Email marketing continues to be one of the most effective ways for banks and credit unions to connect with customers and grow relationships. Today’s successful campaigns are built on customer data, automation, and personalization, not one size fits all messaging. In this blog, you’ll discover five email marketing best practices that can help your institution improve engagement, increase conversions, and deliver a better customer experience.
Category Archives: Banking Insight
Community banks and credit unions have more customer data than ever before, but disconnected systems often prevent them from turning that data into meaningful business insights. In this article, we explore the seven biggest data challenges financial institutions face in 2026, from fragmented customer information and poor data quality to reporting delays and AI readiness. Learn how a modern Customer Data Platform can unify your data, improve decision making, automate marketing, and help your institution strengthen relationships, increase growth, and stay competitive in an increasingly digital banking environment.
AI helps marketers turn relationship data into sharper audiences, better plans, and faster action. Read how.
Financial institutions aren’t struggling with a lack of data—they’re struggling to use it fast enough to matter.
Customer data is everywhere: core systems, loan platforms, digital banking, payments, CRM tools. But instead of creating clarity, it creates friction. Teams spend hours pulling reports, reconciling numbers, and waiting on analysts. By the time insights surface, the moment to act has already passed.
This isn’t just inefficient—it’s a competitive risk.
The institutions pulling ahead today aren’t the ones with the most data. They’re the ones that can turn data into decisions instantly.
For decades, Marketing Customer Information Files (MCIFs) were the default way for banks and credit unions to centralize customer data. They promised
better targeting, sharper marketing, and stronger cross-sell.
But in 2025, that era is ending.
The cost of bad data is staggering. Industry research shows that the global banking sector loses more than $400 billion each year due to dirty data. For individual institutions, that translates into 15 to 25 percent of annual revenue.
