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A Novel Approach for Send Time Prediction on Email Marketing
| Content Provider | MDPI |
|---|---|
| Author | Carolina, Araújo Soares, Christophe Pereira, Ivo Coelho, Duarte Rebelo, Miguel Ângelo Madureira, Ana |
| Copyright Year | 2022 |
| Description | In the digital world, the demand for better interactions between subscribers and companies is growing, creating the need for personalized and individualized experiences. With the exponential growth of email usage over the years, broad flows of campaigns are sent and received by subscribers, which reveals itself to be a problem for both companies and subscribers. In this work, subscribers are segmented by their behaviors and profiles, such as (i) open rates, (ii) click-through rates, (iii) frequency, and (iv) period of interactions with the companies. Different regressions are used: (i) Random Forest Regressor, (ii) Multiple Linear Regression, (iii) K-Neighbors Regressor, and (iv) Support Vector Regressor. All these regressions’ results were aggregated into a final prediction achieved by an ensemble approach, which uses averaging and stacking methods. The use of Long Short-Term Memory is also considered in the presented case. The stacking model obtained the best performance, with an R |
| Starting Page | 8310 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12168310 |
| Journal | Applied Sciences |
| Issue Number | 16 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-19 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Hardware and Architecturee Email Marketing Email Frequency Email Optimization Customer Segmentation Machine Learning Ensemble Learning Deep Learning |
| Content Type | Text |
| Resource Type | Article |
