AI only works for credit unions when brand, data, and staff are aligned. See how Strum’s approach turns analytics and AI into real member-centered growth.
Why AI-Driven Branding Matters for Credit Unions Right Now
Credit union leaders are staring at a tough combo in late 2025: member expectations shaped by big tech, shrinking margins, and fintechs targeting your best members. The credit unions that are still growing have one thing in common — they treat brand, data, and AI as one connected system, not three separate projects.
That’s exactly where Strum sits. What started as Weber Marketing Group is now Strum Agency (brand and growth strategy) and Strum Platform (financial marketing analytics). And the core idea Ben Stangland pushes is simple:
“The more information you can empower employees with, the better the whole organization is.”
This post builds on that conversation from The CUInsight Network and connects it directly to our AI for Credit Unions: Member-Centric Banking series. We’ll look at how branding, data, and AI come together to:
- Clarify who your target members really are
- Use your existing data in smarter, more member-centric ways
- Execute AI-driven campaigns your staff can actually use
- Rebrand without losing your soul as a member-owned cooperative
If you’re trying to move from “random acts of marketing” to a clear, AI-supported growth strategy, this is for you.
Brand Strategy + AI Analytics: Two Sides of the Same Coin
The key idea: AI works best when it’s pointed at a clear brand and growth strategy. Without that, it’s just expensive math.
Strum splits this into two complementary engines:
- Strum Agency – naming, branding, and growth strategy for credit unions
- Strum Platform – financial marketing analytics software that turns member data into action
Most credit unions treat these as different conversations: “brand” with marketing, “data” with IT, and “AI” with whoever just went to a conference. That separation kills impact.
Why brand focus has to come first
Ben’s work with credit unions often starts with a hard question: Who are you really trying to grow with? Not “everyone in our field of membership,” but specific segments:
- Emerging professionals building credit
- Families juggling debt and savings
- Small business owners in key local industries
Once you define that, AI suddenly has a job:
- Predict which members are most likely to need an auto loan in the next 60 days
- Identify which households are at risk of churn
- Find members who look like your most profitable core segment
AI for credit unions is member-centric only when it’s branded and targeted. Otherwise you’re just sending more generic messages faster.
Using Data You Already Have to Understand Members Better
The fastest way to boost member-centric AI isn’t buying more tools. It’s liberating the data you already have from silos.
Strum Platform is built around what most CUs already sit on:
- Core transaction data
- Loan and product history
- Digital banking and call center interactions
- Basic demographics and householding
Then it can supplement with additional data sets (psychographics, propensity scores, etc.) to deepen insight. But the real difference comes from how that data is shared internally.
Ben stresses the cost of silos: when only a few people see the data, the entire organization loses.
What “data in the hands of employees” actually looks like
Here’s what I’ve seen work well when CUs embrace this mindset:
- Frontline staff dashboards show member life stage, product mix, and likely next-best product in plain language
- Marketing teams build campaigns based on real behavioral segments, not just age ranges
- Lending teams use AI scores coupled with policy rules to speed up approvals while still honoring risk standards
For example, instead of a generic “Open a HELOC” campaign, you target:
- Homeowners with 40%+ equity
- Who used a credit card to cover more than $1,000 in the last 90 days
- Who’ve engaged with financial wellness content recently
That’s AI-enhanced, member-centric marketing grounded in reality, not hype.
AI, Machine Learning, and Cloud: What Actually Matters
Ben talks about machine learning, artificial intelligence, and cloud-based computing as core to Strum’s evolution. For CU executives, the tech stack isn’t the interesting part. What matters is what changes on Monday morning for your team.
Here’s the thing about AI for credit unions: the value shows up in a small number of very specific use cases.
High-impact AI use cases for credit unions
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Propensity modeling for product offers
Use ML models to predict which members are most likely to need:- Auto loans
- Credit cards or line-of-credit relief
- HELOCs
- First mortgages or refis
Then prioritize offers and cadence based on those scores.
-
Churn and relationship risk detection
Spot members who:- Shift direct deposit away
- Reduce debit/credit usage sharply
- Stop logging into digital banking
Trigger human outreach or tailored offers, not just another email blast.
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Next-best action in member service
When a member calls or chats, AI models can suggest:- Relevant questions to ask
- Products that match their behavior
- Education or financial wellness content that fits their situation
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Fraud monitoring and anomaly detection
Using behavioral patterns rather than only hard rules (amount, location) can reduce false positives and catch sophisticated fraud earlier.
