Abstract: Data for a transaction is modeled by receiving a source set of data. The source set of data comprises data representing a plurality of transactions stored in a source transaction database. An estimation model for modeling data for a transaction is received. A mapping between the source set of data and a model parameter database is received. The model parameter database comprises a plurality of model parameters for the estimation model. The parameters extracted from the model parameter database and the source set of data in a Bayesian framework are combined using a parameter estimation engine to obtain an updated set of model parameters. The updated set of model parameters is stored in the model parameter database.

Abstract: A price sensitivity score is calculated for a prospect by obtaining data associated with a prospect. A scoring function is determined (which is a function of one or more scoreable variables) based at least in part on training data set. A price sensitivity score is calculated by evaluating the scoring function using the data associated with the prospect.