Abstract: Techniques are disclosed for specifying a predictive goal that describes an action that a customer of an organization may take or a behavior that a customer of the organization may exhibit. A training engine receives the predictive goal from a user via a Graphical User Interface (GUI). The user specifies the predictive goal by entering one or more customizable logical or comparative statements. A training engine divides historical data into training data and testing data, selects useful and relevant features from the historical data as inputs to a machine learning model, and trains the machine learning model. The training engine transmits the trained machine learning model to an inference engine that generates, for each customer in a historical data set, a probability that the customer will satisfy the predictive goal within a specified time frame. The disclosed techniques further generate explanatory data associated with the trained machine learning model.

Abstract: Techniques are disclosed for generating explanatory data for predictions generated by a machine learning model. An explanatory engine receives a trained machine learning model and a plurality of predictions generated by the machine learning model. The explanatory engine calculates a predictive strength for the trained machine learning model based on the plurality of predictions. The explanatory engine further determines one or more features having at least a threshold influence on the plurality of predictions and displays, via a graphical user interface, one or more of the influential features and an indication of the predictive strength of the trained model.

Abstract: Techniques are disclosed for segmenting users based on user interactions with messages transmitted over various electronic communication channels. In some embodiments, a segmentation application normalizes sets of event data associated with user interactions with messages that are transmitted over multiple electronic communication channels to generate intermediate labels associated with the sets of event data. The normalization includes applying a time decay while accounting for messaging cadence, messaging volume, and user interactions with messages associated with an industry. The intermediate labels are combined to generate a final label associated with the user via a pairwise combination technique that maintains a more favorable label when a pair of intermediate labels are combined. Additional messages can then be generated and/or transmitted based on the final label.

Abstract: A process and system for heating semiconductor substrates in a processing chamber on a susceptor as disclosed. In accordance with the present invention, the susceptor includes a support structure made from a material having a relatively low thermal conductivity for suspending the wafer over the susceptor. The support structure has a particular height that inhibits or prevents radial temperature gradients from forming in the wafer during high temperature processing. If needed, recesses can be formed in the susceptor for locating and positioning a support structure. The susceptor can include a wafer supporting surface defining a pocket that has a shape configured to conform to the shape of a wafer during a heat cycle.

Abstract: A process and system for heating semiconductor substrates in a processing chamber on a susceptor as disclosed. In accordance with the present invention, the susceptor includes a support structure made from a material having a relatively low thermal conductivity for suspending the wafer over the susceptor. The support structure has a particular height that inhibits or prevents radial temperature gradients from forming in the wafer during high temperature processing. If needed, recesses can be formed in the susceptor for locating and positioning a support structure. The susceptor can include a wafer supporting surface defining a pocket that has a shape configured to conform to the shape of a wafer during a heat cycle.