Grossman's team mined customer demographics and account data including transaction activity and account balances along several product lines. From that analysis, they found customers who were likely to invest in mutual funds, and they used that information to help Fleet's Investment Services division target prospective clients. So when a customer calls, a rep has a much better chance of cross-selling if he knows what accounts the customer holds and whether the customer is part of a group with a propensity to buy a particular product. At Bank of America, customer service representatives equipped with customer profiles gleaned from data mining pitch new products and services that are the most relevant to callers. For example, a customer in a certain age group who has children and a home equity loan with the bank is a good candidate for taking out a student loan.
Data mining helps the bank identify such customers.
The Toronto-based bank calculates current profitability profiles of households and produces models to predict the profitability of customers over a lifetime. Bank of Montreal, for example, analyzes mortgage customers' transactions in checking, savings and other accounts for insight into who is at risk of defaulting. The bank was surprised to find that some customers who consistently made their mortgage payments late were not necessarily at a high risk of defaulting. The bank found that a certain type of customer is in the habit of paying bills late but has the wherewithal to fulfill his or her obligations, says Jan Mrazek, chief specialist of the data mining group at Bank of Montreal. By analyzing the transactional behavior of customers across all their accounts, the bank can see which customers experience periodic cash flow crunches and which may truly be in danger of defaulting.