Yet the amount of publicly available data about each and every one of us grows all the time, to the point where an organization with the will, the patience and the right software can analyze in detail our financial transactions, our habits, our political leanings, our preferences and our geographic location, among other things.
Banks have been analyzing their customer data for decades, most thoroughly in the credit card business, searching for signs of fraud, willingness to upgrade to a new product or propensity to leave for a competitor. Today, many banks have projects under way to pull together customer data from all channels - branch, ATM, online banking, mobile banking, call center, social media sites - in one place, to mine that data in real time and use it to cross-sell, up-sell, detect fraud and keep customers in the right products. There are six trends guiding such projects.
1. The Big Data myth. The trendy phrase "Big Data" refers to data sets that have grown so large and complex that they become awkward to work with using standard database management tools.
Data volumes undoubtedly increase all the time. IBM estimates 2.5 quintillion bytes of data are created every day from a variety of sources including sensors, social media, and mobile devices around the world. IDC estimates the market for "big data" technology and services will grow at an annual rate of nearly 40 percent to reach $16.9 billion by 2015.
One bank customer recently described banks' data challenge to Boxley Llewellyn, global retail banking director at IBM, as "being in a big room full of data that's a little dark, so sometimes data gets trapped in a corner and sometimes it can't be found quickly enough. A wind of streaming data, social data and unstructured data is knocking at the door, and we're starting to let it in. It's a scary place at the moment."
But the idea that businesses need to store, mine and analyze every scrap of the customer data they collect is not practical.
"A lot of times when analytics and engineering people ask the business people what data they want, they get this answer back: collect everything and we'll sort it out on the back end," says Joseph Stanhope, senior analyst at Forrester Research. "That's not a data management strategy. There is too much data from too many sources coming at us too quickly for us to just save everything forever. You do need to be discerning about what data the business uses, which data goes to a KPI that shows us if we're moving the business forward. If people can't articulate what they need up front, they're not going to pick it up on the back end."
Gaming companies, for instance, don't mindlessly store all the data they collect on gamers, he observes. They curate the data to understand what is useful and what isn't, and create data hierarchies, schemas and categories to manage, condense, add and change information. "To understand this is more than technology, it's about people in the organization and the culture," Stanhope says. "If you can't evolve and change what you curate, then you do have to store and collect everything and business passes you by."
Edgar Enciso, executive vice president and director of customer intelligence at BBVA Compass, concurs. "We have a lot of noise around Big Data," Enciso observes. "The first challenge is to clean that information and define what data and analytic makes sense. We have information for everything and for everyone. However, when you try to be hands-on with the data, we have to clean it up and put it in a meaningful way so we can make the right decisions."
2. The use of predictive models to make better offers to customers." Once banks get that full picture of customers, they interrogate all the data they have and build predictive models," says David Wallace, global industry marketing manager, financial services at SAS. "They match the predicted behavior with campaigns for new or enhanced products, cross sell and up sell. They identify customers at risk of attrition and put programs in place to try and save the customer relationship because it's less expensive to keep customers than to get more. Predictive modeling is at the heart of all those activities."
BBVA is a case in point. It has three main goals for its customer analytics efforts, much of which are carried out in SAS Enterprise Miner analytics software, according to Enciso.
First, the bank is trying to make the right decisions to target the right offers to the right customers, through customer segmentation. The bank segments customers into the categories of wealth management, commercial banking, retail consumer and small business. It also performs lifecycle segmentation, grouping customers according to life stages, such as singles, independent professionals, young families and retirees.
The second goal is to understand customer profitability. "On the customer side and on our side, we want to have the right rates and pricing," Enciso says. "When we find customers who are not profitable, we try to find a way to serve them better, to keep them but put them in the right products."
The third objective is to analyze customers' life events and predict their future needs. "We're trying to see what are the customers likely to buy, what's their next problem?" Enciso says. The bank analyzes patterns in transactions and balance levels. "When we see that our customers are lowering their business with us, we're trying to find a way to keep the business," he says. If, on the other hand, a customer is increasing his balances, the bank tries to move that person to a higher segment with a better service level.