Abstract: A customer metric is calculated for customer records corresponding to a plurality of customers and recording banking activities and/or attributes of the plurality of customers. A target segment is identified based on the metrics by comparing the metric to a threshold condition. The target segment is divided into action segments and a different customer development action is performed for each segment. Logistic regression is performed with respect to the action segments and cluster equations are generated that describe sub-segments that have combinations of activities and attributes that are indicative of a positive response to the customer development action. The process may be repeated for the sub-segments using the same or a different metric thereby identifying more and more specific sub-groups of customers that respond similarly.

Abstract: In one embodiment, a computer accessible medium stores a plurality of instructions which, when executed: receive account data corresponding to a plurality of accounts at a financial institution; and generate a retention score for each account. The retention score comprises a numerical value that indicates a relative likelihood of retention of that account by the financial institution. In some embodiments, the retention score also comprises a component indicator field that indicates one or more components that are affecting the numerical value. In one embodiment, the numerical value of the retention score may be used to affect an overdraft limit for the account. Some embodiments generate a profit score for each account, and divide the plurality of accounts into subsets based on the retention/profit scores. Different subsets may have different marketing strategies. Retention scores calculated before and after a marketing campaign may be used to evaluate the campaign.