Predicting Sales E-Mail Responders using a Natural Language Model

TitlePredicting Sales E-Mail Responders using a Natural Language Model
Publication TypeJournal Article
Year of Publication2015
AuthorsKrause, M., & Kulkarni A.
Published inCommunications of the ACM

Email is a standard and popular means of establishing potential business relationships between salespeople and future customers, but it is difficult for machines to generate messages that are as convincing as a human author. We take first steps towards the automatic generation of human-quality emails by presenting a model predicting if an e-mail will be successful in eliciting a response. In this paper we propose a natural language model that predicts whether a human-authored sales e-mail will get a response from a previously uncontacted recipient. We test our algorithm with a set of 116 outbound sales e-mails used in practice. Our algorithm is successfully able to predict if an e-mail in this set received a response with an F1 score of 0.81. This work provides initial steps in understanding how to automate convincing communication over email between humans and computers.

ICSI Research Group

Networking and Security