Predicting Email and Article Clickthroughs with Domain-adaptive Language Models

Abstract

Marketing practices have adopted the use of computational approaches in order to optimize the performance of their promotional emails and site advertisements. In the case of promotional emails, subject lines have been found to offer a reliable signal of whether the recipient will open an email or not. Clickbait headlines are also known to drive reader engagement. In this study, we explore the differences in recipients' preferences for subject lines of marketing emails from different industries, in terms of their clickthrough rates on marketing emails sent by different businesses in Finance, Cosmetics and Television industries. Different stylistic strategies of subject lines characterize high clickthroughs in different commercial verticals. For instance, words providing insight and signaling cognitive processing lead to more clickthroughs for the Finance industry; on the other hand, social words yield more clickthroughs for the Movies and Television industry. Domain adaptation can further improve predictive performance for unseen businesses by an average of 16.52% over generic industry-specific predictive models. We conclude with a discussion on the implications of our findings and suggestions for future work.