The power of integrated predictive analytics for credit unions
In today’s financial landscape, credit unions must optimize operations while delivering personalized member experiences. It can sometimes feel like you have to sacrifice one for the other – that in order to create personalized experiences, you must sacrifice optimization, or vice versa. With the right technology, however, your credit union can achieve the best of both worlds, ensuring your members feel valued while also receiving efficient services.
Predictive analytics, models powered by machine learning and AI, can help identify areas for targeted messaging by using data to uncover information about your members. Let’s explore six different models – member segmentation, member retention, next best product, member risk score, member lifetime value, and next best action – and how they can help your credit union create individualized member experiences while maintaining efficiency.
Holistic member understanding
In order to meet member needs, your credit union first needs to understand what members are looking for and where they are in their own financial journeys. Utilizing a member segmentation model allows your credit union to divide members into different groups, based on their spending habits and needs. This method allows your credit union to focus specific marketing on the groups most likely to respond positively to the messaging.