Enhancing Third-Party Product Marketing with Reinforcement Learning Techniques

Third-party product marketing is one of the most challenging and complex areas in today’s very competitive market calling for effectiveness in areas such as personalization, real-time decision making and optimal resource utilization. Many standard approaches to market promotion not work well when it comes to meeting these needs because of their rigidity and inability to be easily expanded on as necessary. This led to Reinforcement Learning (RL) arising as a promising solution for enhancing customer value, dynamic price management, and advanced online campaign optimization. This paper identifies these challenges and seeks to use RL, namely Deep Q-Learning, to solve them while proposing a data-driven marketing optimization framework. While the conventional approaches to model involve the deployment of rule-based or heuristic models, the proposed framework self-adapts from customer behavior, market conditions and campaign feedbacks to make real-time, customer-specific marketing decisions. This study is unique that it applies RL in third party product promotion which is attempt to apply many of these theoretical RL developments in digital marketing practice. This framework is divided into stages, that include data preprocessing, define the RL environment, training and testing the model. Customer characteristics and market situation are considered as states of the RL environment. Our findings reveal raised conversion rate by 20%, higher revival rate 27% of customers compared to conventional model. As a result, the outcome of this study indicates the effectiveness of RL in enhancing the flexibility, scale-up, and accuracy of marketing optimization. Thus, this research is useful in the wider study of RL for marketing as the solutions here provide a strong foundation for the development of such intelligent and adaptive tools for marketing professionals to gain a competitive edge.