Abstract: A device may obtain, from a collection of data sources, personal information and activity information for a group of individuals. The device may generate profile information by associating the personal information and the activity information for each individual in the group of individuals. The device may determine a set of features capable of being used to train a set of machine learning models by using one or more feature identification techniques to analyze the profile information. The set of features may include generic features and contextualized features associated with the group of individuals. The device may train the set of machine learning models using one or more subsets of features of the set of features. The device may use the set of machine learning models to process a request from a client device for a list of prospective targets for a campaign.

Abstract: A device may determine a set of trip attributes, associated with a driving trip, based on at least two of: location data associated with the driving trip, geospatial data associated with the driving trip, or weather data associated with the driving trip. The device may derive a set of driving style variables, associated with a manner in which the vehicle is driven during the driving trip, based on at least two of: the location data, the geospatial data, the weather data, acceleration data associated with the driving trip, or a location-based variable derived from the location data. The device may generate a trip risk score, associated with the driving trip and based on the set of trip attributes and the set of driving style variables, that includes a metric indicating a level of risk associated with the driving trip. The device may provide information associated with the trip risk score.

Abstract: A device may obtain, from a collection of data sources, personal information and activity information for a group of individuals. The device may generate profile information by associating the personal information and the activity information for each individual in the group of individuals. The device may determine a set of features capable of being used to train a set of machine learning models by using one or more feature identification techniques to analyze the profile information. The set of features may include generic features and contextualized features associated with the group of individuals. The device may train the set of machine learning models using one or more subsets of features of the set of features. The device may use the set of machine learning models to process a request from a client device for a list of prospective targets for a campaign.

Abstract: A device may determine a set of trip attributes, associated with a driving trip, based on at least two of: location data associated with the driving trip, geospatial data associated with the driving trip, or weather data associated with the driving trip. The device may derive a set of driving style variables, associated with a manner in which the vehicle is driven during the driving trip, based on at least two of: the location data, the geospatial data, the weather data, acceleration data associated with the driving trip, or a location-based variable derived from the location data. The device may generate a trip risk score, associated with the driving trip and based on the set of trip attributes and the set of driving style variables, that includes a metric indicating a level of risk associated with the driving trip. The device may provide information associated with the trip risk score.