Abstract: A machine learning model may be trained using annotated communications data. Each communication (e.g., a short messaging system (SMS) message or email) is annotated with a measure of user interaction. The machine learning model is thus trained to predict a measure of user interaction for future communications. Before sending future communications, at least a portion of the communication is provided to the trained machine learning model to predict the expected measure of user interaction with the communication. In response to the prediction, the sender of the communication may alter the communication. The system may automatically send the communication if the predicted measure of user interaction exceeds a predetermined threshold and only prompt the user if the predicted measure of user interaction does not exceed the predetermined threshold.

Abstract: A machine learning model may be trained using annotated communications data. Each communication (e.g., a short messaging system (SMS) message or email) is annotated with a measure of user interaction. The machine learning model is thus trained to predict a measure of user interaction for future communications. Before sending future communications, at least a portion of the communication is provided to the trained machine learning model to predict the expected measure of user interaction with the communication. In response to the prediction, the sender of the communication may alter the communication. The system may automatically send the communication if the predicted measure of user interaction exceeds a predetermined threshold and only prompt the user if the predicted measure of user interaction does not exceed the predetermined threshold.

Abstract: Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. One method includes an operation for training a machine-learning program to generate a frequency model that determines a frequency for sending communications to users. The training utilizes training data defined by features related to user information and responses of users to previous communications to the users. The method further includes determining, by the frequency model and based on information about a first user, a first frequency for the first user. The first frequency identifies the number of communications to transmit to the first user per period of time. Further, the method includes operations for receiving a communication request to send one or more communications to the first user and determining send times for the one or more communications to the first user based on the first frequency. The communications are sent at the determined send times.

Abstract: Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. One method includes an operation for training a machine-learning program to generate a frequency model that determines a frequency for sending communications to users. The training utilizes training data defined by features related to user information and responses of users to previous communications to the users. The method further includes determining, by the frequency model and based on information about a first user, a first frequency for the first user. The first frequency identifies the number of communications to transmit to the first user per period of time. Further, the method includes operations for receiving a communication request to send one or more communications to the first user and determining send times for the one or more communications to the first user based on the first frequency. The communications are sent at the determined send times.

Abstract: Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. Send Time Optimization (STO) uses machine learning (ML) to recommend a personalized send time based on a recipient's past engagement patterns. The purpose of the ML model is to learn patterns in the data automatically and use the patterns to make personalized predictions for each recipient. The send time recommended by the model is the time at which the model believes the recipient will be most likely to engage with the message, such as clicking or opening, and use of the send time mode is expected to increase engagement from recipients. Additional customizations include communication-frequency optimization, communication-channel selection, and engagement-scoring model.

Abstract: A machine learning model may be trained using annotated communications data. Each communication (e.g., a short messaging system (SMS) message or email) is annotated with a measure of user interaction. The machine learning model is thus trained to predict a measure of user interaction for future communications. Before sending future communications, at least a portion of the communication is provided to the trained machine learning model to predict the expected measure of user interaction with the communication. In response to the prediction, the sender of the communication may alter the communication. The system may automatically send the communication if the predicted measure of user interaction exceeds a predetermined threshold and only prompt the user if the predicted measure of user interaction does not exceed the predetermined threshold.

Abstract: Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. One method includes an operation for training a machine-learning program to generate a frequency model that determines a frequency for sending communications to users. The training utilizes training data defined by features related to user information and responses of users to previous communications to the users. The method further includes determining, by the frequency model and based on information about a first user, a first frequency for the first user. The first frequency identifies the number of communications to transmit to the first user per period of time. Further, the method includes operations for receiving a communication request to send one or more communications to the first user and determining send times for the one or more communications to the first user based on the first frequency. The communications are sent at the determined send times.

Abstract: Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. Send Time Optimization (STO) uses machine learning (ML) to recommend a personalized send time based on a recipient's past engagement patterns. The purpose of the ML model is to learn patterns in the data automatically and use the patterns to make personalized predictions for each recipient. The send time recommended by the model is the time at which the model believes the recipient will be most likely to engage with the message, such as clicking or opening, and use of the send time mode is expected to increase engagement from recipients. Additional customizations include communication-frequency optimization, communication-channel selection, and engagement-scoring model.