Most credit unions don’t lose members over rates. They lose them because members don’t feel known. Here’s how AI-powered, member-centric marketing fixes that.
Most credit unions aren’t losing members because of bad rates or weak products. They’re losing them because members don’t feel known.
Karen McGaughey from Strum said it plainly on The CUInsight Network: “It’s no longer an option to move slow.” She was talking about marketing, data, and digital experience—but really, she was talking about survival. Member expectations have shifted faster in the last three years than in the previous ten, and the only credit unions winning are the ones shifting from product-centric to member-centric strategies, powered by data and AI.
This post builds on Karen’s perspective and connects it to a bigger theme in this series: AI for Credit Unions: Member-Centric Banking. We’ll walk through how AI, smart marketing strategy, and real member insight come together to create experiences that feel personal, relevant, and trustworthy—at scale.
From Product-Centric to Member-Centric: What Actually Changes
The core shift is simple: member-centric credit unions design marketing, products, and experiences around real member needs and behavior, not around internal product silos. AI just makes this shift faster, more precise, and more scalable.
Here’s what that looks like in practice:
- Product-centric: “We’re launching a new HELOC. Let’s email everyone.”
- Member-centric with AI: “Identify homeowners with high credit card utilization, strong repayment history, and equity in their homes. Target them with a HELOC offer framed as a debt-consolidation solution and follow up with a financial wellness journey.”
Same product. Completely different relevance.
AI helps you move from guessing to evidence-based personalization:
- It predicts which members are likely to need an auto loan in the next 90 days.
- It spots members showing early signs of attrition.
- It tailors channel, offer, and timing based on behavior and preferences.
Most credit unions talk about “knowing their members.” The ones who actually do are using data and AI to prove it every day, at every touchpoint.
You Can’t Move Fast Without a Strategy and a Team
Karen’s point about speed is spot on: you can’t afford 12–18 month marketing cycles anymore. But moving fast doesn’t mean being reactive or chaotic. It means having a clear strategy, the right people, and the right tools.
What the right foundation looks like
A credit union that’s ready for AI-powered, member-centric marketing usually has:
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A clear member strategy
- Defined priority segments (e.g., young families, small business owners, retirees).
- Clarity on what “success” looks like for each: adoption, engagement, share of wallet, digital usage.
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A dedicated data + marketing squad
- Not just one analyst buried in IT.
- A cross-functional team that includes marketing, data/analytics, operations, and member experience.
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A realistic roadmap
- Start with 2–3 high-impact journeys: onboarding, cross-sell, and retention.
- Layer AI capabilities gradually instead of trying to automate everything at once.
This is where agencies like Strum often come in: they bring marketing strategy, brand clarity, and data analytics muscle that many credit unions don’t have in-house yet. I’ve found that the best outcomes come from partnerships, not handoffs—your team understands your members, the agency understands the frameworks and tech, and AI ties it together.
Turning Data Into Insight: The Real Engine of Member-Centric AI
Most credit unions are sitting on a goldmine of data and using maybe 5–10% of it for anything meaningful. The gap isn’t data volume; it’s data activation.
Start with the data you already have
You likely have:
- Core transaction data (spend patterns, payment behavior)
- Digital banking data (logins, features used, drop-off points)
- Loan and credit data (balances, rates, performance)
- Demographic and household data
AI can analyze this data and answer questions your teams have been guessing at for years:
- Who is likely to need a loan, and when?
- Which members are under-served and could deepen their relationship?
- Which segments are at highest risk of churn?
For example:
- Members who stop using your credit card at gas and grocery but are still transacting heavily overall have likely switched cards.
- Members who log into digital banking less, stop opening emails, and stop visiting branches often show attrition signals 60–90 days before they leave.
AI models can flag these patterns and push them into your CRM or marketing platform as next-best-actions, so your team can respond with relevant outreach instead of generic campaigns.
Data + storytelling = differentiation
Karen emphasizes storytelling, and she’s right: data without narrative doesn’t move people. The win is combining data insight with a brand story that actually means something to members.
Example approach:
- Data insight: Members aged 25–35 with checking + debit but no credit card are heavy digital users and often overdraft.
- Story: “We’re here to help you move from financial stress to control.”
- Offer: An AI-identified segment gets a tailored campaign around a low-limit starter credit card plus in-app budgeting tools and overdraft-avoidance tips.
