Abstract: A method including applying a propensity model to a subject vector to generate a propensity value estimating a probability that a subject will perform an action. The subject vector has a data structure having features storing information regarding the subject. The method also includes applying a Shapley additive explanation tool to the propensity model to generate a subset of the features that contributed to the propensity value more than a remaining set of the features. The method also includes selecting an actionable feature from the subset of the features. The actionable feature includes a feature in the subset that an entity is able to influence. The method also includes applying a correlation model to labels for training data and the actionable feature to generate an output that describes a reason why the subject performs the action. The method also includes presenting the actionable feature and the output.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: Certain aspects of the present disclosure provide techniques for intelligent grouping of travel data for review through a user interface. In one example, a method for providing grouped travel data to a user interface of an application includes receiving travel data from an application running on a remote device; generating one or more travel data-based features from the travel data thereby creating featurized travel data; applying a pattern mining technique to the featurized travel data to detect a plurality of patterns in the featurized travel data; for each trip record in the featurized travel data: determining a plurality of trip record groups in which the trip record falls based on the plurality of patterns; and adding the trip record to a trip record group of the plurality of trip record groups according to a prioritization scheme; and transmitting the trip record group to the application running on the remote device.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: Dynamic state-space modeling within a special purpose hardware platform to determine non-conversion risks for each trial user and churn risks for each active subscriber having exhibited a sequence of behaviors. The state-space model may be operable to determine a loss risk for each of a provider's active trial users and/or subscribers.

Abstract: Certain aspects of the present disclosure provide techniques for intelligent grouping of travel data for review through a user interface. In one example, a method for providing grouped travel data to a user interface of an application includes receiving travel data from an application running on a remote device; generating one or more travel data-based features from the travel data thereby creating featurized travel data; applying a pattern mining technique to the featurized travel data to detect a plurality of patterns in the featurized travel data; for each trip record in the featurized travel data: determining a plurality of trip record groups in which the trip record falls based on the plurality of patterns; and adding the trip record to a trip record group of the plurality of trip record groups according to a prioritization scheme; and transmitting the trip record group to the application running on the remote device.

Abstract: The instant systems and methods are directed to a contextual bandits machine learning model configured to enable granular synchronized ecosystem personalization and optimization. The system and methods determine an objective and feed the objective and one more lifecycle model propensity scores as inputs to the contextual bandits machine learning model. The contextual bandits machine learning model then generates one or more potential weighted model rewards, wherein each potential weighted model reward includes at least a desired user action, a weight, a channel, and an expected change to the objective, and selects a weighted model reward that optimizes the objective. An action recommendation is subsequently transmitted to a user device based on the weighted model reward, wherein the action recommendation is presented in a selected channel associated with the weighted model reward. Feedback associated with the action recommendation is collected and used in training and fine-tuning of the model.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: Certain aspects of the present disclosure provide techniques for recommending trip purposes to users of an application. Embodiments include receiving labeled travel data from the application running on a remote device including a plurality of trip purposes. Embodiments include building a topic model representing words associated with a plurality of topics. Embodiments include training a topic prediction model, using the plurality of topics and one or more features derived from each of the plurality of trip records, to output a topic based on an input trip record. Embodiments include training a purpose prediction model, using the topic model and the plurality of trip purposes, to output a trip purpose based on an input topic. The trip purpose may be recommended to a user via a user interface of the application running on the remote device.

Abstract: Aspects of the present disclosure provide techniques for displaying reduced data sets based on pre-classification of a larger data set. Embodiments include receiving a plurality of activity records describing a plurality of activities associated with the user. Embodiments further include grouping the plurality of activities into one or more pre-classified data sets based on the plurality of activity records. Embodiments further include providing the user with a summary of a pre-classified data set of the one or more pre-classified data sets via a user interface. Embodiments further include providing the user, via the user interface, with a user interface element that allows the user to provide input related to the pre-classified data set based on the summary. Embodiments further include receiving input from the user via the user interface, the input relating to the pre-classified data set.

