Abstract
Email marketing works as a top channel to generate leads for many businesses. The marketing automation platforms are part of this strategy and can improve the success of email campaigns. Many of these platforms use subject line tools to predict if an email will be opened or not, as a success metric. However, the text content is unused. Thus, this work proposes to predict the likelihood of a user clicking the Call to Action button of an email based on the content. We implement our proposal in a real-case scenario of corporate communication emails from a private university in Mexico. After building a machine learning model, the results were promising and validated our proof-of-concept. We consider the results relevant for further investigation around other ways to improve the success of an email using the text content, and this model could be reliable in most campaigns and could be used to determine which words influence the click rate metric the most.
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References
Balakrishnan, R., Parekh, R.: Learning to predict subject-line opens for large-scale email marketing. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 579–584 (2014). https://doi.org/10.1109/BigData.2014.7004277
Holovach, H.: 105 email marketing statistics you should know in 2023. https://snov.io/blog/email-marketing-statistics/
Junnarkar, A., Adhikari, S., Fagania, J., Chimurkar, P., Karia, D.: E-mail spam classification via machine learning and natural language processing. In: 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pp. 693–699 (2021). https://doi.org/10.1109/ICICV50876.2021.9388530
Miller, R., Charles, E.: A psychological based analysis of marketing email subject lines. In: 2016 Sixteenth International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 58–65 (Sep 2016). https://doi.org/10.1109/ICTER.2016.7829899
Paulo, M., Miguéis, V.L., Pereira, I.: Leveraging email marketing: Using the subject line to anticipate the open rate. Expert Syst. Appl. 207, 117974 (2022). https://doi.org/10.1016/j.eswa.2022.117974, https://www.sciencedirect.com/science/article/pii/S0957417422012040
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inform. Process. Manage. 45(4), 427–437 (2009)
Wang, Z.: Working with, preparing bag-of-word data for regression (2016). https://stackoverflow.com/questions/37510014/working-with-preparing-bag-of-word-data-for-regression Accessed 4 June 2023
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Bernardo, E., Lorenzo, K., Reyes, G., Ponce, H. (2024). Predict Email Success Based on Text Content. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Computational Intelligence. MICAI 2023. Lecture Notes in Computer Science(), vol 14391. Springer, Cham. https://doi.org/10.1007/978-3-031-47765-2_6
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DOI: https://doi.org/10.1007/978-3-031-47765-2_6
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