Optimization of Email Marketing Campaigns Leveraging Machine Learning Techniques

Optimization of Email Marketing Campaigns Leveraging Machine Learning Techniques

Nejla Zukorlić

Email marketing is widely recognized as an effective digital marketing channel, offering a considerable return on investment (ROI). One key challenge is determining the optimal day and time to send emails to maximize customer response rates. This thesis explores the application of machine learning (ML) algorithms to predict the best send times for email marketing campaigns, focusing on improving response rates. The research utilizes historical email marketing data, including customer demographics, response behavior, and email send dates. Based on this data, various machine learning models, including decision trees and random forests, as well as ensemble methods at the end, will be used to predict the optimal day for sending emails. The study will also examine how factors like customer age and tenure influence response rates at different times. The question is if the machine learning-based predictions of the optimal send day and time will significantly improve response rates compared to traditional methods. Also, incorporating demographic factors, such as age and tenure, hopefully will improve the accuracy of these predictions. The expected outcome is that MLbased optimization will outperform traditional scheduling methods, providing a more effective and data-driven strategy for email campaign timing.

Email Marketing, Machine Learning, Send-Time Optimization, Customer Engagement, Response Rates, Data-Driven Marketing