Abstract: An automation analytics system and method for building analytical models for an education application uses data-availability segments of students, which are clustered into segment clusters, to create the analytical models for the segment clusters using a machine learning process. The analytical models can be used to identify at least at least actionable insights.

Abstract: Student data-to-insight-to-action-to-learning analytics system and method use an evidence-based action knowledge database to compute student success predictions, student engagement predictions, and student impact predictions to interventions. The evidence-based action knowledge database is updated by executing a multi-tier impact analysis on impact results of applied interventions. The multi-tier impact analysis includes using changes in key performance indicators (KPIs) for pilot students after each applied intervention and dynamic matching of the pilot students exposed to the appropriate interventions to other students who were not exposed to the appropriate interventions.

Abstract: An automation analytics system and method for building analytical models for an education application uses data-availability segments of students, which are clustered into segment clusters, to create the analytical models for the segment clusters using a machine learning process. The analytical models can be used to identify at least at least actionable insights.

Abstract: The present invention relates to a system and method for efficiently estimating the sensitivity, or elasticity, of customer demand to changes in price in a business-to-business market environment. More particularly, this method relies on a parametric demand model, and a corresponding offer model which is referred to as the Joint Demand Model. This model is used to estimate the elasticity of market segments using win-only transactional data. In addition, this invention provides a method for efficiently calculating the estimation error of the estimated elasticity, and uses such estimation error in a weighting scheme based on a hierarchical model in order to produce a reliable estimate of elasticity.