Monopoly pricing with limited demand information

被引:29
作者
Eren S.S. [1 ]
Maglaras C. [2 ]
机构
[1] Barclays Capital Inc, New York, NY 10166
[2] Graduate School of Business, Columbia University, Division of Decision, Risk and Operations
关键词
Competitive analysis; Regret; Revenue mangement; Robust pricing;
D O I
10.1057/rpm.2009.41
中图分类号
学科分类号
摘要
Traditional monopoly pricing models assume that firms have full information about the market demand and consumer preferences. In this article, we study a prototypical monopoly pricing problem for a seller with limited market information and different levels of demand learning capability under relative performance criterion of the competitive ratio (CR). We provide closed-form solutions for the optimal pricing policies for each case and highlight several important structural insights. We note the following: (1) From the firm's viewpoint the worst-case operating conditions are when it faces a homogeneous market where all customers value the product equally, but where the specific valuation is unknown. In cases with partial demand information, the worse case cumulative willingness-to-pay distribution becomes piecewise-uniform as opposed to a point mass. (2) Dynamic (skimming) pricing arises naturally as a hedging mechanism for the firm against the two principal risks that it faces: first, the risk of foregoing revenue from pricing too low, and second, the risk of foregoing sales from pricing too high. And, (3) even limited learning, for example market information at a few price points, leads to significant performance gains. © 2010 Macmillan Publishers Ltd.
引用
收藏
页码:23 / 48
页数:25
相关论文
共 28 条
[1]  
Araman V.F., Caldentey R.A., Dynamic pricing for non-perishable products with demand learning, Working Paper, (2005)
[2]  
Aviv Y., Pazgal A., Pricing of short life-cycle products through active learning, Working Paper, (2005)
[3]  
Ball M.O., Queyranne M., Toward robust revenue management: Competitive analysis of online booking, Operations Research, 57, 4, pp. 950-963, (2009)
[4]  
Ben-Tal A., Nemirovski A., Robust convex optimization, Mathematics of Operations Research, 23, 4, pp. 769-805, (1998)
[5]  
Bergemann D., Schlag K., Pricing without priors, Journal of the European Economic Association, 6, 2-3, pp. 560-569, (2008)
[6]  
Bertsimas D., Popescu I., Optimal inequalities in probability theory: A convex optimization approach, SIAM Journal of Optimization, 15, 3, pp. 780-804, (2005)
[7]  
Bertsimas D., Sim M., The price of robustness, Operations Research, 52, pp. 35-53, (2003)
[8]  
Bertsimas D., Thiele A., A robust optimization approach to supply chain management, Proceedings of the Springer Lecture Notes An Computer Science, 3064, pp. 86-100, (2004)
[9]  
Besbes O., Zeevi A., Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms, Operations Research
[10]  
El Ghaoui L., Lebret H., Robust solutions to leastsquares problems with uncertain data, SIAM Journal on Matrix Analysis and Applications, 18, 4, pp. 1035-1064, (1997)