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This paper proposes a behavioral pricing model that enhances traditional pricing algorithms by incorporating concepts from mathematical psychology and information theory on how consumers perceive discounts. We propose a framework that systematically incorporates the effect of quoted discounts and historical promotions on consumers' valuations and helps marketers determine the optimal discount strategy. We apply our framework on a publicly available data set from an online retailer. The data set consists of transactional and customer data. Our experiments reveal that the behavioral pricing model can lead to very different pricing decisions compared to the traditional pricing model. For some product groups, we observe that the behavioral model suggests offering lower discounts than the traditional pricing model to capture the thrill and surprise of a deal without significantly sacrificing the profit margin. On the other hand, for certain product groups, while the traditional pricing model recommends not giving discounts at all, the behavioral pricing model suggests offering a smaller discount to entice customers to make purchases. [ABSTRACT FROM AUTHOR]