Member Attrition, and How Analytics Can Help Avoid It
November 20, 2017
It goes without saying that credit unions want to keep their members, and keep them happy. As credit unions look to enhance offerings, ensure superior member service and stay competitive, alleviating member attrition is at the top of the priority list.
It is well documented that retaining existing members takes less time and money than attracting new ones, so it’s a no-brainer from a business perspective to focus on retention. In addition, there are significant insights to be gained from having a better understanding of why the members who leave choose to do so.
Predictive analytics has power on both sides of the attrition equation. It can serve as the foundation to help achieve a higher level of member satisfaction: knowing what members want and when they want it helps determine the best way to deliver on the promise of great service and offers members the efficiency they demand. On the flip side, accessing and analyzing key data points about members who leave can help provide invaluable information to help credit unions address and alleviate the most common causes of attrition, both in general and more specifically.
Taking a look at both of these perspectives can help bring the power of predictive analytics into clearer focus as it pertains to reducing member churn:
- Elevating services and offerings for current members. Predictive analytics allows credit unions to leverage key pieces of data to improve services. If data shows, for example, that the average transaction time to execute a mobile deposit has increased, it might compel a re-evaluation of the process and the technology to streamline and improve efficiency for members who use it. Data can also be used to flag loans at risk for default, which can pave the way for preemptive outreach and prioritization of collections efforts. Any case in which the data can inform better decision making is a win/win for credit union leadership and the members served.
- Better understanding member attrition. Looking closely at data points about the number of transactions, frequency of transactions and recency of transactions and correlating them to factors such as age and other social and demographic information is part of a larger puzzle. The puzzle pieces, together, help credit unions build attrition risk profiles. Attrition risk profiles can help credit unions identify specific members, or groups of members, who are likely to leave and can serve as the catalyst in creating specific initiatives or campaigns to help reduce attrition.
In today’s world, predictive analytics and the rise of mobile banking go hand-in-hand. The digitization of credit union services allows for many more data collection opportunities, which helps improve member services. And, when members do leave, credit unions have a hard set of data points that help explain why. With this information in hand, credit unions can create a positive upward spiral in which members are more satisfied, churn is minimized and better business decisions help keep the forward momentum going strong.
Read our whitepaper, Predictive Analytics for Credit Unions, and learn more about how credit unions can harness the power of predictive analytics to drive member engagement and transform their business.