An RNN-Survival Model to Decide Email Send Times

TL;DR

This paper introduces an RNN-based survival model to predict email open times, enabling firms to optimize send times for improved engagement based on sequential email interactions.

Contribution

The paper presents a novel RNN-survival model that leverages sequential email data to accurately predict times-to-open, outperforming traditional survival analysis methods.

Findings

RNN-Survival outperforms traditional survival analysis in predicting times-to-open.

Accurate prediction of open times enables optimal email send timing.

Model improves email engagement by tuning send times based on predicted open times.

Abstract

Email communications are ubiquitous. Firms control send times of emails and thereby the instants at which emails reach recipients (it is assumed email is received instantaneously from the send time). However, they do not control the duration it takes for recipients to open emails, labeled as time-to-open. Importantly, among emails that are opened, most occur within a short window from their send times. We posit that emails are likely to be opened sooner when send times are convenient for recipients, while for other send times, emails can get ignored. Thus, to compute appropriate send times it is important to predict 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 of emails sent to a million customers over five months. The sequence of emails received by a person from a sender is a result of interactions with past emails from the sender, and hence contain useful signal that inform our model. This sequential dependence affords our proposed RNN-Survival (RNN-S) approach to outperform survival analysis approaches in predicting times-to-open. We show that best times to send emails can be computed accurately from predicted times-to-open. This approach allows a firm to tune send times of emails, which is in its control, to favorably influence open rates and engagement.