面向策略性消费者的动态定价及应对机制研究
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摘要
动态定价是收益管理的重要引擎之一,传统应用领域是航空公司、宾馆、汽车租赁和旅游线路等服务性行业,近年来已广泛应用于零售业、体育比赛、娱乐事件管理、房地产、交通运输和制造业等行业。动态定价是指产品的价格随着时间、地点或消费者的不同,而不断改变的一种定价方式。时间上的价格差别是指产品的价格根据不同时刻供应和需求的状态,不断随着时间改变。
随着动态定价的普遍应用与信息技术的高速发展,越来越多消费者的购买行为开始具有策略性,这些策略性消费者通过信息技术获得厂商的价格和库存等信息,对厂商未来的价格走势形成理性预期,由此来选择有利于自己的购买时机。即使当前的市场价格低于消费者对产品的价值评价,策略性消费者仍然可能等待到低价时才购买,以获取更大的消费者剩余,因此由于消费者策略行为的存在,动态定价的理论研究和实际应用面临很大的挑战。
策略性消费者对厂商动态定价决策以及收益的影响已不可忽视。因此如何在决策模型中考虑消费者策略行为,以及如何引入应对机制来缓解策略性消费者的影响,去引导消费者在高价时提前购买,进而提高厂商的收益,这无论对于理论研究还是实际应用,都是十分必要的。
本文在对当前的动态定价和消费者策略行为理论研究进展加以综述的基础上,运用博弈论与动态优化的理论和方法,从给定产能的服务提供商动态定价和未给定库存的零售商动态定价这两个方面出发,通过引入可召回、差价补偿和快速补货等应对机制,对面向策略性消费者的动态定价及其应对机制问题进行了较深入的研究。
首先研究了退票机制下考虑策略性消费者的服务提供商动态定价问题。建立了一个基于消费者价值评价不确定的两周期动态定价模型,即在临近服务交付期时,消费者的价值评价得以实现,低端消费者会提出退票的要求。对比了是否实行退票机制时服务提供商的期望收益,研究结果表明,当提前销售期的销售量足够大,即使现场销售期高端消费者的比例较低,为消费者退票给予返款并再销售可以提高服务提供商的收益。
接下来研究了可召回机制下面向策略性消费者的航空公司动态定价问题,建立了传统动态定价模型与可召回机制下的动态定价模型。针对面向策略性消费者的航空公司动态定价问题,引入可召回票的销售机制,构造了一个考虑策略性消费者的单航班两周期动态定价模型,求解了可召回机制下的最优定价决策,并利用算例试验分析了可召回机制的有效性。研究发现,通过可召回机制缓解策略性消费者的影响,可以提高航空公司的收益。
然后研究了差价补偿机制下,考虑消费者策略行为的季节性产品季末出清动态定价问题。假设市场中同时存在策略性和短视性消费者,分析了零售商考虑策略性消费者的动态定价,以及差价补偿机制对零售商定价和收益的影响。研究表明策略性消费者延迟购买影响了零售商的定价决策并使得收益减少,而差价补偿机制可以消除策略性消费者的延迟购买行为,提高零售商的收益。
最后研究了快速补货机制下基于消费者策略行为的易逝品定价及订购量的决策问题。运用博弈论和消费者行为理论,研究了策略性消费者的等待行为对零售商决策和利润的影响,并引入快速补货机制来应对消费者策略行为。研究表明:当快速补货成本满足一定条件时,零售商可以实行快速补货减少期初订货量来缓解消费者策略行为的影响,并提高销售价格和期望利润。在本部分的扩展研究中,还基于高科技易逝品的再制造系统定价问题,将传统的快速补货模型扩展到再制造柔性补货模型。
Inter-temporal pricing is one important engine in revenue management, and its traditional application areas are airlines, hotels, car rental, travel routes and other service industries. In recent years, it has been widely used in retail, sports, entertainment event management, real estate, transportation and manufacturing industries. Inter-temporal pricing refers to a pricing model which changes prices over time, place or different consumers. Different pricing in time refers to prices of product change continuously according to the state of supply and demand over time.
