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BankFinancial, a $1.6 billion financial institution, is no stranger to predictive marketing. It has been reaping the benefits of it for some seven years. Recently, however, BankFinancial executives wanted to be more proactive with efforts to retain the approximately 1.5 percent of BankFinancial customers who left the bank each year, but felt limited in their ability to mix data sources and segment effectively.
They enlisted SPSS’s recently released PredictiveMarketing software, which enables the integration of data sources, such as records at the transactional level, account level, and even customer and household level. Staff now can “slice and dice” data in many ways. “It’s like being able to look at all the layers of a layered cake,” says Bill Connerty, BankFinancial’s assistant vice president of research.
For example, notes Connerty, “We will be able to look at how many checks a customer wrote this month versus last month, and if there’s a continual down slope over a three-month period, we know that customer is likely to churn. Or, if a customer has direct deposit … and then suddenly no longer has direct deposit, we know it immediately.” Those customers are segmented to a high-priority list and contacted immediately by local branch managers. “It’s as simple as exporting an Excel file monthly to the bankers,” says Connerty.
Then, the bank can create new models for customers likely to churn and identify which retention efforts various segments respond to. In the future, Connerty says BankFinancial hopes to automate the process, centralizing the outbound telemarketing efforts to a call center.
One of the primary goals was to keep things simple for his two-person staff. And after running a few tests, he says, “We’re really pumped up about it.”
They enlisted SPSS’s recently released PredictiveMarketing software, which enables the integration of data sources, such as records at the transactional level, account level, and even customer and household level. Staff now can “slice and dice” data in many ways. “It’s like being able to look at all the layers of a layered cake,” says Bill Connerty, BankFinancial’s assistant vice president of research.
For example, notes Connerty, “We will be able to look at how many checks a customer wrote this month versus last month, and if there’s a continual down slope over a three-month period, we know that customer is likely to churn. Or, if a customer has direct deposit … and then suddenly no longer has direct deposit, we know it immediately.” Those customers are segmented to a high-priority list and contacted immediately by local branch managers. “It’s as simple as exporting an Excel file monthly to the bankers,” says Connerty.
Then, the bank can create new models for customers likely to churn and identify which retention efforts various segments respond to. In the future, Connerty says BankFinancial hopes to automate the process, centralizing the outbound telemarketing efforts to a call center.
One of the primary goals was to keep things simple for his two-person staff. And after running a few tests, he says, “We’re really pumped up about it.”
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