Automated customer engagement prediction and classification

Abstract

Methods and apparatuses are described for automated customer engagement prediction and classification. A server generates a feature vector comprising variables corresponding to historical user activity data for a user. The server encodes, for each feature vector, each variable in the feature vector into a corresponding weight-of-evidence value. The server transforms each encoded feature vector into an embedding in a multidimensional vector space. The server generates, for each user, a user engagement probability value by identifying embeddings of other users in proximity to the user embedding using a similarity measure and determining an engagement outcome for the identified embeddings. The server assigns each user to an engagement probability cluster based upon the engagement probability value for the user. The server generates instructions for a remote device to initiate communications to each user based upon the assigned engagement probability cluster.