Neural network-based prediction of SMTP errors and bounces in cold emailing: a comparative study of GRU, CNN, and TCN

The cold email industry faces significant challenges in ensuring successful message delivery, with SMTP errors and bounces being common occurrences. Predicting these errors can help optimize email delivery and improve senders’ reputation. In this study, the abilities of Gated Recurrent Units (GRU), Convolutional Neural Network (CNN), and Temporal Convolutional Network (TCN) were examined to predict SMTP errors and bounces in the context of a cold email dataset. The study reveals that both the GRU networks and the CNNs have achieved over 70% accuracy in predicting SMTP errors and bounces. Exploiting all three architectures, GRU, MLP, and TCN, can substantially enhance the management and optimization of the cold email sending process, leading to improved email delivery rates and better sender reputation.