Abstract: System and method including accessing a feature associated with a plurality of user identities (IDs); accessing a structure specifying mappings between the plurality of user IDs and a plurality of user canonical IDs; generating groups of feature values of the feature based on the mappings, each group of feature values being associated with a corresponding group of user IDs and with a corresponding user canonical ID; aggregating each group of feature values to calculate an aggregate feature value of the feature, each aggregate feature value associated with the corresponding user canonical ID; computing predictive traits associated with the plurality of user canonical IDs, the predictive traits including likelihoods of events or trait values, the computation of the predictive traits using the aggregate feature values associated with the corresponding user canonical IDs; and causing display, at a user interface (UI) of a computing device, of the computed predictive traits.

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 Rules-Based Just-In-Time (RBJIT) content streaming engine collects information such as behavior, usage, movement, and preferences about a user, any groups that the user is associated with, and the set of all users in general. Based on this information, the RBJIT may preferentially select multimedia content and content suggestions for user display, increasing the likelihood that the surfaced content will be of interest to a user. In this way, browsing time for a user on a device with a limited form factor is reduced and network bandwidth is conserved by not surfacing content that the user ultimately will not view.

Abstract: Described herein are techniques for optimizing scheduling of access events (e.g., downloads) on mobile devices based on server congestion. In some embodiments, response times are monitored for a number of servers at various times to establish availability patterns for those servers. An indication of a number of software applications installed upon a mobile device is used to identify a number of access events to be associated with that mobile device. The servers associated with those access events are identified and an access schedule is generated based on the availability patterns. The access schedule is then provided to the mobile device, which initiates execution of the access events according to the access schedule.

Abstract: A Rules-Based Just-In-Time (RBJIT) content streaming engine collects information such as behavior, usage, movement, and preferences about a user, any groups that the user is associated with, and the set of all users in general. Based on this information, the RBJIT may preferentially select multimedia content and content suggestions for user display, increasing the likelihood that the surfaced content will be of interest to a user. In this way, browsing time for a user on a device with a limited form factor is reduced and network bandwidth is conserved by not surfacing content that the user ultimately will not view.

Abstract: Described herein are techniques for optimizing scheduling of access events (e.g., downloads) on mobile devices based on server congestion. In some embodiments, response times are monitored for a number of servers at various times to establish availability patterns for those servers. An indication of a number of software applications installed upon a mobile device is used to identify a number of access events to be associated with that mobile device. The servers associated with those access events are identified and an access schedule is generated based on the availability patterns. The access schedule is then provided to the mobile device, which initiates execution of the access events according to the access schedule.

Abstract: A Rules-Based Just-In-Time (RBJIT) content streaming engine collects information such as behavior, usage, movement, and preferences about a user, any groups that the user is associated with, and the set of all users in general. Based on this information, the RBJIT may preferentially select multimedia content and content suggestions for user display, increasing the likelihood that the surfaced content will be of interest to a user. In this way, browsing time for a user on a device with a limited form factor is reduced and network bandwidth is conserved by not surfacing content that the user ultimately will not view.

Abstract: A Rules-Based Just-In-Time (RBJIT) content streaming engine collects information such as behavior, usage, movement, and preferences about a user, any groups that the user is associated with, and the set of all users in general. Based on this information, the RBJIT may preferentially select multimedia content and content suggestions for user display, increasing the likelihood that the surfaced content will be of interest to a user. In this way, browsing time for a user on a device with a limited form factor is reduced and network bandwidth is conserved by not surfacing content that the user ultimately will not view.

Abstract: A Rules-Based Just-In-Time (RBJIT) content streaming engine collects information such as behavior, usage, movement, and preferences about a user, any groups that the user is associated with, and the set of all users in general. Based on this information, the RBJIT may preferentially select multimedia content and content suggestions for user display, increasing the likelihood that the surfaced content will be of interest to a user. In this way, browsing time for a user on a device with a limited form factor is reduced and network bandwidth is conserved by not surfacing content that the user ultimately will not view.