LAS VEGAS–On average, a credit union has between 60 and 100 different data systems, but by bringing some focus and discipline to all that information, it can improve member service and drive results to the bottom line, according to two people.
In short, credit unions need to make better use of data analytics, attendees at the CUNA Marketing & Business Development Council’s annual meeting were told by Anne Legg, director of client/market strategies with CUNA Mutual’s AdvantEdge Analytics, and Emily Engstron, director of client success with CUNA Mutual.
What data analytics is really allow about, said Legg, is taking the hug of credit unions and adding a brain to it.
“We need to be thinking about that data that makes the member’s life better. It’s the head part,” said Legg. “It’s about data-centricity. Why does the member come to you in the first place?” The answer, she said, is to find answers related to shelter, transportation, credit or retirement.
It’s critical staff be part of that data journey, stressed Legg, as at some point the CU’s employees are going to interact with members and they need to know the data that’s all around the individual member.
Speaking of staff, Legg, who said many credit unions remain unsure of what other relationships members have with other providers, told the group, “We have found there is a gap in data consumption.” She urged the creation of data savvy teams that can get the level of data to the front line that makes them “member obsessed.”
“We need to think about solving members’ problems in cross-functional teams. Many people will say, ‘Just give it to marketing.’ Everybody has to be working to understand how we are solving,” said Legg. “The final piece is to think about how are we going to do this in a fast way. We have to be agile, we have to start thinking of doing things in two-week sprints.”
This includes data governance, which is increasingly important as every CU is going to be audited on their data.
Data Process
According to Legg, the strategic priorities of the organization determine the appropriate data governance operating model and provide a linkage to business value and stakeholder engagement. What this allows is prioritization, said Legg. “Data elements are prioritized by the enterprise based on their level of criticality.
Metadata and Data Lineage
“Metadata and data lineage will be documented and published in an Enterprise Data Dictionary to drive common understanding and consistent usage of data,” she said.
And what does all that mean? That’s just a dictionary of the data in the credit union, said Legg.
Data Practice
Engstrom urged CUs not to begin with the data, but instead start with what the problem is for what the CU is trying to solve.
In looking to use cases, a common issue is member attrition, according to Engstrom, who said many wrestle at every month-end with why members left. “It’s about what’s in the data that can predict the member’s likelihood to leave. What are the small incremental things your members are doing that are causing them to potentially leave the CU or reduce in value.”
The other issue CUs raise, of course, is what Engstrom called the “Holy Grail,” indirect members and turning them into active members.
Member Attrition
When you can really understand the member data you can better engage to bring them back into the credit union,” said Engstrom. “You want to identify your highest value members who have the propensity to leave, versus your lower value members.”
Action
It’s not just about understanding the data and having a strategy, but there must also be a strategy on how to execute on the data, said Engstrom. “And this is where marketing is really important.”
“When we work with credit unions we see varying results on actually executing on the data,” said Engstrom. “How engaged the marketing team is in data analytics is really the precursor to how successful they are going to be.”
Product Propensities
Engstrom noted CUNA Mutual has access to a “ton” of member data from third parties, due to the company’s scale, including behavioral analysis and member attributes related to insurance products. The pool includes 59-million members.
Members can fall into 54 different segments as defined by AdvantEdge Analytics. Who makes up these members and their behavior propensities?
“We hear a lot that we don’t do direct mail, it’s all online. But what the data show is people respond to mail. Members have a very high propensity to respond to direct mail versus another channel.”
What can you do when you get back to your CU with the data you have in your system?
Some small steps to take include going to the core system and pulling a query on ACHs, where members are spending funds. “Knowing where your members are spending and sending their money, and how to engage with those members to keep the funds here, to keep the loans here and reacquire them,” said Engstrom.
Engstrom also pointed to credit card transactional data as another source of terrific information. Knowing members shopping at Home Depot or Lowe’s signals potential home improvements may be under way.
Why is Target low on credit card transactions? Because so many consumers use its Red Card to get a discount (and save Target on interchange). The Red Card can be a red flag among others for potential decreases in interchange income.
Number one for the use of credit cards by members? Amazon, which is partnering with American Express. “If Amazon were actually a bank, where does that put you,” asked Legg.
Every credit union needs to think hard on how to become top of card on Amazon purchases. That might include rewards points for Amazon purchases using the credit union’s card.