, who hasworked with his team on the effort. This was especially criticalduring the economic downturn because San Mateo CU, like manyothers, were carefully watching the bottom line as it kept an evenkeener eye on where the expenses were.  

“Early on, a board member spoke up and asked, 'when things getbetter, are we going to be ready for opportunities as they arise,'”Tabler said. 

To that end, the executive team invested in conducting a marketarea analysis, which provided two hierarchy rankings of zip codes.The first was member growth propensity in order of bestopportunities for growth overall and member loan growth. The secondprovided 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 underservedarea in East Palo Alto that had opened nine years ago, announced itwas closing because the branch was 'only marginallyprofitable.'” 

San Mateo CU CEO Barry Jolette and an executive assistanthappened to be attending a meeting. In passing, leaders from acommunity group in the underserved area asked if the credit unioncould 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 Hispanicpopulation. “That zip code was ranked sixth in auto loan andoverall growth propensity, yet the entire nine years the bank,which specialized in small business, was there, they had not asingle mortgage or auto loan” said Tabler. 

With branches, a credit union may usually aim toward a five-yeargoal of profitability, Tabler explained. San Mateo CU's new branchreached that goal within two years. It helped that because theexisting location belonged to a bank, there weren't the typicalcosts associated with building a branch. 

“It was economically feasible for us to make the right decisionwithin a short period of time,” Tabler said. “The only reason wehave a fast-growing branch in an underserved community is becausewe already had the research at our fingertips.”

When it comes to data, some expert say the analysis shouldstrive to be working, living document that helps empower teamsacross the organization to gain an understanding of how to bestserve the membership. 

“There is no silver bullet. “It boils down to know thy memberand it's a continual process of getting to the next level of thedata and what else can you pull out to generate some results,”Norris said. “You've also got to do ROI analysis before and afterevery campaign. We aim for more hits than misses but I don't mindthe misses because what we learn from them gets us to the nextlevel.”

Once the data is gathered, the process is dictated byinformation revealed. Tabler said focus and simplicity are key tonot being overwhelmed by the sheer volume of data available.

“We try to keep it as simple as possible in the context ofprimary products and drill down from there to build and applyprofile reports in different ways,” said Tabler. “Yes, pulling datagenerates huge reports, but what happens is once you find it'ssuccessful and repeatable, you keep searching to find newopportunities with the data you have.”