Like an overwhelming number of banks, Wells Fargo wants to treat its customers individually. It wants to know what products and services they want based on their life and financial situations. And in making a clean break from its past, it doesn’t want to suggest products its customers don’t need or already have—or have turned down a dozen times.
But knowing what they want—or not—takes data. Lots of it. Data organized and analyzed in a meaningful way. After all, the numbers won’t lie and customers won’t lie in wait to seize opportunities.
“To meet rising customer expectations and compete with fintech startups and other digital-first companies, we must leverage the full breadth and depth of our data,” says Zac Maufe, Wells Fargo’s head of data management and insights.
For starters, that data needs to paint a clear picture of customers: “It’s all about engaging with them in a personalized way that reflects their full relationship and holistic financial needs,” Maufe adds.
But giving bank executives data-driven insights to predict consumer needs and desires hasn’t been easy. Like most banks, Wells certainly has always held copious customer data—on more than 70 million customers, to be precise—but it’s often spread among multiple bank units and storage systems and thus hard to find and share.
“Wells Fargo’s legacy data management environment, like most legacy companies, was structured around the lines of business with product groups each building what they needed,” Maufe says. This obscured what he calls “a horizontal customer view.”
Thus executives in the bank’s credit card operations, for example, didn’t always know about the mortgage and investment experiences of their clients—while in the reverse direction, the credit card folks were likewise in the dark.
Getting a grip on ‘gobblygoop’
Then in 2017, Wells formed a centralized Data Management and & Insights team to develop an overall data strategy and to develp the architecture to support advanced analytic capabilities. Not easy work, this: The insights team began with what they called “gobblygoop.”
But in getting the data up to snuff, the bank is taking an enterprise approach by building one data environment, implementing common data language bank-wide and implementing a data “lake,” Maufe explains.
As for what’s swimming in that lake, “It will allow us to store a huge amount of data in a scalable, cost-effective way,” he says. “We’ll have the tools to provide customer views and we’re investing in new technology platform for data analysis.”
Advanced technology, including machine learning and other artificial technology, will certainly bolster that effort, especially on the analysis side. But before calling in the fancy analysis tools, the data needs consolidation. Unorganized and inaccessible, the data can’t help executives and employees who need it to service customers.
And the data has to be complete.
Information rich—execution poor?
Wells’ experience is not unusual, according to Chuck Schaeffer, CEO of Vantive Media, which provides software for business marketing. “A high majority of bank executives agree that they are information rich and execution poor,” he says. “Data is costly to maintain and there can be a lot of missed opportunities if not utilized properly.”
And those opportunities are obvious, Schaeffer says. With the right information about customers, banks know their interaction history and can analyze past transactions to determine why they take certain actions—down to why certain types of customers or branches reflect different results and follow patterns. Then, they can predict behaviors and even prescribe offerings based on customer profiles, segment and prior actions.
Tiffani Montez, a senior analyst of Aite Group, explains that banks have had some limited success with using data analysis for marketing and customer services in the past but have lacked a comprehensive strategy to bring data together. Says Montez: “There has been a little progress here and there and some efforts at personalization for years. But banks haven’t done a good job delivering a comprehensive program.”
Part of moving forward revolves around revising the old cross sell model for a data-driven age. Banks that fully utilize the data can assist customers with their financial wellness, a competitive coup.
“Customers are struggling to manage their finances,” Montez says. “But bank products have become commoditized and banks compete on price. They need to compete on better customer experience and personalization.”
After all, a consumer might not want a credit card just yet—but they often want help getting their money matters organized. Which, interestingly enough, goes back to banks getting their data matters organized.
Signs of improvement and imperatives
But in analyzing data, time is of the essence as the digits never stand still. “The value of data depreciates at about 2 percent per month so a real-time data management strategy is essential,” Schaeffer points out.
“Customers who bank on mobile devices or by chat need real-time guidance that requires personalized experience,” Montez adds. And the number and types of customers who use advanced technology to bank is growing like never before (even as you read this executive report).
Schaeffer believes the overall situation for banks is improving. Rather than wrestle big, expensive systems to gather data from multiple legacy systems, many now turn to systems that consolidate it all in the cloud.
“The use of cloud-based systems has made it much easier for banks to gather and access customer data from throughout the institution,” he says. That means good news for banks big and small, as affordable data processing becomes ubiquitous.
What next, then? Another technology we’ll see gather momentum in 2019 is artificial intelligence, which helps get data ready for banking prime time. “Banks’ ability to use AI can go a long way to personalize service and enhance customer experiences,” Montez says.
But once again, banks need to move fast as their customers are in many cases moving faster with the smart use of technology. In fact, a majority of millennials will leave a financial institution for a better app, while customers list customized solutions as their top priority, BAI Banking Outlook research shows.
The data is there. The question is, where are you?
Lauri Giesen has spent more than 25 years writing about banking technology and payments for numerous business and financial publications. In the 1990s, she founded and edited Financial Service Online, a magazine covering Internet-based forays into banking and investment services.
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