Abstract
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.
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Notes
- 1.
A person or company that advertises or promotes something.
- 2.
Person or entity that sends the email.
- 3.
Short description of the email content.
- 4.
Description that complements the email subject line.
- 5.
Fast, commission, credit, loan, free, money and easy.
- 6.
{change; bank}, {card; free}, {fast; online}, {fast; easy} and {free; commission}.
- 7.
This filter selection was performed inside each one of the ten Cross Validation loops.
References
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)
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 2014
Berthold, M.R., et al.: KNIME: the konstanz information miner: version 2.0 and beyond. ACM SIGKDD Explor. Newslett. 11(1), 26–31 (2009)
Biloš, A., Turkalj, D., Kelić, I.: Open-rate controlled experiment in e-mail marketing campaigns. Trziste/Market 28(1), 93–109 (2016)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Bonfrer, A., Drèze, X.: Real-time evaluation of email campaign performance. Mark. Sci. 28(2), 251–263 (2009)
Chittenden, L., Rettie, R.: An evaluation of email marketing and factors affecting response. J. Target. Meas. Anal. Mark. 11(3), 203–217 (2003)
Email marketing industry report. https://www.campaignmonitor.com/resources/guides/2018-email-marketing-industry-report/
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
IBM Corp. Released 2016. IBM SPSS Statistics for Windows, Version 24.0. IBM Corp, Armonk, NY (2016)
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 2018
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)
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_11
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
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 2000
Acknowledgements
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.
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Conceição, A., Gama, J. (2019). Main Factors Driving the Open Rate of Email Marketing Campaigns. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_12
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