Adaptive Pricing Strategies in Digital Marketing (Statistics, Optimization & Information Computing, 2025)
Dynamic pricing in digital marketing plays a crucial role in enabling businesses to adapt to the ever-changing market conditions and meet customer demands effectively. This paper presents an improved methodology for leveraging machine learning, specifically the Deep Q-Network (DQN) model, to optimize dynamic pricing decisions in the digital marketing domain. The DQN model architecture incorporates deep neural networks and reinforcement learning algorithms to learn and optimize pricing decisions. The model is trained using hyperparameters optimized through experimentation. The results demonstrate the superiority of the DQN model over a baseline strategy, with significant improvements in revenue, profit, conversion rate, customer lifetime value, market share, and price elasticity. The findings highlight the potential of machine learning in enhancing e-marketing strategies, allowing businesses to adapt pricing decisions in real-time based on customer behavior and market dynamics. This research contributes to the growing body of knowledge on dynamic pricing and provides valuable insights for businesses seeking to leverage advanced analytics in digital marketing.