The application concerns executing digital content campaigns, such as email marketing campaigns, using machine learning. Past emails to a user are partitioned into time bins 702. A recurrent neural network is trained on these past emails 704 and used to select a time bin during which a new email is sent to the user 706. The recurrent neural network may generate a predicted engagement metric for a past email based on the email’s time bin 704a, and then compare the predicted engagement metric to a ground-truth engagement metric for the past email 704b. The recurrent neural network may generate a hazard metric for one of the time bins 706a, where the hazard metric indicates the instantaneous probability that a user will interact with a new email sent during that time bin. The hazard metric may be based on a survival function embedded in the recurrent neural network. A schedule for sending multiple emails may be generated based on multiple time bins determined by the neural network. The time bins may span a planning window, and the past emails may be divided among the bins according to a grouping proportion.
Determining strategic digital content transmission time utilizing recurrent neural networks and survival analysis