For years, analysts and other industry observers have criticized banks for concentrating more on acquiring customers than on retaining those they already have. They usually cite the fact that although banks have truckloads of data on their clients, they just don't seem to know what to do with all that information.
Celent's Grealish says that the technology to enable banks to look in-depth at their customers has always been there. What they lack, she adds, is the ability to use that data. "Banks' organizational capabilities to harness that technology isn't there," Grealish asserts. "The ability to structure a credible ROI and get a piece of the pie that they seek is missing. They have quite a bit of data, but it's hard for them to harness it."
Grealish notes that most of the leading retail banks use analytics to some degree. However, "Where predictive analytics gets challenging is around the infrastructure required to first formulate what they want to predict and the ability to let them act on that," she says.
One notable exception is in the credit card area. Grealish and other experts point out that the use of predictive analytics in this space is actually quite mature.
According to Laurent Desmangles, a principal with New York-based Oliver Wyman, predictive analytics have worked best in the card industry to identify customers who are at risk for leaving. "The credit card is a transactional product with a good data trail," Desmangles explains. "Other bank products don't quite have the same kind of data trail as cards. Tools for identifying 'churners' and for launching retention tactics have had a mixed record and haven't traveled well out of the consumer finance realm."
The road to employing predictive analytics to their fullest capabilities likely will be long and bumpy. But banks understand that they must start somewhere. According to Michael Nicastro, chief marketing officer with Glastonbury, Conn.-based core systems provider Open Solutions, a good place for banks to begin is in changing how they operate.
"Banks have to think differently to use predictive analytics," he says. "They have to stop marginalizing and commoditizing their core systems. Legacy systems are great transaction collection systems, but they don't tell you anything about the data -- it's unfiltered data. The fundamental problem is the base information about your clients."
The basics need to be hammered out first, agrees Dinesh Sheth, CEO of uMonitor, a Memphis-based provider of solutions for customer acquisition and retention. "Are banks ready to use predictive analytics?" Sheth poses. "If they still have trouble with their basic systems -- such as running an inefficient system that makes new-account opening take an hour to perform -- it doesn't make sense to use predictive analytics yet. They need to get the basics right and then look at how they can improve customer retention."
Open Solutions' Nicastro, however, sees the movement by many banks, especially on the international front, to implement new core systems as an impetus to employing more analytics. "As we see a global shift to implementing new core systems, analytics will become more of a reality," he explains. "Retention is about knowing how to play against your biggest competitors. Our bank clients are competing with PayPal now. PayPal analyzes every transaction in real time, and they know exactly what to say the next time they deal with a customer. Their core information is good. Getting this information is the key before a bank can even start to use the analytics."
Still, many banks are taking the first steps to better anticipate their customers' needs and intercept any churn before it happens. Although most aren't using analytics throughout the entire organization, they are taking a measured approach so that they can see what works and what needs improvement.