3 minutes
CU incorporates dat analysis into every decision.
At $2.4 billion Provident Credit Union, Redwood Shores, Calif., “We strive to incorporate data analysis into everything we do and every decision we make,” says CUES member John Haggarty, VP/marketing for $2.4 billion Provident Credit Union, Redwood Shores, Calif. It’s so engrained in the CU’s culture, that employees get mugs that read “Without data, you are just another person with an opinion.”
Two examples of how the credit union used data to make improvements follow.
Super Reward Checking:
Data precipitates a fundamental change in product design
Super Reward Checking is a flagship product targeted to a broad range of members and used as a tool to become their primary financial institution. Its main feature is a high rate of return on balances if members meet certain transaction criteria. Previously, the requirement for the higher rate was 10 monthly debit transactions from the account.
“After surveying our Super Reward Checking members and segmenting based on spending and behavioral patterns, we discovered a loophole in product design: [Some] members were performing incremental transactions to earn the high rate paid on their balances. We decided a change in product design was required,” explains CUES member John Haggarty, VP/marketing for the CU. “The product wasn’t meeting our goal of increasing monthly spend total (with this segment) or solidifying the PFI relationship.”
Because of the data analysis, the CU could not only see the extent of the group but also subsequently changed product requirements from frequency of spend to total monthly spend. “Rather than making a change based on instinct, we relied on the data to support the change,” Haggarty says.
Since implementing the change, Provident CU has seen a 34 percent increase in median spend per Super Reward Checking account.
Auto Loans:
A change in preapproval criteria based on data insights provides a dramatic lift in response.
In years past, Provident CU routinely mailed one auto loan solicitation a year to a range of prospects, based on credit scores. “We assumed these groups, no matter their score, would respond the same,” explains Haggarty. “Through data analysis, we determined that higher scores didn’t respond to the offer nearly so well compared to those with lower scores. It led us to make a significant change in how we purchase our direct mail lists and the criteria we select, which now focuses on the middle group of credit scores and eliminates high scores altogether. By homing in on the right group, we can mail offers more frequently. Now, we’re mailing loan offers four times per year to these middle groups, yielding a much higher response rate.”
This table shows the before and after results in non-member auto loan pre-approval campaigns. Using data to tweak its list criteria, the CU increased efficiency while funding more auto loans.
|
Prior to Criteria Change |
New Criteria Implemented |
% Change |
|
|
Non-Member Records |
48,499 |
39,809 |
-18% |
|
Loan Applications |
211 |
335 |
59% |
|
Fundings |
123 |
184 |
50% |
|
Funding Volume |
$2.82 M |
$3.83 M |
36% |
|
Funding Response Rate |
0.25% |
0.46% |
84% |
Stephanie Schwenn Sebring established and managed the marketing departments for three CUs and served in mentorship roles before launching her business. As owner of Fab Prose & Professional Writing, she assists CUs, industry suppliers and any company wanting great content and a clear brand voice. Follow her on Twitter @fabprose.
