Strategic Consumer Response to Dynamic Pricing of Perishable Products

  • Minho Cho
  • Ming Fan
  • Yong-Pin Zhou
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 131)


Dynamic pricing is a standard practice that sellers use for revenue management. With the vast availability of pricing and inventory data on the Internet, it is possible for consumers to become aware of the pricing strategies used by sellers and to develop strategic responses. In this chapter, we study the strategic response of consumers to dynamic prices for perishable products. As price fluctuates with the changes in time and inventory, a strategic consumer may choose to postpone a purchase in anticipation of lower prices in the future. We analyze a threshold purchasing policy for the strategic consumer, and conduct numerical studies to study its impact on both the strategic consumer’s benefits and the seller’s revenue. We find that in most cases the policy can benefit both the strategic consumer and the seller. In practice, the seller could encourage consumer waiting by adopting a target price purchasing system.


Arrival Rate Dynamic Price Price Volatility Limit Order Revenue Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Anderson CK, Wilson JG (2003) Wait or buy? The strategic consumer: Pricing and profit implications. Journal of the Operational Research Society 54(2):299–306CrossRefGoogle Scholar
  2. Aviv Y, Pazgal A (2008) Optimal pricing of seasonal products in the presence of forward-looking consumers. Manufacturing & Service Operations Management 10(3):339– 359 CrossRefGoogle Scholar
  3. Belobaba P (1989) Application of a probabilistic decision model to airline seat inventory control. Operations Research 37:183–197CrossRefGoogle Scholar
  4. Besanko D, Winston W (1990) Optimal price skimming by a monopolist facing rational consumers. Management Science 36(5):555–567CrossRefGoogle Scholar
  5. Bitran G, Caldentey R (2003) An overview of pricing models for revenue management. Manufacturing & Service Operations Management 5(2):203–229Google Scholar
  6. Biyalorgorsky E (2009) Shaping consumer demand through the use of contingent pricing, in Operations Management Models with Consumer-Driven Demand, ed. Serguei Netessine and Christopher S. Tang, SpringerGoogle Scholar
  7. Brumelle SL, McGill JI (1993) Airline seat allocation with multiple nested classes. Operations Research 41:127–137CrossRefGoogle Scholar
  8. Choi S, Kimes SE (2002) Electronic distribution channel’s effect on hotel revenue management. Cornell Hotel and Restaurant Administration Quarterly 43(2):23–31Google Scholar
  9. Chung K, Van Ness B, Van Ness R (1999) Limit orders and the bid-ask spread. Journal of Financial Economics 53:255–287CrossRefGoogle Scholar
  10. Elmaghraby W, Gulcu A, Keskinocak P (2008) Designing the optimal preannounced markdowns in the presence of rational consumers with multi-unit demands. Manufacturing & Service Operations Managements 10(3): 126–148Google Scholar
  11. Elmaghraby W, Lippman S, Tang CS, Yin R (2009) Will more purchasing options benefit customers? Production and Operations Management. ForthcomingGoogle Scholar
  12. Etzioni O, Knoblock C, Tuchinda R, Yates A (2003) To buy or not to buy: Mining airfare data to minimize ticket purchase price. SIGKDD’03, August 24–27, Washington, DC, USAGoogle Scholar
  13. Foucault T (1999) Order flow composition and trading costs in a dynamic limit order market. Journal of Financial Markets 2:99–134CrossRefGoogle Scholar
  14. Gallego G, van Ryzin G (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizon. Management Science 40:999–1020CrossRefGoogle Scholar
  15. Harris L, Hasbrouck J (1996) Market vs. limit orders: the SuperDOT evidence on order submission strategy. Journal of Financial and Quantitative Analysis 31:213–231CrossRefGoogle Scholar
  16. Ho T, Tang CS, Bell DR (1998) Rational shopping behavior and the option value of variable pricing. Management Science 44:145–160CrossRefGoogle Scholar
  17. Johnson E, Moe W, Fader P, Bellman S, Lohse G (2004) On the depth and dynamics of online search behavior. Management Science 50(2):299–308CrossRefGoogle Scholar
  18. Kimes SE (1989) Yield management: a tool for capacity-constrained service firm. Journal of Operations Management 8:348–363CrossRefGoogle Scholar
  19. Kincaid WM, Darling DA (1963) An inventory pricing problem. Journal of Mathematical Analysis and Applications 7(2):183–208CrossRefGoogle Scholar
  20. Knapp L April 9, (2003) Algorithms key to cheap air fare. Wired NewsGoogle Scholar
  21. Liddle A (2003) Using web for discounting clicks with digital diners. Nation’s Restaurant News 37(20):172Google Scholar
  22. Littlewood K (1972) Forecasting and control of passengers. 12th AGIFORS Symposium Proceedings. Nathanya, Israel 95–128Google Scholar
  23. Liu Q, van Ryzin G (2005) Strategic capacity rationing to induce early purchases. Manufacturing & Service Operations Management 8(1): 110–115Google Scholar
  24. McGill J, van Ryzin G (1999) Revenue management: Research overview and prospects. Transportation Science 33:233–256CrossRefGoogle Scholar
  25. Montgomery A, Hosanagar K, Krishnan R, Clay K (2004) Designing a better shopbot. Management Science 50(2):189–206CrossRefGoogle Scholar
  26. Netessine S, Shumsky RA (2005) Revenue management games: Horizontal and vertical competition. Management Science 51(5):813–831CrossRefGoogle Scholar
  27. Robinson LW (1995) Optimal and approximate control policies for airline booking with sequential nonmonotonic fare classes. Operations Research 43:252–263CrossRefGoogle Scholar
  28. Su X (2007) Inter-temporal pricing with strategic customer behavior. Management Science 53(5): 726–741CrossRefGoogle Scholar
  29. Talluri K, van Ryzin G (2004) The theory and practice of revenue management. Kluwer Academic Publishers, DodrechtGoogle Scholar
  30. Zhao W, Zheng Y (2000) Optimal dynamic pricing for perishable assets with nonhomogeneous demand. Management Science 46:375–388CrossRefGoogle Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Department of Information Systems and Operations ManagementMichael G. Foster School of Business, University of WashingtonSeattleUSA

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