@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},
	}