2SHARESShareShareSharePrintMailGooglePinterestDiggRedditStumbleuponDeliciousBufferTumblr,Alyssa Nolte Alyssa Nolte is Chief Development Officer for Discida (de-see-da), a national research science firm that serves a wide range of organizations, including credit unions. Discida data scientists developed the Brand … Web: www.discida.com Details It’s been well-documented that acquiring new members is far more costly than growing relationships with the ones you already have. It’s just one reason that building member loyalty is a priority for most credit unions, and one of the key metrics measured in a strong, healthy brand. Happy, loyal members can be a credit union’s greatest asset. Not just for their lifetime value, but for the new members they may recruit with word-of-mouth, positive online reviews and social shares. If you have a segment of active and engaged members, pat yourself on the back. What you’re doing for those folks is working well. But what about your not-so-engaged members? They’re quieter and easier to forget about. Before a member leaves, there’s rarely an announcement or singular tell-tale sign. Rather, there’s a subtle pattern of behaviors – and non-behaviors – that insinuates an impending departure. Because there are many potential behaviors and variables relative to member attrition, it can be difficult to identify members in danger. Unless you have the right data science tools, that is. Artificial intelligence solutions, such as neural networks, are providing credit union leaders with new windows of insight into members with the greatest likelihood of disengaging from their products and services. Rather than accept attrition as an inevitable part of business, credit unions are now working to become proactive and sharply-focused in their retention efforts. Here’s an example…. Let’s say a credit union was losing market share. Throwing up their hands in despair isn’t an option for leadership. Neither is be impulsively rolling out marketing offers. Wouldn’t it be great to respond with laser-focused efforts right where they’ll have the most impact? With the assistance of a tool like an AI neural network, a credit union can deeply analyze member data to get those answers. The network’s ability to synthesize large amounts of multi-dimensional data can reveal the behavioral patterns and factors that precede a member leaving. That, in turn, allows a credit union to segment its member base and deploy hyper-personalized promotions that get the right offer to the right member at the right time. AI and data science enable credit unions to become both proactive and effective, reaching waning members with re-engagement campaigns before they’re gone for good. Credit unions don’t need a crystal ball to know which members will leave. In fact, they have the answers right now. They’re just buried deep within their data, waiting for the right tools to uncover them.