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How to know your customer using data

With more customer data available than ever before, teams can finally identify critical trends quickly and make smart pivots for faster growth. Learn how to apply these strategies during COVID in this blog.

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How Data Integration Helps Credit Unions Win Against Digital Competitors

by: Chuck Schweiger, Senior Director, Analytics and Advisory Services at eCapital Advisors, an Incorta partner

Traditional financial institutions are facing intense competition from digital financial services providers. Credit unions are no exception. In the past, community was a big differentiator for credit unions. People became members through their job, locality, school, labor union, or other group affiliation. The other differentiator was their not-for-profit status. That allowed them to provide additional services and the best rates possible — usually lower than customers would find at a bank.

Online competitors are eroding those advantages. For example, you can put your money in a savings account at an online-only bank and get a better interest rate because their costs are so much lower. People are also finding that transacting online is more convenient, and over the past few years have become more confident about the security of online financial services.

That leaves credit unions looking for new ways to compete. I see three major opportunities for them to use all the data they have at their disposal:

 

Predictive analytics: Improving the ability to make predictions that help cut costs and generate new revenue is top of mind for many credit unions. Being able to identify predictors of fraud on a loan or credit card application is an important capability. So is the ability to predict and prevent defaults.

Predicting and preventing churn is also very important. Members leave for a hundred different reasons. There’s always been a desire to predict what products or services will keep them on board. Now it’s more of a requirement than a desire. But how do you know who is most likely to respond to a particular product or service offer? Combining data about age, family makeup, credit history, which products and services are being used, and how members respond to campaigns can improve predictions.

Creating a community feeling in the call center: Being part of a community is all about being known and valued. When a member calls for any reason, they should be connected with someone who recognizes them as a member. The person on the other end of the line should be able to see their history with the credit union, the services they use, and have their account balances at the ready.

They should also have all the information they need to be able to help with a given question or problem. They shouldn’t have to route members from person to person, but if they do have to transfer the call, members should not have to repeat the same information over and over again.

Speeding response to regulators: Complying with an ever-changing slate of regulatory requirements is an ongoing challenge. It’s not as exciting as customer-facing initiatives, so financial institutions tend to look at it as a necessary evil. That is to say, they don’t see it as something they can optimize, or turn into a competitive advantage.

There’s some truth to that. Every institution faces the same requirements, and changes to regulations and reporting requirements are outside of their control. But there is one aspect that credit unions can control, and that’s how efficiently they can pull together accurate data and reports. Pushing a button and getting all of the information you need to get to the regulators instantaneously saves tons of time and money.

 

A Case of Disparate Data

To have a 360-degree member view — and to be able to cut costs so you can invest more in marketing and customer service — is highly valuable.

One of the things holding credit unions back on initiatives like these is that they're inundated with a lot of disparate data about their membership. A member might have a checking account, a savings account, a mortgage, a credit card, and a personal loan. They might be frequent ATM users. They may do some of their transactions through an online portal. All of these touchpoints involve separate data stores.

Credit unions have to be able to bring all the data together and find the business use cases that will deliver insight and real value. The organizations that I see succeeding with this have a strong data science/machine learning program with the right tools and the right people.

It starts with visionary leadership and experience with data in the financial services industry. Business and technical staff need to have curious minds and be good collaborators because it takes multiple people to make this work. You need data scientists to build and test different models, people with data engineering skills and programming skills to support them, and business users that understand and utilize the data.

You might have data teams for multiple areas — marketing, credit cards, etc. I work with one credit union that has five data scientists spread across different functions. Within each functional area, you need a process for business leaders to come up with the questions and problems that can have a real impact on the business when addressed.

 

The Ability to Explore

The foundation for generating those questions is having all your data integrated and connected, and having the ability to bring in new data sources quickly for exploration. You can do that with Incorta.

The knock on these types of initiatives has always been that you spend 75% of your time mucking around with the data to get it ready and 25% actually building a model that can give you some real value. Historically, that has led to a lot of frustration and skepticism about the ROI of building a data science program.

With Incorta it's the opposite: About 25% of your time is spent on the data and 75% of your time is spent actually building models. That gives you the ability to dig deeper to find insights about your community that can help you restore the credit union advantage.

Redstone Federal Credit Union, for example, uses Incorta to empower its data science team to independently explore and organize complex data, build schemas, and create new dashboards – all without burdening IT.