Optimizing Discount Allocation with Deep Learning in Competitive Markets

In today’s highly competitive markets, discount strategies play a pivotal role in customer acquisition and retention. Traditional discount allocation methods, however, often fail to account for real-time changes in consumer behavior and competitor pricing. This paper proposes a deep learning-based framework to optimize discount allocation across customer segments, leveraging historical sales data and competitor activity to dynamically tailor promotions. Experimental evaluations on synthetic retail datasets show that the proposed model significantly improves conversion rates and overall profitability compared to rule-based benchmarks. This study demonstrates the potential of intelligent pricing systems to deliver personalized value while maintaining market competitiveness.