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.
To further focus your efforts, using a member lifetime value model can allow your credit union to identify and focus in on those members who will create long-term value for your credit union. Focusing on these members will increase the overall health of your credit union, allowing you to better meet the needs of other members.
Enhanced Decision-Making
Data should be driving your credit union’s decisions at every turn and using predictive models can help drive those decisions. A member risk score can help you identify those members at risk of defaulting on a loan or credit card, allowing you to implement preventative measures. Use a next best product model to determine what products and services members are most likely to respond positively to, allowing you to create targeted marketing campaigns. With models such as these, every decision your credit union makes will be targeted and more effective.
Proactive Member Engagement
Engaging members is always a top priority, and data provides a great opportunity to understand efficient ways to drive engagement. A next best action model can allow you to identify what actions members are likely to respond to, such as an email campaign or a mailer. Using a member retention model can allow you to identify those members most likely to leave the credit union, allowing you to target them for reengagement. Using these two models together can be especially effective at improving retention rates.
Creating a Targeted Approach with Predictive Analytics
Predictive analytics can help drive more informed decision making for your credit union. Each of the previously described models can aid in specific areas, but the true power of predictive analytics comes from taking a combined approach. By using all of the previously mentioned models in combination, you can achieve a more comprehensive view of your membership, which will enhance decision-making, resource allocation and ensure a personalized member experience, driving growth and satisfaction. To achieve unparalleled success with predictive analytics, reach out to Trellance today.
This article was written by Avery Swiontek, Product Manager, Predictive Analytics at Trellance.
