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Email communications are ubiquitous. Firms control send times of emails andthereby the instants at which emails reach recipients (it is assumed email isreceived instantaneously from the send time). However, they do not control theduration it takes for recipients to open emails, labeled as time-to-open.Importantly, among emails that are opened, most occur within a short windowfrom their send times. We posit that emails are likely to be opened sooner whensend times are convenient for recipients, while for other send times, emailscan get ignored. Thus, to compute appropriate send times it is important topredict times-to-open accurately. We propose a recurrent neural network (RNN)in a survival model framework to predict times-to-open, for each recipient.Using that we compute appropriate send times. We experiment on a data set ofemails sent to a million customers over five months. The sequence of emailsreceived by a person from a sender is a result of interactions with past emailsfrom the sender, and hence contain useful signal that inform our model. Thissequential dependence affords our proposed RNN-Survival (RNN-S) approach tooutperform survival analysis approaches in predicting times-to-open. We showthat best times to send emails can be computed accurately from predictedtimes-to-open. This approach allows a firm to tune send times of emails, whichis in its control, to favorably influence open rates and engagement.

An RNN-Survival Model to Decide Email Send Times