Ron Smith’s thirst for knowledge feeds his love for data analytics. Smith is chief growth officer at $1.6 billion asset Texas Trust Credit Union in Mansfield, Texas, where he oversees the institution’s growth strategy in a role that brings a centralized focus on increasing membership through data intelligence.
Ron Smith:I have a natural love for data and a thirst for knowledge. I’ve always been the “why” person. Why did somebody come into this branch? Where did they come from? I want to look beyond the fact that we opened five new accounts to find out why and discover how we can duplicate that effort to be more successful.
A: One of the biggest problems all credit unions have is expanding their relationships with existing members. Through our data analysis we found that after 60 days of joining the credit union, 90% of our members aren’t likely to expand their relationship. If these members don’t have a checking account with us in the first 60 days, we’ve missed an opportunity.
We’re making changes so we can capture these types of member engagements early in the relationship. We’re also focusing on analytics for nonmembers. We’re in the Dallas/Fort Worth area so it isn’t cost effective to market to every community. We’re analyzing data to identify people strategically and build targeted acquisition campaigns. In the past, we would send out mass quantities of mailers and coupons and blanket the market.
Credit scores are another data component we are evaluating to identify individuals for targeted campaigns. But there’s a line between targeting too much and not targeting enough. Long-term, you must find that line and constantly monitor and adjust it as it moves. We’re looking for the sweet spot.
A: For us it has been more of a human capital investment. Until 18 months ago, we didn’t have a data analytics team. We had one person in IT who loves data. With the creation of the growth team in 2020, we now have four data people: a data analyst, manager of data, data business analyst, and a senior vice president of data who report to me.
At the same time, you have to learn how to trust the data. You may think you know your members and then you pull the data and it shows something completely different. We thought most of our members were “average Joes,” people earning $40,000 to $50,000 a year. But the data showed that more than 40% of members earn in the six figures. Because we didn’t recognize our members, we had been developing products for the wrong group of people.
A: Pulling data for credit unions is tricky because of system limitations and because the data is often housed in two or three different systems. The key is doing multiple analyses. If you have one person doing this, you’re not getting an analysis so much as one person’s opinion. You need a team of people to deliver a true analysis.
A: Know who your members are and develop the right services and products to fit them. Many members don’t use credit unions as their primary financial institution. They may have several banking relationships. If you look only at their activity with you, you may not understand their full financial picture.
When we see that we’re just one of many financial institutions our members use, we can determine what those other institutions are doing differently to capture business. Then we can develop our products and services so these members use us more.