Imagine being able to build stronger member relationships, uncover new business opportunities, and report your strongest numbers in the history of your organization – all without meeting members in person and during a pandemic-induced recession.
It’s not impossible. In fact, that’s exactly what we at Financial Center First Credit Union did using predictive analytics.
As one of the strongest credit unions in the country, Financial Center is well capitalized. When COVID-19 struck, we knew that some members would be facing financial challenges. So, we acted fast.
Proactively reaching out to members came as a directive from Financial Center’s CEO and President, Kevin Ryan, who strongly believed that the timing couldn’t have been better to guide members through financial uncertainty. After all, that’s what being a not-for-profit, member-owned credit union is all about. This belief led to the launch of Financial Center’s ‘We Care’ campaign.
Predictive Analytics to Build Trust
To launch the ‘We Care’ campaign, Financial Center built a new application that relies on analytics to identify members most in need of support – a loan, perhaps, or permission to skip a payment. The application built outbound call campaigns that allowed our staff to reach out to assure members that our Credit Union was there to help them through crisis and provide access to funds if needed.
Usually, by the time members contact us, they’ve already in a financial crisis, and that can be stressful. Instead of waiting for members to contact us, we reached out to them. By initiating the calls, we provided financial counseling, and addressed member needs – building higher levels of trust and stronger relationships.
Using predictive analytics, we were able to:
- Identify members most likely to need assistance and offer fee waivers, short-term loans or debt consolidation loans, competitive deposit rates, the option to skip a loan payment, and more.
- Provide members with the assurance that the Credit Union can provide access to money when and how they need it as well as financial products.
- Align employees with members they had previously engaged – providing a familiar voice with guidance on the other end of the phone.
- Create relevant and helpful talking points for employees calling members.
The success of the campaign went beyond calling random members, though. Before any employee contacted a member, they needed to know in advance the answer to the age-old question, “Is now a good time to call?”
Using the sophisticated analytics baked into our InterSystems data platform, our staff knew who to call, when to call, what to say, and how to say it. This intelligence improved the process of generating accurate call lists and scripts designed to give members peace of mind, as well as improved the conversion results of our calling efforts.
Taking it a step further, once a call list was built, it ran through an analytics funnel that focused on the following four key areas.
- Centers of influence: This includes insurance brokers, who work with employer groups, who in turn become credit union members. Identifying the centers of influence first helped whittle down the list and ensures that the right Credit Union team member was reaching out to the appropriate organization.
- Segmentation: The two segmentation types that were used are based on demographics and the type of relationship the member had with the Credit Union.
- Behavioral patterns: Using analytics to identify behavioral patterns across branch transactions, online lending, the call center, and other critical touch points have always been key to knowing when to contact a member. Prior to COVID-19, we tracked four behavioral patterns; we increased it to 34 during COVID-19 to gain deeper insight.
- Loyalty: The Credit Union has created a loyalty matrix score based on member type and total aggregate relationships (number of accounts or financial products, levels of activity, and length of membership).
Based on these factors, Financial Center can accurately predict the members who are most likely to accept an offer.
Investing in Members Pays Off
We measured our success based on how many members we were able to assist. In turn, this had a significant impact on our community. As a purpose-driven credit union, we believe that helping one member improve their financial life impacts their family, which ripples out to the community as a whole. And, when communities are improved, we make an impact on our world. By leading with purpose over profit, the numbers take care of themselves.
Speaking of numbers, before COVID-19, the average number of calls we made per month were less than 2,000. Today, we’re making 9,000 calls a month. Using predictive analytics, we’ve produced three of our top lending months in our 67-year history. I call that success, no matter what’s happening in the world.
Every week, we generate a dashboard showing how many thousands of members’ lives we’ve positively influenced. The metrics include number of members contacted and reached, products and services used, transactions, and more. The dashboard is a reminder to the team that every day, they’re making a difference in members’ lives. Rapid innovation drove this, and a commitment to caring will continue to help us through this unprecedented period of uncertainty.
About the Author
Cameron Minges is the current Executive Vice President and the incoming President of Financial Center First Credit Union – a $730 million headquartered in Indianapolis, Indiana. He has worked in the credit union industry for nearly 30 years, serving as Chief Information Officer for three of Indiana's top credit unions. Minges is the key architect behind Financial Center’s Predictive Analytics Program, which has helped the organization achieve record loan production results and has been widely recognized in national publications. He holds certifications in software development, database management, network design, and data analytics. Additionally, Minges attended the University of Phoenix majoring in Business Administration and is currently pursuing the CUES CEO Management Institute Designation.