That’s not just cross-sell. That’s member-centric financial wellness, powered by analytics and AI.
Where AI Delivers Immediate Impact for Credit Unions
AI for credit unions isn’t just about chatbots. It spans multiple parts of the member journey. Here are five high-impact areas that connect directly to marketing and member-centric strategy.
1. Smarter member marketing and personalization
AI models can:
- Score members on likelihood to respond to a product offer.
- Recommend the best channel: email, SMS, in-app, phone, or branch.
- Adjust frequency to avoid over-communication and fatigue.
Result: fewer “spray and pray” campaigns, more targeted journeys. I’ve seen institutions increase campaign response rates by 30–50% just by using simple propensity models.
2. Fraud detection that still feels human
Members care about security as much as convenience. AI-driven fraud detection systems can:
- Spot abnormal behavior patterns within milliseconds.
- Reduce false positives by learning each member’s normal habits.
Marketing’s role here? Communicate clearly. When members understand why they’re getting alerts and how their credit union is protecting them, trust grows. That trust makes every future interaction easier.
3. Fairer, faster loan decisioning
AI-powered decisioning, done responsibly, helps credit unions:
- Use more data points than traditional scores alone.
- Process applications faster with consistent criteria.
- Expand access to credit while managing risk.
The member-centric angle: explain the process. Show members how your credit union uses technology to be more fair, not less human. That’s a story worth telling in your brand and marketing.
4. Member service automation that actually helps
AI chatbots and virtual assistants aren’t about replacing staff; they’re about:
- Answering simple questions 24/7.
- Guiding members to the right products or human experts.
- Surfacing insights from conversations back into your data ecosystem.
Set a high bar: if the bot can’t resolve an issue quickly, it should hand off smoothly to a person with full context. When that works, member satisfaction scores usually rise, not fall.
5. Financial wellness tools that keep members loyal
AI-powered budgeting and wellness tools can:
- Categorize spending in real time.
- Nudge members when they’re close to overspending.
- Suggest savings or payoff strategies based on real behavior.
Members don’t just remember the rate you gave them. They remember who helped them feel in control of their money. That’s long-term loyalty you can’t buy with traditional advertising.
How to Get Started Without Overwhelming Your Team
The reality? You don’t need a massive AI lab to make progress in 2026. You need clear priorities and the discipline to ship small wins.
Here’s a simple path I recommend to credit union leaders:
Step 1: Pick one critical journey
Good starting points:
- New member onboarding
- Card or loan usage deepening
- At-risk member retention
Define what “good” looks like: higher activation, more digital engagement, better product adoption, lower churn.
Step 2: Map the data you already have
For that one journey, list:
- What you know today (e.g., age, products, balances, behaviors)
- What you wish you knew (e.g., financial goals, preferred channels)
- Where that data lives (core, CRM, LOS, digital banking, surveys)
Then identify one or two AI use cases: e.g., predicting which new members are most likely to become “primary” relationships, or which are at risk of going dormant.
Step 3: Partner where it makes sense
Agencies and AI vendors can:
- Help build or configure models.
- Integrate data sources.
- Design campaigns and creative that line up with insights.
Your team stays focused on what only you can own: understanding your members, your market, and your mission.
Step 4: Measure, learn, and iterate
Avoid vanity metrics. Track:
- Response rate and conversion rate by segment.
- Digital engagement before and after AI-powered campaigns.
- Member satisfaction and NPS on key journeys.
Treat AI as a learning engine. Every campaign, every interaction, every test makes the next one smarter.
The Credit Unions That Win Will Know Their Members Best
Karen’s advice to credit unions was clear: focus on strategies that help you know your members better than anyone else. AI, done right, is how you scale that knowing.
For this AI for Credit Unions: Member-Centric Banking series, the pattern is emerging:
- Data and AI turn raw information into real member insight.
- Insight powers more human, relevant storytelling.
- Member-centric experiences build loyalty, deposits, and growth.
The credit unions that thrive over the next few years won’t necessarily be the ones with the biggest budgets. They’ll be the ones that combine strategy, storytelling, and AI to consistently answer one question in every campaign and every interaction:
“Does this make a real member feel more understood, more supported, and more confident in their money?”
If your answer is yes—and you can prove it with data—you’re on the right track. If not, this is the moment to change course.
Now is the time to move faster, get sharper with data, and build AI-powered, member-centric journeys that actually feel human.