Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that selectively process data based on the type of data available are described. In one aspect, a system includes a front-end server and a back-end server. The back-end server obtains, for each of a set of clients, membership data for the client and determines whether the membership data for the client includes one or more of (i) data specifying at least one segment on which the client claimed a spot or (ii) a geographic identifier for a primary geographic location for the client. Based on the determining a given membership data attribution model is selected for attributing membership data for the client to one or more routes and g at least a portion of the membership data for the client is attributed to the one or more routes using the given membership data attribution model.

Abstract: The present teaching relates to forecasting a campaign performance using predictive modeling. In one example, a request for forecasting a campaign performance is received from a user. A plurality of parameters associated with the request are retrieved. A predictive score is generated based on the plurality of parameters. A variable vector is constructed based on one or more of the plurality of campaign parameters selected by the user. A key performance indicator (KPI) matrix is generated in accordance with the predictive score based on the variable vector.

Abstract: The present teaching relates to forecasting a campaign performance using predictive modeling. In one example, a request for forecasting a campaign performance is received from a user. A plurality of parameters associated with the request are retrieved. A predictive score is generated based on the plurality of parameters. A variable vector is constructed based on one or more key performance indicators (KPIs) selected by the user. A campaign data matrix is generated in accordance with the predictive score based on the variable vector.

Abstract: Certain embodiments of the present disclosure describe the combined analysis of dynamic models and static models generated as part of a healthcare delivery process. Based on the combined analysis, As-Is and variation models (each having dynamic and static components) are generated. In one embodiment, the As-Is model components may be used in strategic planning. Likewise, in one embodiment, the variation model components may be used to derive respective dynamic and static quality metrics that may be used in report and control processes applied to the healthcare delivery process.