Most credit unions are rich in member data but poor in insight. Here’s how to use AI and data-driven marketing to create truly member-centric experiences.
Most credit unions are sitting on a goldmine of member data and treating it like a filing cabinet.
Mark Weber from Strum likes to say, “Know your members better than you ever have.” The reality? Very few credit unions actually do. They know account balances, loan types, maybe a few demographics—but they don’t really know who members are, what they need next, or when they’re about to leave.
This matters because the next wave of growth in credit unions won’t come from more branches or broader rate campaigns. It’ll come from data-driven marketing and AI that make every interaction feel personal, relevant, and timely—at scale.
In this post, part of the AI for Credit Unions: Member-Centric Banking series, we’ll break down how credit unions can use data and AI to transform marketing from “spray and pray” to predictive, omnichannel, and member-obsessed.
Why Data-Driven Marketing Is Non‑Negotiable for Credit Unions
Data-driven marketing for credit unions is about one thing: using actual member behavior to decide who to talk to, about what, and when.
Most credit union marketing is still:
- Campaign-based instead of lifecycle-based
- Product-first instead of member-first
- Channel-siloed instead of omnichannel
AI flips this. When you connect core data, digital banking, call center logs, web analytics, and campaign responses into one analytics platform (like what Strum Platform is built for), you can stop guessing and start prioritizing.
The key shift: from demographics to behaviors
Demographics tell you who your members are on paper.
- Age 42, homeowner, two kids, income $95K.
Behavior tells you what they actually do and what they’re likely to do next.
- Logs into digital banking daily from mobile
- Frequently checks credit score
- Browses auto loan rates page 3x in a week
- Pays a high-rate auto loan to another institution
AI models can score:
- Propensity to buy (who’s likely to need an auto refi next 60 days)
- Propensity to churn (who’s shifting deposits away)
- Next-best product (which offer will most likely be accepted)
Once you have that, marketing stops being a calendar of promotions and becomes a stream of context-aware conversations across email, mobile, web, and in-branch.
Why this is urgent heading into 2026
As we close out 2025, three trends are colliding:
- Rate sensitivity is intense. Members are shopping hard, and switching is easier than ever.
- Big banks and fintechs already use AI for personalization. If your experience feels generic by comparison, you lose.
- Regulators are watching data and AI. The credit unions that learn to use AI responsibly now will be in a far better position than those who wait.
Ignoring data-driven marketing isn’t staying “safe”—it’s quietly eroding member relevance.
Omnichannel Tracking: Seeing the Full Member Journey
Omnichannel tracking means you track and connect member interactions across every channel—branch, call center, mobile app, website, email, SMS, even direct mail.
Mark Weber emphasizes this because member journeys are rarely linear anymore. A typical lending journey might look like this:
- Member sees a generic rate ad on social
- Visits your auto loan page twice but doesn’t apply
- Calls the contact center with a “general question”
- Walks into a branch two weeks later to “ask about payments”
If your systems aren’t stitched together, that’s four random events.
With good omnichannel tracking and AI, that’s a high-intent auto refi opportunity with a predicted value, likelihood to close, and recommended offer.
What omnichannel tracking should capture
At a minimum, your data-driven marketing stack should unify:
- Core & LOS data – balances, products, loan performance
- Digital banking behavior – logins, feature usage, device patterns
- Web analytics – visits to key product pages, abandonment points
- Marketing engagement – email opens/clicks, SMS responses, ad interactions
- Human interactions – notes from branch and call center, appointment bookings
When this data flows into a marketing analytics platform, AI can:
- Assign journey stages (e.g., “shopping,” “onboarded,” “at risk”)
- Trigger real-time messages (e.g., follow-up after loan application abandonment)
- Recommend next-best actions for frontline staff
Example: Turning passive browsing into an active conversation
Say a member:
- Logs in from mobile three times in a week
- Checks the “My FICO” feature twice
- Spends two minutes on your “Debt Consolidation” page
With omnichannel tracking plus AI:
- The model flags them as high propensity for personal loan or HELOC
- A personalized message appears in online banking: “Ready to simplify your debt? Here’s how much you could save based on your current relationship.”
- If they call the contact center, the rep sees a “Debt Consolidation Interest: High” tag and an approved script.
That’s member-centric banking, powered by data—not guesswork.
From Raw Data to Insight: What AI Actually Does for CU Marketing
AI in credit union marketing isn’t magic. It’s a set of tools that turn raw data into prioritized actions.
Here are the core AI capabilities that matter for a member-centric strategy:
1. Segmentation and micro‑audiences
Traditional segmentation: “Millennial homeowners in this ZIP code.”
AI-driven segmentation:
- “Members who are 60–90 days from likely needing an auto refi”
- “Small business owners with growing deposit balances and no credit line”
- “Members likely to defect in the next 6 months due to external payment patterns”
This is what platforms like Strum Platform are built to handle—behavior- and value-based segments that drive smarter campaigns and better ROI.
