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.

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.

Abstract: Methods and apparatuses are described for determining future user actions using time-based featurization of clickstream data. A server captures clickstream data corresponding to web browsing sessions and converts the clickstream data into tokens by identifying each unique URL and parsing each unique URL into tokens. The server generates a frequency matrix based upon the tokens, and generates a latent feature vector for each URL in the session based upon the frequency matrix. The server merges the latent feature vectors and the clickstream data into an aggregate clickstream vector set for a user. The server assigns time-decayed weight values to each latent feature vector in the aggregate clickstream vector set. The server combines the time-decayed latent feature vectors to generate a clickstream embedding for the user, and executes a machine learning model using the clickstream embedding to generate one or more predicted actions of the user.