The following is an article written by Trellance’s Product Manager, Predictive Analytics, Avery Swiontek. It originally appeared on CUInsight.com.
Let’s face it—credit unions want to grow, but not all members are created equal. Some members become loyal advocates, actively using multiple products, referring friends and engaging deeply with the credit union. Others may only open a single account and remain inactive. The challenge is figuring out how to find more of the former and fewer of the latter.
Membership Growth Challenges: Navigating Saturation
Credit union membership is indeed growing nationwide, but the rate of growth is slowing, especially in mature markets. In many areas, particularly urban regions, financial consumers are already affiliated with multiple institutions, making the competition for attention and loyalty fiercer than ever. In this environment, it’s no longer enough to rely on traditional marketing strategies or broad demographics. Credit unions need smarter, sharper ways to find and connect with potential members who are more likely to join, and, more importantly, stay.
This is where predictive analytics shine. By identifying patterns in existing high-performing member segments, it becomes possible to find new members who are statistically more likely to behave in similar, high-value ways.
Predictive New Member Acquisition Models: The Smart Crystal Ball
New member acquisition AI models act like a smart crystal ball—but one based on data, not guesswork. At the heart of these models are a deep analysis of your current membership, especially those with high engagement scores. These are members who use multiple services, log into online banking frequently, apply for loans and have a strong relationship with your institution.
The most powerful of these models cross-reference these behavioral signals with ZIP+4-level third-party data, revealing granular demographic, financial and lifestyle traits at the micro-neighborhood level. Using advanced machine learning, they then build lookalike audiences—people who closely match your best members on key indicators.
This means you can now market not just to "anyone in the area," but to highly targeted profiles on targeted blocks—whether they’re tech-savvy Gen Zers, upwardly mobile professionals or retirees with stable financial behaviors.
Smarter Campaigns, Better Results
Instead of casting a wide (and expensive) net, these models help credit unions focus their outreach with surgical precision. That’s especially powerful for smaller and mid-sized institutions without the massive ad budgets of national banks.
And the results don’t stop at acquisition. These newly acquired members—selected based on deep data alignment—are far more likely to activate, engage and grow with your credit union. With the same predictive data feeding into your core marketing platforms (HubSpot, Digital Onboarding, direct mail vendors, etc.), credit unions can orchestrate personalized journeys from day one.
This approach positions AI not just as a targeting tool, but as the foundation for ongoing personalization, segmentation and engagement across the lifecycle.
Finding the Model That Works For Your Credit Union
Predictive analytics solutions like new member acquisition models can be a great asset for your credit union and can help increase overall profitability, but how do you implement one at your credit union?
One option is to build your own model, using available data and third-party Zip+4data. This option allows you to customize the model to ensure it meets your specific credit union’s unique needs. It can be cost and time prohibitive, so this may not be the best choice for every credit union.
Another option is to partner with an AI service provider. This option quickly allows for faster, more cost-effective implementation and allows your credit union to quickly start researching potential new members. When choosing a provider, ensure they understand the credit union mission and credit union needs, and that they utilize the data you need to take action.
Smarter Growth Starts Here
In an era of market saturation and rising competition, growth isn't just about adding numbers. It's about adding the right members: those who will grow with your institution and become long-term advocates. Predictive models make that possible.
