Abstract: A commercial process with a dependent variable can be associated with a set of independent variables. The commercial process can continuously provide data collection opportunities. An intervention is designed using a model to predict the dependent outcome. The actual outcome of the intervention can be determined within the window of utility for these data. One objective is to improve intervention outcomes with prediction. Purely random outcomes (no model prediction) and outcomes resulting from the intervention (model operations) are aggregated into separate files—a sequence of control model data files and a sequence of model data files of operational data. These model data files and control model data files are used to analyze model performance and to react automatically when identified conditions warrant.

Abstract: A commercial process with a dependent variable can be associated with a set of independent variables. The commercial process can continuously provide data collection opportunities. An intervention is designed using a model to predict the dependent outcome. The actual outcome of the intervention can be determined within the window of utility for these data. One objective is to improve intervention outcomes with prediction. Purely random outcomes (no model prediction) and outcomes resulting from the intervention (model operations) are aggregated into separate files—a sequence of control model data files and a sequence of model data files of operational data. These model data files and control model data files are used to analyze model performance and to react automatically when identified conditions warrant.

Abstract: A commercial process with a dependent variable can be associated with a set of independent variables. The commercial process can continuously provide data collection opportunities. An intervention is designed using a model to predict the dependent outcome. The actual outcome of the intervention can be determined within the window of utility for these data. One objective is to improve intervention outcomes with prediction. Purely random outcomes (no model prediction) and outcomes resulting from the intervention (model operations) are aggregated into separate files—a sequence of control model data files and a sequence of model data files of operational data. These model data files and control model data files are used to analyze model performance and to react automatically when identified conditions warrant.

Abstract: A method for monitoring performance of an IT-based computing system over the life cycle of the system is disclosed. The life cycle includes a conception phase, an analysis phase, an architecture design phase, a system design phase, a construction phase, a testing phase, a deployment phase, and finally, an operational phase. Performance requirements for the IT-based computing system are defined during the analysis phase. In the architecture design phase, the appropriate hardware and software infrastructure are selected for use in designing the base architecture for the system. In the system design phase, application specific system components are defined for the system. In the construction phase, the hardware platform is assembled and the software design specifications are turned into executable code. The constructed system is then tested for functionality and performance prior to being deployed for subsequent operation.