PALM DESERT, Calif.–Credit unions have opportunity to grab more market share by using credit data CUs already have on members and then asking the right questions.
The questions that needs to be asked, Melton Knight of Experian told the California/Nevada Leagues’ REACH meeting here, are these:
- Do you know your share with members and what they have elsewhere?
- Do you have insight into how members perform “off you?” “We know our members pay us better than others, but can we quantify it.”
- Which loan products should you grow? What would your members be most receptive to?
Knight told credit unions credit file data can reveal a lot of information that can in turn be used to drive the strategic pan
“At a high level, you can get a sense of where your assets sit,” said Melton. “You can get balance trends over time, yield trends over time. You can define your loans by type. But the whole story is what do those same members have off-book. You might find another $150 million in auto loans somewhere else.”
Using auto loans and credit cards as specific examples, Knight said credit data can reveal where members have loans elsewhere and what they are paying for those loans.
“What if you could understand the kinds of interest rates they are paying?” asked Knight. “On auto loans, you might find many members paying 7.5% APRs or higher on auto loans. That is where you want to start.”
Using a credit union with which Experian has worked as an example, he said the CU found 73% of its assets were with members who were prime or better, but it had just 51% of those members’ auto loans. That meant $60 million in auto loans with prime members had gone to other lenders.
The same credit union also found that 10% of its members had student loans outstanding, although the CU itself had zero student loans on its books.
“What if you could focus your marketing dollars on consumers and members more likely to open a credit union account over a bank account,” asked Knight, who pointed to Experian data and analysis that can help a credit union identify those members.
It identifies those members by breaking them into clusters, such as the “I love my credit union” cluster and the “I love my bank cluster,” the latter identified by credit files showing members with most accounts at a bank.
“Information can be used to filter not just members, but prospects who also have an affinity for CUs,” he said.
Specific product clusters can also be created around auto, credit card, HELOC, mortgage and personal loans, and other products.
Knight said when that kind of information has been used, credit unions have seen “amazing lift.”
“The application of the cluster on top of what you are already doing from a marketing perspective (delivers a) minimum of 200% increase,” according to Knight.
Knight paid particular attention to understanding which members are doing business “off you,” as well as analysis that can show which loan products are most likely to grow.
Using that information, for example, a CU can create “micro-segments” for an auto loan recapture campaign targeting those “off us” auto loans. Micro-segments can include members who have high-rate loans about to expire; non-members who are non-prime but paying low rates, and auto thin files.
“With those three segments, you now can create very specific segment marketing strategies,” said Knight. “You want to focus recapture campaigns on members who have an incentive to move.”
Those types of efforts, he reminded, reduce marketing costs by focusing on just the members who are ideal targets for the message.