2. Propensity and churn models
Propensity models answer: Who is most likely to respond to a specific offer right now?
Churn models answer: Who is quietly disengaging or shifting away?
What this can look like:
- A churn model flags 2,000 high-value members at risk. Instead of a generic newsletter, they receive a tailored check-in from a “Member Success” team and targeted value-add offers.
- A mortgage propensity model identifies renters with stable direct deposits and rising balances—perfect candidates for a first-time homebuyer campaign.
The result: fewer blanket blasts, more surgical, high-yield outreach.
3. Next-best product and next-best action
Next-best product (NBP) is table stakes. Next-best action (NBA) is where things get interesting.
- NBP: “This member is most likely to open a credit card next.”
- NBA: “Based on their debt load and branch visit history, the best next action is a financial wellness consult, not a new credit line.”
For a member-centric credit union, NBA matters more. AI should help you do the right thing for the member, not just sell the next product.
4. Measurement and closed-loop learning
AI doesn’t stop at campaign send.
Strong data-driven marketing platforms feed actual outcomes back into the models:
- Who opened? Who clicked?
- Who applied? Who booked? Who funded?
- Who stayed? Who deepened the relationship?
Campaigns aren’t “did it work?” guesses—they’re exercises in constant learning where every month the model gets sharper.
Practical Steps to Build a Data-Driven, AI‑Ready Marketing Program
You don’t need a giant team or a Silicon Valley budget. But you do need a plan.
Here’s a practical roadmap I’ve seen work for credit unions from $500M to $10B in assets.
Step 1: Get your data house in order
No platform or AI model can fix chaotic data.
Focus on:
- A unified member ID across core, LOS, CRM, and digital channels
- Clear data ownership between IT, marketing, and analytics
- A basic member 360 view that marketers can actually access
If you’re working with a partner like Strum, this is usually where they start—they build the plumbing before turning on the analytics.
Step 2: Start with 2–3 high-value use cases
Don’t try to “AI everything.” Pick a few use cases with fast, visible impact, such as:
- Auto refinance recapture
- First-time homebuyer nurture journeys
- Deposit retention for high-balance members
- Reactivation of dormant digital banking users
For each use case, define:
- Target audience (who)
- Trigger or signal (when)
- Offer and experience (what)
- Success metrics (how you’ll judge it)
Step 3: Build omnichannel journeys, not one-off campaigns
For each use case, map a simple multi-touch journey:
- Email 1: Education or value story
- Email 2: Personalized savings example or calculator
- SMS/Push: Friendly reminder or deadline
- Web/App: Personalized banner or offer tile
- Branch/Call Center: Talking points and visible cues in the CRM
The goal is for the member to feel like one coherent conversation is happening, no matter how they show up.
Step 4: Layer in AI scoring and automation
Once the first journeys are live:
- Add propensity scores to prioritize who enters the journey
- Use AI-driven send time optimization to schedule messages
- Test content variations based on member segment (e.g., renters vs. homeowners)
Even basic AI here can lift conversion rates by 20–40%. I’ve seen credit unions double campaign performance simply by using behavior-based triggers instead of static lists.
Step 5: Report like a CFO is watching
To keep momentum (and budget), translate results into business outcomes, not just marketing metrics.
Report things like:
- New loans or deposits attributable to the journeys
- Net interest income from recaptured loans
- Decrease in churn rate for targeted segments
- Cost per funded account vs. previous campaigns
When you show data-driven marketing as revenue infrastructure, not “nice visuals,” leadership support follows quickly.
Guardrails: Doing AI Marketing the Right Way
AI needs boundaries, especially in a values-driven movement like credit unions.
Here’s what “responsible AI marketing” looks like in practice:
- Transparency: Be clear in your privacy notices about how member data is used for personalization.
- Fairness: Regularly test models to ensure certain groups aren’t being unfairly excluded or targeted.
- Explainability: Choose tools and partners that can explain why a model is recommending an action.
- Human oversight: Keep humans in the loop for sensitive decisions—especially lending and collections.
Used well, AI helps you be more human, not less. It gives staff the insight to have better conversations, not scripts to replace empathy.
Where This Fits in Your Member-Centric AI Strategy
Data-driven marketing is the connective tissue of AI for Credit Unions: Member-Centric Banking.
- Fraud models protect members.
- AI underwriting expands fair access to credit.
- Virtual assistants handle simple service.
- Financial wellness tools guide better decisions.
But if your marketing and member engagement aren’t data-driven, members never fully experience the value of the rest.
The next logical step for any credit union is simple:
- Audit how you’re using member data today
- Identify one or two high-impact journeys where AI could sharpen targeting or timing
- Talk with your internal team or a partner like Strum about the analytics foundation you’ll need
Credit unions were built on relationships. AI and data-driven marketing don’t replace that—they scale it. The institutions that win over the next five years will be the ones whose members say, “They just get me.”