Visible to the public Learning to predict subject-line opens for large-scale email marketing

TitleLearning to predict subject-line opens for large-scale email marketing
Publication TypeConference Paper
Year of Publication2014
AuthorsBalakrishnan, R., Parekh, R.
Conference NameBig Data (Big Data), 2014 IEEE International Conference on
Date PublishedOct
KeywordsAccuracy, attribution scoring iterative method, Business, deals, Electronic mail, Email, feature extraction, human editors, large-scale e-mail marketing, learning, learning (artificial intelligence), marketing data processing, open e-mail rates, performance prediction accuracy improvement, Postal services, Predictive models, random forest based model training, subject, subject line performance, subject line syntax, subject-line keywords, subject-line open rate prediction learning, Weight measurement
Abstract

Billions of dollars of services and goods are sold through email marketing. Subject lines have a strong influence on open rates of the e-mails, as the consumers often open e-mails based on the subject. Traditionally, the e-mail-subject lines are compiled based on the best assessment of the human editors. We propose a method to help the editors by predicting subject line open rates by learning from past subject lines. The method derives different types of features from subject lines based on keywords, performance of past subject lines and syntax. Furthermore, we evaluate the contribution of individual subject-line keywords to overall open rates based on an iterative method-namely Attribution Scoring - and use this for improved predictions. A random forest based model is trained to combine these features to predict the performance. We use a dataset of more than a hundred thousand different subject lines with many billions of impressions to train and test the method. The proposed method shows significant improvement in prediction accuracy over the baselines for both new as well as already used subject lines.

DOI10.1109/BigData.2014.7004277
Citation Key7004277