%0 Journal Article %A Lei Zhang and Tingting Xu and Jun He and Zhenyu Yan and Roger Brooks %D 2023 %J International Journal of Economics and Management Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 195, 2023 %T Personalized Email Marketing Strategy: A Reinforcement Learning Approach %U https://publications.waset.org/pdf/10013019 %V 195 %X Email marketing is one of the most important segments of online marketing. Email content is vital to customers. Different customers may have different familiarity with a product, so a successful marketing strategy must personalize email content based on individual customers’ product affinity. In this study, we build our personalized email marketing strategy with three types of emails: nurture, promotion, and conversion. Each type of emails has a different influence on customers. We investigate this difference by analyzing customers’ open rates, click rates and opt-out rates. Feature importance from response models is also analyzed. The goal of the marketing strategy is to improve the click rate on conversion-type emails. To build the personalized strategy, we formulate the problem as a reinforcement learning problem and adopt a Q-learning algorithm with variations. The simulation results show that our model-based strategy outperforms the current marketer’s strategy. %P 167 - 174