Abstract: Methods and apparatuses are described for automated optimization and personalization of customer-specific communication channels using feature classification. A server captures historical interaction data comprising a channel type, a user identifier, an interaction date, and a user response value. The server generates a channel feature vector for each combination of channel type, user identifier, and interaction date. The server identifies features from the channel feature vectors for each different channel type and aggregates the features into a common feature vector. The server executes a trained classification model on the common feature vectors to select user identifiers for each different channel type that have an engagement probability value at or above a corresponding threshold.

Abstract: Methods and apparatuses are described for automated optimization and personalization of customer-specific communication channels using feature classification. A server captures historical interaction data comprising a channel type, a user identifier, an interaction date, and a user response value. The server generates a channel feature vector for each combination of channel type, user identifier, and interaction date. The server identifies features from the channel feature vectors for each different channel type and aggregates the features into a common feature vector. The server executes a trained classification model on the common feature vectors to select user identifiers for each different channel type that have an engagement probability value at or above a corresponding threshold.