Abstract: Certain aspects of the present disclosure provide techniques for intelligent grouping of travel data for review through a user interface. In one embodiment, a method for providing grouped travel data to a user interface of an application, comprises: receiving a plurality of trip records from an application running on a remote device; providing a first subset of the plurality of trip records to a prediction model; providing a second subset of the plurality of trip records to a model training module; receiving labels for each trip record of the first subset of the plurality of trip records from the prediction model; grouping the first subset of the plurality of trip records based on the received labels; and transmitting the grouped first subset of the plurality of trip records to the application running on the remote device.

Abstract: Certain aspects of the present disclosure provide techniques for intelligent grouping of travel data for review through a user interface. In one example, a method for providing grouped travel data to a user interface of an application includes receiving travel data from an application running on a remote device; generating one or more travel data-based features from the travel data thereby creating featurized travel data; applying a pattern mining technique to the featurized travel data to detect a plurality of patterns in the featurized travel data; for each trip record in the featurized travel data: determining a plurality of trip record groups in which the trip record falls based on the plurality of patterns; and adding the trip record to a trip record group of the plurality of trip record groups according to a prioritization scheme; and transmitting the trip record group to the application running on the remote device.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: Predictive modeling within a special purpose hardware platform to determine scenarios that are most likely to increase conversion potential for each trial user and retention potential for each active subscriber of a service, collectively referred to as a propensity score. The predictive models are integrated with a contextual marketing system that uses a loss risk assessment to learn user behavior and optimize content messaging designed to improve actual conversion or retention behavior for the user.

Abstract: Systems and methods of subscriber retention analysis are disclosed. A system is configured to obtain an instance of a current subscriber data for the first current subscriber subscribed to a product for a first amount of time and configured to provide the first instance of the current subscriber data to a machine learning (ML) classification model. Training the ML classification model is based on a plurality of data sets as training data. Each data set includes an instance of historic subscriber data over the first amount of time of a subscription for a historic subscriber. The system is also configured to generate, using the ML classification model, a predicted likelihood in retaining the first current subscriber based on the first instance of the current subscriber data.

Abstract: Certain aspects of the present disclosure provide techniques for intelligent grouping of travel data for review through a user interface. In one example, a method for providing grouped travel data to a user interface of an application includes receiving travel data from an application running on a remote device; generating one or more travel data-based features from the travel data thereby creating featurized travel data; applying a pattern mining technique to the featurized travel data to detect a plurality of patterns in the featurized travel data; for each trip record in the featurized travel data: determining a plurality of trip record groups in which the trip record falls based on the plurality of patterns; and adding the trip record to a trip record group of the plurality of trip record groups according to a prioritization scheme; and transmitting the trip record group to the application running on the remote device.

Abstract: Certain aspects of the present disclosure provide techniques for recommending trip purposes to users of an application. Embodiments include receiving labeled travel data from the application running on a remote device including a plurality of trip purposes. Embodiments include building a topic model representing words associated with a plurality of topics. Embodiments include training a topic prediction model, using the plurality of topics and one or more features derived from each of the plurality of trip records, to output a topic based on an input trip record. Embodiments include training a purpose prediction model, using the topic model and the plurality of trip purposes, to output a trip purpose based on an input topic. The trip purpose may be recommended to a user via a user interface of the application running on the remote device.

Abstract: A modular data center system includes an upper module including a roller assembly attached to a frame of the upper module, the roller assembly configured to engage at least a portion of a rail assembly attached to a structure of a data center building; and a lower module attachable to the upper module and configured to suspend above a floor of a human-occupiable workspace of the data center that supports a plurality of racks including a plurality of heat-generating electronic devices, the lower module including a power support assembly configured to provide electrical power to the plurality of heat-generating electronic devices.

Abstract: Certain aspects of the present disclosure provide techniques for providing personalized groups of travel data for review through a user interface. Embodiments include receiving trip records associated with a user from an application running on a remote device, providing the trip records to a prediction model, and receiving a plurality of groups from the prediction model, each group of the plurality of groups comprising a subset of the trip records. Embodiments include providing each group of the plurality of groups to a personalization model, the personalization model having been trained based on user feedback to determine personalization scores for each group of the plurality of groups. Embodiments include receiving a personalization score for each group of the plurality of groups from the personalization model and transmitting one or more groups selected based on the personalization scores to the application to be displayed via the user interface.