@article{(Open Science Index):https://publications.waset.org/pdf/10013019, title = {Personalized Email Marketing Strategy: A Reinforcement Learning Approach}, author = {Lei Zhang and Tingting Xu and Jun He and Zhenyu Yan and Roger Brooks}, country = {}, institution = {}, abstract = {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.}, journal = {International Journal of Economics and Management Engineering}, volume = {17}, number = {3}, year = {2023}, pages = {167 - 174}, ee = {https://publications.waset.org/pdf/10013019}, url = {https://publications.waset.org/vol/195}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 195, 2023}, }