Main Factors Driving the Open Rate of Email Marketing Campaigns
Email Marketing is one of the most important traffic sources in Digital Marketing. It yields a high return on investment for the company and offers a cheap and fast way to reach existent or potential clients. Getting the recipients to open the email is the first step for a successful campaign. Thus, it is important to understand how marketers can improve the open rate of a marketing campaign. In this work, we analyze what are the main factors driving the open rate of financial email marketing campaigns. For that purpose, we develop a classification algorithm that can accurately predict if a campaign will be labeled as Successful or Failure. A campaign is classified as Successful if it has an open rate higher than the average, otherwise it is labeled as Failure. To achieve this, we have employed and evaluated three different classifiers. Our results showed that it is possible to predict the performance of a campaign with approximately 82% accuracy, by using the Random Forest algorithm and the redundant filter selection technique. With this model, marketers will have the chance to sooner correct potential problems in a campaign that could highly impact its revenue. Additionally, a text analysis of the subject line and preheader was performed to discover which keywords and keyword combinations trigger a higher open rate. The results obtained were then validated in a real setting through A/B testing.
KeywordsDigital Marketing Email Marketing Marketing campaigns Open rate
This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project: UID/EEA/50014/2019.
- 1.Afzal, H., Khan, M.A., ur Rehman, K., Ali, I., Wajahat, S.: Consumer’s trust in the brand: can it be built through brand reputation, brand competence and brand predictability. Int. Bus. Res. 3(1), 43 (2010)Google Scholar
- 2.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. IEEE, October 2014Google Scholar
- 4.Biloš, A., Turkalj, D., Kelić, I.: Open-rate controlled experiment in e-mail marketing campaigns. Trziste/Market 28(1), 93–109 (2016)Google Scholar
- 8.Email marketing industry report. https://www.campaignmonitor.com/resources/guides/2018-email-marketing-industry-report/
- 10.IBM Corp. Released 2016. IBM SPSS Statistics for Windows, Version 24.0. IBM Corp, Armonk, NY (2016)Google Scholar
- 11.Jaidka, K., Goyal, T., Chhaya, N.: Predicting email and article clickthroughs with domain-adaptive language models. In: Proceedings of the 10th ACM Conference on Web Science, pp. 177–184. ACM, May 2018Google Scholar
- 12.Koller, D., Sahami, M.: Toward optimal feature selection. In: Proceedings of the Thirteenth International Conference on International Conference on Machine Learning, pp. 284–292. ICML’96 (1996)Google Scholar
- 13.Luo, X., Nadanasabapathy, R., Zincir-Heywood, A.Nur, Gallant, K., Peduruge, J.: Predictive analysis on tracking emails for targeted marketing. In: Japkowicz, N., Matwin, S. (eds.) DS 2015. LNCS (LNAI), vol. 9356, pp. 116–130. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24282-8_11CrossRefGoogle Scholar
- 14.Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
- 15.Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the Fourth International Conference on the Practical Applications of Knowledge Discovery and Data Mining, pp. 29–39. Citeseer, April 2000Google Scholar