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http://hdl.handle.net/1942/32319
Title: | Predicting email opens using machine learning techniques | Authors: | BYLOIS, Niels | Advisors: | NEVEN, Frank | Issue Date: | 2020 | Publisher: | tUL | Abstract: | In this thesis we use machine learning techniques to predict email campaign open rates based on the subject line. We tackle the following two specific problems: “Given the subject line, what will be the open rate of the email for a target audience?” and “Given a subject line, will a specific user open the email?”. Both problems are solved in the context of one company that has a small email campaign dataset combined with a large audience. For the first problem, we use regression models such as Ridge regression to predict the open rate of a single subject line for the target audience of the company. We analyze the effect of multiple pre-processors such as scales and Principal Component Analysis. However, due to the lack of email subject line data this model is outperformed by a simple statistical model that utilizes the individual user data. Next, for user-specific open predictions, there is no baseline or simple statistical model. Instead, we propose a method that uses classification models, such as logistic regression and a random forest classifier, to predict for each individual user if they will open an email with a specific subject line by combining the features from the user and an email. We will demonstrate that word embeddings have a significant influence on the machine learning model and that the model performance can improve by only using emails form a single language. We have devised a method of calculating the open rate of an email from the user-specific predictions. | Notes: | master in de informatica | Document URI: | http://hdl.handle.net/1942/32319 | Category: | T2 | Type: | Theses and Dissertations |
Appears in Collections: | Master theses |
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