Abstract: A method and apparatus for training a neural network to compute hazard functions for customers and analyzing hazard functions, both for an individual customer, and for set of customers to focus marketing techniques. The hazard function represents the likelihood of churn for a particular customer. The gain in lifetime value is also calculated for each customer which incorporates the present value of the customer with the future value of the customer if a new contract is entered. The overall shape of the hazard function, combined with the gain in lifetime value, specifies what marketing techniques are to be applied together with what additional incentives are to be offered to the customer in order prevent churn.

Abstract: Methods and systems operating in accordance with the present invention provide an interactive directed data analysis tool. The tool provides a user interface that receives a model from a data mining system. The tool computes specified business metrics representing business measures of quality reflected by the model. The model parameters may be manipulated by a user. Each time parameters of a model change, the business metrics are re-computed, thereby promoting directed data understanding by allowing a user to immediately see the impact of any changes made.

Abstract: Methods and systems are provided for performing interactive directed data analysis. An interactive data analysis tool provides a user interface that receives a model from a data mining system. The tool computes specified business metrics representing business measures of quality reflected by the model. The model parameters may be manipulated by a user. Each time parameters of a model change, the business metrics are re-computed, thereby promoting directed data understanding by allowing a user to immediately see the impact of any changes made.