Designing Effective Mobile Push Notifications: Machine Learning Insights into User Engagement - Wicaksana, Dwi Yoga; Aisyah, Rheyna Jufri Sifa Zie; Qurrata A'yun, Dian Ulhaq (Journal of Digital Business and Innovation Management, 2025)
This study investigates the impact of mobile push notification design elements on user engagement, utilizing machine learning to derive actionable insights. By analyzing a dataset of over 700 million notifications from an e-commerce platform, the research evaluates the influence of three key features: the presence of emojis, deadlines, and subject line length. A logistic regression model was employed to identify the features most strongly associated with high engagement, defined as notifications receiving over 1,000 opens. The findings reveal that the inclusion of emojis significantly enhances engagement rates, achieving a 91% success rate compared to 20% for notifications without emojis. While deadlines slightly increased engagement, their effect was not statistically significant when examined in isolation. Subject line length demonstrated no consistent influence on engagement. However, a synergistic combination of emojis and deadlines yielded the highest engagement rates at 100%, emphasizing the value of integrating visual and psychological triggers.This study underscores the potential of machine learning in optimizing mobile push notification strategies, offering practical recommendations for businesses to refine their digital marketing efforts. These findings contribute to the growing body of literature on user engagement and provide a foundation for future exploration of notification design and effectiveness.