Creating A Customized Banking Experience With Big Data

Customers have different expectations of their bank, and are willing to pay for different services. Big data opens the door for banks to group their customers according to their banking preferences, which can make customers more satisfied and more profitable.

Banks have been increasingly focused on customer experience in recent years, but they’ve been taking an approach that is too broad, says Dean Nicolacakis, a partner at PwC’s banking and capital markets practice. While many banks are trying to configure a customer experience that is consistent for every customer across every channel, the key to a really great customer experience is providing a different personalized experience that fits different customer segments, Nicolacakis contends. Different customers just want different things - and are willing to pay for different things - from their bank.

“People [in banking] have a blanketed approach to customer experience. Of course, you need a foundation based on good maintenance, not making mistakes in customers’ accounts, etc. But once you provide that minimal level you need to provide some nuance to the experience. You need to ask what differences there are in what customers want?” Nicolacakis explains.

The key to understanding what different customers want from their bank is big data, according to Nicolacakis. By collecting and analyzing all of the data that banks have available about their customers, they can then group those customers into different segments based on their expectations and banking needs. Nicolacakis helped author a report by PwC released in November of last year called Experience Radar 2013, which uses a research method by PwC to break down retail banking customers into different segments. But while many banks segment their customers purely according to demographics, the Experience Radar report groups them according to what they expect and value in terms of banking products and services. The report then explained different key areas that each segment is focused on to clue banks in to what they can offer each segment, and what each segment is willing to pay for.

A perfect example of banks needing to take a more segment-oriented approach to customer experience is in checking account pricing, Nicolacakis says. “Banks assume that everyone wants free checking or a monthly checking account. But there are a lot of people who want a la carte pricing, similar to airlines,” Nicolacakis explains. In researching the Experience Radar study, Nicolacakis found that about 25% of customers are interested in the a la carte model of paying for fees for additional services associated with an account, such as P2P payments. “These customers want a sense of control over what they’re spending at their bank. They don’t want to spend money if they don’t know why they’re spending it,” Nicolacakis says. The customers who favor the a la carte model are also heavy users of digital banking channels, and are highly interested in branch-less digital deposit accounts, he adds.

Another area where banks have taken a vanilla approach of offering everything to every customer at the same price is in mobile banking services, according to Nicolacakis. Another 25% of customers are very biased towards convenience, he explains, and would be willing to pay for extra mobile services that make their lives easier. “Most banks have given mobile banking away for free, but there’s a good portion of the population who’d be willing to pay for it,” he says. Nicolacakis gives the example of mobile bill payment as a service that adds convenience to customers’ lives but banks don’t charge for. And mobile bill payment can be expensive for the bank because it often has to write a a check for the payment.

Another area where big data can help banks handle different customer segments is in cutting costs by understanding channel usage, Nicolacakis add. There are a lot of customers who haven’t adopted the online and mobile channels, and still use the branch and call center to take care of their day-to-day banking needs. For instance, banks still have a sizable portion of customers who dial the call center to see if a check has been deposited or paid, Nicolacakis says. These customers are expensive to service, but because of regulations regarding fees, banks can’t make that much money from them. Many banks are already starting to record all of their customer interactions in the call center for compliance purposes, and by analyzing those call recordings they can identify the customers who are calling about their balances. The bank can then move those customers to online and mobile banking, and it becomes much cheaper to serve them, Nicolacakis explains.

For banks to realize these kinds of customer experience gains and cost savings from big data, data teams need to communicate better with the lines of business, Nicolacakis argues. Many people working on the business side of the bank aren’t knowledgeable about the analytics capabilities that data teams have now, he says. “The businesses just aren’t informed about what the current technology can do. They still think in terms of the old reporting paradigm. But now technology and analytics can use predictive modeling, and they can go and get, and process, data more easily,” he explains.

It’s up to the data team to reach out to the business side of the bank and demonstrate what they can offer for the business, Nicolacakis says. “The data side needs to do this. They need to show what can be done. They should go to the business side and help them understand that they can any questions that they’d like to have answered,” he relates.

This will require some initiative from the data team, but Nickolacakis contends that data teams need to be more experimental and creative. “They need to have a research and development mentality. Right now that simply doesn’t exist in most cases,” he reports. Unless the data team gets more creative and explains to the lines of business what they’re capable of, then the bank will never be able to fully take advantage of its data function.

One area where Nicolacakis thinks data teams could step in and do a lot of positive work for the business is in the mortgage signing process. The process from delivering the mortgage application to when the customer is actually on-boarded can take a significant amount of time, and a lot of documents are emailed back and forth between the customer, agent and bank. This leads to long drawn-out process that frustrates the customer and is inefficient for the bank’s staff. All of the emails that are sent back and forth are unstructured data, Nicolacakis points out, that can be analyzed by the data team. “They can pull those emails and figure out why those transactions are taking so long. They can look for things that are causing exceptions, or find out what the mortgage representatives are not doing well enough to speed the transaction along,” Nicolacakis says.

Jonathan Camhi has been an associate editor with Bank Systems & Technology since 2012. He previously worked as a freelance journalist in New York City covering politics, health and immigration, and has a master's degree from the City University of New York's Graduate School ... View Full Bio