LEWISTON, Idaho—Potlatch No. 1 Financial Credit Union (P1FCU) is projecting a significant increase in revenue and member engagement after refining how it markets its checking account products.
The $2.1-billion P1FCU had been seeking to improve adoption and engagement of its checking accounts and is being featured here as part of the CU Daily’s ongoing 2026 series “The Profitability Imperative.”
According to the credit union, it turned to segmentation and propensity modeling to analyze its membership base, focusing on the top 10% of members most likely to engage with checking accounts and the bottom 10% least likely to do so. The analysis identified key demographic and behavioral traits—including income level, home ownership and property type—that distinguish high-potential members.
The credit union used Pittsburgh-based BlastPoint for the analysis, which said its success is based as much on what it didn’t do, as what it did do.
What came out of the data analysis were insights allowed P1FCU to shift from broad-based marketing to more targeted campaigns aimed at specific member segments, according to the organizations. It then assigned propensity scores to individuals to estimate their likelihood of engagement, enabling P1FCU to more efficiently allocate marketing resources and tailor messaging.
Where to Start
Where should a credit union start when it comes to leveraging the data it has?
According to BlastPoint, with the data it already has.
“Most CUs are sitting on gold: core data, transactions, tenure, product holdings, channel usage and call center notes that they’ve never really put to work,” said Tomer Borenstein, CTO with BlastPoint. “The trick is to pick one specific question tied to revenue, not ‘let’s do analytics’ as a general project. For P1FCU it was literally: who, out of our entire membership, is most likely to open a new checking account in the next 90 days? Work backward from the answer you need to the data you already have. That’s what turns analytics into a revenue line.
“And internal data is only part of the picture. There’s a surprising amount of useful external data available to CUs for free,” Borenstein continued. “The CUScorecard alone is a deep resource, and there’s plenty more beyond that. We put together a short guide on layering it in (Beyond Internal Data). Most CUs have more to work with than they realize before anyone ever needs to pay for third-party data.”
36% Open Rate
P1FCU and BlastPoint said the resulting campaigns generated a 36% open rate—about 80% higher than industry benchmarks—and produced an average of 31 new checking accounts per campaign.
If scaled, the model is expected to generate approximately 298 new checking accounts annually, with projected revenue of up to $447,000 and an indirect member conversion rate of as much as 12%, according to BlastPoint’s analysis.
BlastPoint said its analytics platform enabled P1FCU to move toward a more data-driven and automated approach to member engagement, positioning the credit union for continued growth.
The ‘Key’
The key in P1FCU’s case, according to Borenstein, was that it didn’t begin by buying a bigger list but instead by “asking a better question.”
“At first, they did what most credit unions do–they try to convert their legacy checking holders into the new product,” Borenstein told the CU Daily. “That’s a pretty narrow play. What actually worked was going wider and more specific at the same time: using propensity to find likely members across their whole member base, not just the people who already held an older account. They stopped asking ‘Who looks like a checking member?’ and started asking, ‘Who’s actually ready to become one?’”
According to Borestein, the numbers cited above, such as the 36% open rate (which is 80% above industry benchmark), the 31 new checking accounts per campaign and, once automated, the projected 298 new accounts a year have resulted in a 22x conversion lift that gets the headline, “but the real story is that they stopped casting a wide net and started listening to what their own data was telling them.”
Where the Revenue is Coming From
The figure that will get the attention of many CU leaders if the $47,000 in projected revenue. Where does that figure come from?
“It’s about relationships, not fees,” said Borenstein. “And the dollar figure itself is almost beside the point. That $447,000 at P1FCU’s scale will look different at a $300M shop than a $3 billion one. What matters is the shape of the return, because that’s what travels from one credit union to another.”
Specifically, Borenstein said the math works out this way: 298 projected new checking accounts × $1,500 in annual value per relationship = $447,000.
“That $1,500 isn’t fee income,” he said. “It’s what a primary checking relationship is actually worth: the deposits that sit with you, the debit activity and the loans and cards that tend to come with it.”
Borenstein pointed CU Daily readers to the ratios underneath the number, saying those scale regardless of CU size.
Cost is 80% Below Industry Average
“P1FCU’s cost of member acquisition on this work now runs roughly 80% below the industry average,” he explained. “Their conversion rate from indirect members to multi-product core members has grown to 11.4%. And we’ve seen a 22x lift in conversion on targeted campaigns. Put it together and you get a consistent picture: cheaper to acquire the right members, and those members go deeper with you once they’re in. That math works whether you’re $300 million or $3-billion.
“The ROI here isn’t about squeezing existing members harder,” he added. “It’s about finding the members who are genuinely ready to go deeper with you, and building that relationship before another institution does. You can compete on fees, or you can build relationships that compound.”
Where Many CUs Get ‘Stuck’
Why don’t other credit unions see the kind of success being experienced by Potlatch #1 FCU? Because they’re “stuck,” according to Borenstein.
“Segmentation still tends to mean sorting members by demographics – think ‘Millennials in this ZIP with balances over $5K.’ That’s descriptive; it tells you who a member is, not what they’re about to do,” Borenstein said. “Propensity comes at it differently. It scores each member by their likelihood to take a specific action in a specific window. It’s predictive and individual. Two members with near-identical demographics can have wildly different propensity scores because their behavior in the data is different.
“In plain terms: demographic segmentation tells you who to put on a mailing list. Propensity tells you who to call on Tuesday,” he added.
Moreover, what is often “overlooked,” according to Borenstein, is that it’s not about propensity vs. segmentation, it’s about both, with each doing different things.
“Propensity tells you who to reach out to, and when. Segmentation, done well, with behavioral and attribute data, not just demographics, tells you how: what message to design, which channel will actually land, what tone fits that member,” he said. “Propensity gets you to the right list. Segmentation gets the right message into their hands. Run them together and you stop wasting motion on either end.”