Why cloud-based matters for smaller credit unions
Cloud-based platforms give mid-sized and community credit unions access to:
- Scalable compute power for ML models
- Frequent model updates and improvements
- Easier integration with digital banking, CRM, and martech tools
You don’t need a massive internal data science team. You need a partner who understands member-centric banking and cooperative values, then brings the AI and cloud details along for the ride.
Rebranding in an AI Era: Don’t Lose Your Cooperative Soul
Strum’s own evolution from Weber Marketing Group to Strum Agency and Strum Platform is a useful blueprint for CUs facing renaming, mergers, or major repositioning.
Many credit unions are wrestling with:
- Fields of membership that no longer match the original name
- Growth beyond SEG-based identity
- The fear that rebranding means “becoming a bank”
Ben’s stance — and I agree — is that rebranding done right makes your mission more visible and more actionable, not less.
How AI can support a healthier rebrand
When credit unions lean into AI and analytics during a rebrand, they can:
- Validate the target member strategy: Are we picking segments where we truly have an advantage?
- Test brand narratives: Which storylines actually resonate with core and growth segments?
- Measure impact quickly: Track how different segments respond to new messaging, digital experiences, and offers.
A practical approach:
- Use member data to map your real membership footprint — age, life stage, products, geography.
- Identify 2–3 priority segments that line up with your cooperative strengths.
- Involve staff from across departments so the new brand matches internal reality.
- Launch AI-informed campaigns under the new brand that are explicitly member-centric.
The worst rebrands are cosmetic. The best ones reshape how you use data, how you talk to members, and how your employees make decisions.
Making Data Sharing a Cultural Habit, Not a One-Off Project
Ben’s quote about empowering employees with information isn’t just a nice line — it’s a cultural test.
If only marketing and IT see the insights from your AI tools, you’re paying for a spotlight and keeping it in a closet.
Credit unions that actually become “member-centric with AI” do a few things differently.
Practical steps to break data silos
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Create a shared scorecard
Build one simple dashboard everyone sees monthly: member growth by segment, engagement, digital adoption, and primary FI status where possible. -
Translate analytics into stories
Don’t just show charts. Tell frontline staff: “Members like Sarah — teachers in their 30s with kids — are increasingly leaving us for fintechs for small-dollar credit. Here’s what we’re doing about it, and here’s how you can help.” -
Train for AI-informed conversations
When next-best-action or propensity scores surface in the CRM, give staff simple scripts and permission to use them. If a member’s likely to need debt consolidation, that’s a conversation starter, not a robotic sales pitch. -
Reward teams for using insights
Celebrate branches and departments that act on data and share learnings, not just those that hit raw sales numbers.
This is where Ben’s mix of analytics and human touch really shows: he talks about bullet journaling, jazz, and family in the same breath as AI and data. That’s the mindset credit unions need — technology that amplifies human connection, not replaces it.
Where to Start: A Simple Roadmap for AI-Ready Brand Growth
If you’re feeling behind on AI or overwhelmed by rebranding, you don’t need a 3-year moonshot. You need one clear, member-focused next step. Here’s a practical path that matches the Strum approach and fits our AI for Credit Unions: Member-Centric Banking series.
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Clarify your target members
Define 2–3 segments you want to grow with. Be specific. Tie them to your cooperative mission. -
Audit your data foundation
List where key member data lives: core, LOS, CRM, digital, contact center. Identify the top two silos blocking insight. -
Pilot one AI use case
Don’t try to do everything. Choose one:- Propensity-based auto loan campaign
- Churn-risk outreach program
- AI-assisted member service suggestions in the contact center
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Share results widely
Show staff what changed: response rates, member satisfaction, product adoption, or reduced attrition. -
Align brand messaging around member outcomes
Make sure your brand — name, visuals, and voice — talks about what you actually help members achieve, backed by data.
The reality? AI for credit unions isn’t just about smarter models. It’s about clear brand focus, data in motion, and employees who feel trusted with information. Strum’s journey from a traditional marketing agency to a combined agency–platform model is proof that when you connect those dots, you can grow without becoming another generic financial brand.
If your team is serious about member-centric banking in 2026 and beyond, your next strategic discussion shouldn’t be “Should we use AI?” It should be: “How do we align our brand, our data, and our people so AI actually serves our members?”