All the best data analytics technology in the world isn't helpful if bankers don't know how to use it.
Analytics tools that mine through data have become essential as banks look to give customers personalized, relevant communications and offers. But simply buying technology solutions isn't enough; banks need to have the right people and processes in place and know what they want the data to do for them, some experts say.
"Banks of all sizes will need to use predictive analytics going forward," said Paul Schaus, president and chief executive of the consulting firm CCG Catalyst. "But there's so much data they have access to, though, so they first need to define what data they are looking for and why. Without a clear data strategy, you can get overwhelmed by the sheer amount of it. It's like trying to take a sip of water from a fire hose."
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This means banks need to have a definite plan for what they want the data to do for them, such as trying to assist growth in a particular market or sell more of a particular financial product. Further, Schaus said, banks will need to employ more data scientists to help them use data more effectively.
Most large banks are already doing this, but it is something smaller banks will need to do, too. For banks that don't have the budget for full-time data scientists, employing consultants or short-term analysts on a per-project basis will be necessary, Schaus said.
The good news is that finding data scientists is becoming easier. Data science is an increasing focus of colleges and university course offerings, said Ambreesh Khanna, group vice president and general manager of Oracle Financial Services' Analytical Applications group.
"All universities are offering courses in data science now," he said. "Lots of people are showing up in the market with data science degrees, which means the price of hiring a data scientist is dropping. It was very expensive five or six years ago; not so much now."
Like Schaus, Khanna said banks must have a strategy around data, in particular to deepen the relationships with existing customers.
"In mature markets like the U.S., [financial] products are largely commoditized and there aren't many unbanked," Khanna said. "So the focus on banks needs to be more and more on retention. That means appropriately identifying customers that are profitable and who might become profitable based on the data."
The banks that are successful in the future will be those that are able to create lifelong customer relationships that are advisory based, said Emmett Cox, senior vice president of customer intelligence at BBVA Compass.
"There's a huge, tremendous push from consumers to have relationship with their banks today," he said. "They want their banks to help them with the future and for different life stages plan."
Cox comes to banking from the retail industry. He was previously a senior group manager of consumer insights and analytics at Walmart. Cox said banks have an advantage over retailers because consumers have a "deeper relationship" with their banks than any individual retailer.
At Compass, which largely uses analytics tools from SAS, Cox said the bank triess to use data to offer helpful advisory services to customers rather than simply sending out product offers.
"Consumers are constantly hit with a barrage of marketing messages all day," he said. "What we try and strive for is different: to offer help based on their life and financial situations. So things like asking if they are thinking of planning for retirement, or if they are prepared for certain household changes."
Ultimately, data analytics tools combined with in-house expertise will enable banks to offer a much more personalized relationship with customers, Cox said.
"It's not just about a technology, but understanding the information behind the technology and analyzing it," he said. "Today, consumers are fairly specific in what they want, so we're not simply trying to find a single 'aha' moment, but helping them throughout the relationship."
