easily distributable to the right people in the organization. 2. Help us understand what the new digital customer looks like and wants, particularly as technology evolves from a batch-oriented process to real time. 3. Help us with people who know how to get started and how to get the whole organization on board with it.
Broad, narrow possibilities Lakeland Bank, based in Oak Ridge, N.J., jump-started its use of data analytics in a big way. “We use it on different levels,” says Carl Grau, senior vice-president, business intelligence and ebanking, in an interview. “We need to understand our customers. Primarily, we use a lot of it for that. We use it to identify growth opportunities. We use it to grow revenue. We’ll be using it a lot, more recently, to become more efficient.” As one example, Grau relates how the bank decided to open a new loan production office outside its traditional market area. Using a Fiserv business intelligence program, they did peer comparisons in various markets to see what would be a good fit. As a result, says Grau, they found opportunities a little south of their market in northern New Jersey as well as just across the border to the north in New York state. First Tennessee’s Losch relates another example of how data analytics produced a real, tangible result—this time in the realm of customer experience. “The way we use that particular data, for instance, would be understanding how many screens customers have to click through to finish a transaction. How much abandonment we had from customers . . . and how many ultimately opened a new account through the online process; how long it took. “We were able to figure out ways to streamline the application process of certain products that we had,” Losch continues; “clean up our back-end systems for our online banking products so that customers have to go through
fewer screens; and make it a much more seamless visual experience for them that develops fewer disconnects.”
Simplify the format Two keys to translating code-heav y reports coming out of IT: dashboards and exception reporting. “Dashboards give us an overview of our current status, and from there, we can look for anomalies,” says Grau. “We don’t have to waste time looking at the average branch. We have 53 branches. If a branch is producing a higher percentage of income [than others], or another branch is producing a lower percentage of new accounts, what are those branches doing right or wrong? How do we correct them, or how do we replicate them across the other branches?” Adds Losch: “If it’s not in a userfriendly form, people who don’t work with data all the time aren’t going to find it very valuable. We’ve built our sys-
“Data analytics can become a science fair project that doesn’t really get you anywhere. We’ve learned to take it in stages and think about our endgame” –W illiam Losch, First Tennessee tem such that it can be reportable and customizable at various levels of the organization. Then, we’ve overlaid that with . . . graphs and tables and charts, and put structure around bullet points.” It’s not all dashboards. Periodically, reports get issued from core systems about everything that has happened in a given day, quarter, or year. “What we’ve
done is taken those reports, scrubbed them down, and now all we’re providing is just the exceptions,” says Grau. “We’re making it a lot more efficient. Doing one page of exceptions a day, as opposed to perusing through 100 pages of reports. That’s one of the biggest boosts that we’ve gotten from analytics recently.” Data analytics doesn’t have to be confined to big picture, back-office uses. “We have a lot of reports going out to our relationship officers, loan officers, branch managers,” points out Grau. “We use a lot of the analy tics and push it down to our financial centers, absolutely,” says Losch. “You have to be careful not to overwhelm them,” he adds, “because there is so much data that we could share with the front line. They wouldn’t have enough time in the day to actually do what they are best at, which is interact with customers.”
Two lessons learned Stay focused, urges First Tennessee’s Losch. “As with anything, you can spend lots of money and time and effort and resources to build your data analytics, but it can become a science fair project that doesn’t really get you any where. We’ve learned that taking it in stages and thinking about our endgame, so that all the puzzle pieces fit together, is important—making sure that we’re getting returns on the projects that we’re spending time and money on as they occur.” Get support from the top, adds Lakeland’s Grau. “We have some higher-ups in the company who have acknowledged that data is important, that it’s good, but it does nothing if you just let it sit there. You need people, you need resources, you need tools to work the data. Once you get that buy-in, it’s a lot easier.” Don’t forget the human factor. “We think face-to-face, front-line interaction is the most critical piece we have as an organization,” says Losch. “We have a mantra here that says: ‘It’s the tool, not the rule.’ Don’t let [data] just drive how you serve a customer. Use it as part of your business judgment.” April/May 2016
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