"EMAIL PERFORMANCE PREDICTIONS WITHOUT CAMPAIGN HISTORY" has been ACCEPTED for presentation at the 5th International Conference on Data Science and Machine Learning (DSML 2024) scheduled to be held on December 21 ~ 22, 2024, in Sydney, Australia.
EMAIL PERFORMANCE PREDICTIONS WITHOUT CAMPAIGN HISTORY
Sourabh Khot2, Venkata Duvvuri1, Heejae Roh3, and Anish Mangipudi4
1College of Professional Studies, Northeastern University
2College of Professional Studies, Northeastern University
3College of Professional Studies, Northeastern University
4Langley High School, Mclean, Virginia
Abstract
Email will remain a vital marketing tool in 2024. Email marketing involves sending commercial emails to a targeted audience. It currently produces a significant ROI (return on investment) in the marketing sector [1]. This research paper presents a comprehensive study on predicting email open rates, focusing specifically on the influence of subject lines. The open-rate prediction algorithm SLk relies on the semantic features of subject lines utilizing a seed dataset of 4500 anonymized subject lines from diverse business sectors. The algorithm integrates data preprocessing, tokenization, and a custom-built repository of power words and negative words to enhance prediction accuracy. In our experiments the actual open rate margin of error was tracking close to what's allowed as per input error giving confidence that SLk can be directionally used for optimizing subject lines performance without prior history. The findings suggest that precise manipulation of subject line features can significantly improve the efficacy of email campaigns.
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