You have probably heard the following statistic – it costs up to twenty-five times more for an organization to acquire a new customer than to keep an existing one. 1
Churn. Turnover. Attrition. No matter how you phrase it, losing members is an issue most credit unions struggle with on a regular basis. Attrition can be an indication of several factors, including member dissatisfaction, pricing issues, poor marketing, or just the natural part of the member life cycle.
The key to successfully keeping your credit union’s attrition rate to a minimum lies in the ability to uncover the root cause of the problem and by putting your member data to work.
By analyzing member data, credit unions are gaining better insights into their member’ needs, preferences and likely behaviors that trigger attrition. This valuable information enables the development of member loyalty strategies that strengthen the credit union’s ability to retain the members that are costly to attain.
Below are a few steps in the process and how data analytics can help combat future churn.
Get to the Root Cause:
Understanding the reasons for attrition is a crucial first step in identifying any leaks in the member engagement process. With this knowledge, you can begin to close any gaps and know the triggers that lead to members leaving. Identify patterns in any root causes of attrition through simple surveys, phone calls and other member exit processes. Using the data from these events provides insight as to why a member leaves. You can then build data-driven predictive models for attrition and plan for appropriate remedial actions and retention strategies for members that are considered a “flight risk.”
Calculate the Loss:
Once patterns of churn are addressed, your credit union needs to leverage the underlying data to calculate and forecast metrics such as attrition rates and lifetime value of members. The data will provide key insights into the cost of losing members and the percentage of members whose business is lost per month or per year.
Identify Immediate Needs:
Analytics also has the potential to identify the needs of new and existing members, so the credit union can effectively match the best products for them. For example, the credit union can use call center contact information, new member questionnaires and other behavioral attributes to predict what type of products would be the best fit for a new and existing member. By matching members with the right products and services, the member is less likely to have complaints or start looking for better services elsewhere.
Know Member Segments:
Deploying member segmentation techniques has countless business benefits—especially in controlling attrition. Segmenting your members by dividing a member base into geographic, demographic, behavioral and other categories will help your organization target the right products and services and help reduce member churn. Using data analytics, you can successfully segment your members data, and invest more resources into those members who are most likely to respond to your product offerings. You can then further refine your messaging and product/service offering to retain their loyalty.
Today’s consumer-savvy member wants to feel important and understood. If the credit union is not able to customize offers based on the member’s needs, chances are they will look elsewhere for financial services. Data analytics can empower the credit union to access extremely granular and detailed information on each member and allow marketers to send different offers to each member segment based on their interests and life events. Emphasizing this personalized approach to marketing will allow members to feel a more personal connection with your FI even in an ever-increasing digital environment.