When you talk to Jeffery Lee, it’s hard not to hear how excited the chief marketing officer of Seacoast National Bank is about the power of big data. The same goes for Robert Stillwell, the head of analytics at the $4.7 billion asset bank based in Stuart, Florida. With a small handful of colleagues in Seacoast’s marketing department, Lee and Stillwell have combined data analytics and marketing automation software to gain insights into their customers and run dozens of targeted marketing campaigns that, in some cases, generate returns on investment in excess of 100 percent.
Seacoast proves that a bank doesn’t have to be big to benefit from big data. In Lee’s estimation, in fact, just the opposite is true. “Our size is an advantage because our data isn’t trapped in a bunch of different silos,” says Lee. “We have one core banking provider, everything flows through it, and the data is readily available.” This eliminates the technical challenge of wrangling data from disparate sources. It also means that Seacoast “doesn’t have to fight battles about who owns which data,” explains Lee.
The chief information officer of Memphis, Tennessee-based First Horizon National Corp., the holding company for First Tennessee Bank, says the same thing. “This may be an advantage of smaller institutions,” says Bruce Livesay. “Because our environment is less complex, we can use a single tool across all of our channels. And it’s easier for us to do that than the big guys,” he continues, referring to the $29 billion asset bank’s data-driven marketing platform.
Of 13 global and regional banks surveyed by consulting firm McKinsey & Co. recently, almost every one listed advanced analytics among its top five priorities, with many investing heavily in it already. Yet, the expected results haven’t materialized. The problem is that these “efforts remain unconnected and subscale; they have not yet tied together their disparate efforts into a single, unified business discipline.”
Costs no longer serve as an impediment either, even for banks without hundreds of billions of dollars in assets. “The cost of software is unbelievably attainable. I had no idea it had dropped that much,” says Lee, who worked for a major credit card company before joining Seacoast in 2013. This includes the bank’s marketing automation platform as well as its analytics software.
“Because the [cost of] technology required to gather and store relevant data has gone down, it is no longer cost prohibitive for small and midsized financial institutions to get into big data analytics,” says David Macdonald, vice president of financial services at SAS, a leading company in business analytics software and services. Livesay agrees. While he wouldn’t disclose how much First Tennessee’s data-driven marketing automation platform from IBM costs, he made it clear that it “more than pays for itself.”
A direct mail campaign conducted by Seacoast over the past year offers a case in point. By analyzing branch visits, the bank identified customers who frequented branches to deposit checks instead of using mobile deposit. To encourage these customers to switch, Seacoast sent checks for nominal amounts to them with instructions on mobile deposit. Seven percent responded by permanently changing their behavior. That’s seven times the conversion rate one would ordinarily expect from a campaign that isn’t informed by advanced analytics, says Lee.
But if scale and cost aren’t impediments, what’s keeping smaller banks from taking better advantage of their data?
The answer is: Talent. Seacoast hired Stillwell, who had been using data analytics software for 15 years when he joined the bank in 2014. Like Lee, Stillwell came from the credit card industry, which has a reputation of being especially effective purveyors of data. Stillwell started on a shoe-string budget, with analytics software from SAS that ran on a personal computer. This worked as a proof of concept, giving Stillwell the tools and programming language needed to analyze large amounts of data without requiring a substantial investment. Seacoast then upgraded to a more expensive server-run version a year later.
Stillwell has since gone on to use the software to develop a customer lifetime value model that estimates per-customer profitability—it also specifies why a customer is or isn’t profitable. He built an opportunity-sizing engine, too, which identifies the next best product to sell a customer based on the product’s profitability and the customer’s current product portfolio. The bank is now combining these tools with its marketing automation platform to complete and automate the circle between insights and execution.
Once this happens, Lee believes Seacoast will be able to scale up its highly targeted marketing campaigns from the 40 or so it runs right now to more than 10 times that amount. “Most banks our size are doing one marketing campaign a quarter; we’re doing 40 at all times,” says Lee. “And we could be running 400 campaigns.”
Seacoast’s marketing department is now rolling these tools and insights out to a broader audience at the bank. Stillwell built a user interface atop the analytics platform to enable remote access, and the bank has begun educating frontline employees about how the insights from the data can help serve customers more effectively. It does so by offering insight into the products and services each of Seacoast’s customers would benefit most from, as well as the best channels over which to engage them, explains Lee.
These efforts have been well received, though they have at times run up against long-held assumptions. This is especially true in the context of customer profitability. “You ask someone in the branch who their best customer is, and they say it’s the person who comes in every day,” says Lee. But because it costs a bank more to service these customers compared to those who bank remotely, Seacoast’s profitability model comes to the opposite conclusion. “It’s a big cultural change because there are perceptions that don’t always align with what the data tells you,” says Lee.
First Tennessee saw a similar cultural change after implementing its own customer profitability model. “There’s absolutely no doubt that it’s changed the culture of the company. Most banks can’t give these kinds of insights to their bankers,” says Livesay. “The fact that we can has changed the behavior of our sales people. It prioritizes which customers they should be talking to and informs those conversations.”
At the end of the day, there’s no question that big banks derive many benefits from their massive balance sheets, but there are some areas where scale can be a disadvantage. The timely implementation of a fruitful data analytics program may be one of them.
Source: http://www.bankdirector.com/index.php/magazine/archives/fintech-issue/small-banks-using-big-data
