Main Factors Driving the Open Rate of Email Marketing Campaigns

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Conceição, A (Autor) Outra; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: Discovery Science - 22nd International Conference, DS 2019, Split, Croatia, October 28-30, 2019, Proceedings

Páginas: 145-154

22nd International Conference on Discovery Science, DS 2019

28 October 2019 through 30 October 2019

Indexação

Scopus - 10 Citações

Outras Informações

ID Authenticus: P-00R-FC6

DOI: 10.1007/978-3-030-33778-0_12

Abstract (EN): 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. © Springer Nature Switzerland AG 2019.

Idioma: Inglês

Tipo (Avaliação Docente): Científica