Abstract
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.
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References
Anderson CK, Wilson JG (2003) Wait or buy? The strategic consumer: Pricing and profit implications. Journal of the Operational Research Society 54(2):299–306
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
Belobaba P (1989) Application of a probabilistic decision model to airline seat inventory control. Operations Research 37:183–197
Besanko D, Winston W (1990) Optimal price skimming by a monopolist facing rational consumers. Management Science 36(5):555–567
Bitran G, Caldentey R (2003) An overview of pricing models for revenue management. Manufacturing & Service Operations Management 5(2):203–229
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, Springer
Brumelle SL, McGill JI (1993) Airline seat allocation with multiple nested classes. Operations Research 41:127–137
Choi S, Kimes SE (2002) Electronic distribution channel’s effect on hotel revenue management. Cornell Hotel and Restaurant Administration Quarterly 43(2):23–31
Chung K, Van Ness B, Van Ness R (1999) Limit orders and the bid-ask spread. Journal of Financial Economics 53:255–287
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): 126–148
Elmaghraby W, Lippman S, Tang CS, Yin R (2009) Will more purchasing options benefit customers? Production and Operations Management. Forthcoming
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, USA
Foucault T (1999) Order flow composition and trading costs in a dynamic limit order market. Journal of Financial Markets 2:99–134
Gallego G, van Ryzin G (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizon. Management Science 40:999–1020
Harris L, Hasbrouck J (1996) Market vs. limit orders: the SuperDOT evidence on order submission strategy. Journal of Financial and Quantitative Analysis 31:213–231
Ho T, Tang CS, Bell DR (1998) Rational shopping behavior and the option value of variable pricing. Management Science 44:145–160
Johnson E, Moe W, Fader P, Bellman S, Lohse G (2004) On the depth and dynamics of online search behavior. Management Science 50(2):299–308
Kimes SE (1989) Yield management: a tool for capacity-constrained service firm. Journal of Operations Management 8:348–363
Kincaid WM, Darling DA (1963) An inventory pricing problem. Journal of Mathematical Analysis and Applications 7(2):183–208
Knapp L April 9, (2003) Algorithms key to cheap air fare. Wired News
Liddle A (2003) Using web for discounting clicks with digital diners. Nation’s Restaurant News 37(20):172
Littlewood K (1972) Forecasting and control of passengers. 12th AGIFORS Symposium Proceedings. Nathanya, Israel 95–128
Liu Q, van Ryzin G (2005) Strategic capacity rationing to induce early purchases. Manufacturing & Service Operations Management 8(1): 110–115
McGill J, van Ryzin G (1999) Revenue management: Research overview and prospects. Transportation Science 33:233–256
Montgomery A, Hosanagar K, Krishnan R, Clay K (2004) Designing a better shopbot. Management Science 50(2):189–206
Netessine S, Shumsky RA (2005) Revenue management games: Horizontal and vertical competition. Management Science 51(5):813–831
Robinson LW (1995) Optimal and approximate control policies for airline booking with sequential nonmonotonic fare classes. Operations Research 43:252–263
Su X (2007) Inter-temporal pricing with strategic customer behavior. Management Science 53(5): 726–741
Talluri K, van Ryzin G (2004) The theory and practice of revenue management. Kluwer Academic Publishers, Dodrecht
Zhao W, Zheng Y (2000) Optimal dynamic pricing for perishable assets with nonhomogeneous demand. Management Science 46:375–388
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Cho, M., Fan, M., Zhou, YP. (2009). Strategic Consumer Response to Dynamic Pricing of Perishable Products. In: Tang, C., Netessine, S. (eds) Consumer-Driven Demand and Operations Management Models. International Series in Operations Research & Management Science, vol 131. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-98026-3_17
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DOI: https://doi.org/10.1007/978-0-387-98026-3_17
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