With general inter-temporal pricing and development of information technology, more and more consumers’buying behavior becomes strategic. These strategic consumers will use information technology to obtain pricing and inventory information of firms, form rational expectations about the future price trend of firms and time their favorable purchase. Even if the current market price is lower than the product evaluation of the consumer, strategic consumers are still will wait for a low price to purchase to obtain greater consumer surplus. Therefore, Inter-temporal pricing in theoretical research and practical applications are faced with great challenge due to the existence of consumer strategic behavior.
The effects of strategic consumers on the inter-temporal pricing decision and income can not be ignored. Therefore, how to take consumer strategic behavior into account in decision-making model,to introduce operational mechanisms to mitigate the effects,and to induce consumers to purchase early, for improve the profit of firms, is very necessary for theoretical research and practical applications.
This thesis uses game theory and dynamic optimization theory, studies thoroughly the inter-temporal pricing and operational mechanisms in the presence of strategic consumers. We studies the topic through introducing callable mechanism,price-match,and quick response,from the aspects of service provider with given capacity and retailer without given capacity,based on the reviews of current researches of inter-temporal pricing and consumer behavior theory.
Fitst, we investigate the inter-temporal pricing with cancellation when the strategic customer’s value is uncertain. We establish a two-period inter-temporal pricing model,in which the consumers with low valuation will cancel the service before the delivery time of the service. The results show that, value uncertain and cancellation affect the optimal inter-temporal pricing strategy, and when there are enough realized sales in advance period and high-end customers in spot period, service provider can improve profits from refund for cancellation and resale.
Next, we study the problem of airlines’inter-temporal pricing in the presence of strategic consumers; this chapter employs a new callable mechanism. We construct a single-leg, two-period inter-temporal pricing model with strategic consumer behavior and solve the optimal pricing strategy under callable mechanism. We show that the callable mechanism can mitigate the affect of strategic consumers and improve the revenue of airlines, and the numerical simulations are given to illustrate the effectiveness of the callable mechanism.
Then, we assume that there are strategic and myopic customers in the market with heterogeneous valuation, analysis the dynamic pricing model with strategic customers, and the impact of price match policy. We find that the delayed buying of strategic customers affect the retailer's pricing strategy and make profit decreased. The price match policy can be a mechanism to eliminate the delay in the purchase of strategic customers, and improve the retailer's earnings.
At last, we study the pricing and order quantity decisions of perishable product in the presence of strategic consumers. This chapter uses game theory and consumer behavior theory, and studies the waiting behavior of the strategic consumers how to affect the retailer’s decisions and expected profit. Then we use a mean of quick response to mitigate the impact of strategic consumer behavior. The study result shows that, when the cost of quick response belongs to a relatively mild range, the retailer can reduce the original order quantity through quick response to mitigate the impacts, take higher price and earn more expected profit. The expanded research investigates the remanufacturing flexible replenish based on pricing of the remanufacture of high tech perishable goods。
随着动态定价的普遍应用与信息技术的高速发展,越来越多消费者的购买行为开始具有策略性,这些策略性消费者通过信息技术获得厂商的价格和库存等信息,对厂商未来的价格走势形成理性预期,由此来选择有利于自己的购买时机。即使当前的市场价格低于消费者对产品的价值评价,策略性消费者仍然可能等待到低价时才购买,以获取更大的消费者剩余,因此由于消费者策略行为的存在,动态定价的理论研究和实际应用面临很大的挑战。
策略性消费者对厂商动态定价决策以及收益的影响已不可忽视。因此如何在决策模型中考虑消费者策略行为,以及如何引入应对机制来缓解策略性消费者的影响,去引导消费者在高价时提前购买,进而提高厂商的收益,这无论对于理论研究还是实际应用,都是十分必要的。
本文在对当前的动态定价和消费者策略行为理论研究进展加以综述的基础上,运用博弈论与动态优化的理论和方法,从给定产能的服务提供商动态定价和未给定库存的零售商动态定价这两个方面出发,通过引入可召回、差价补偿和快速补货等应对机制,对面向策略性消费者的动态定价及其应对机制问题进行了较深入的研究。
首先研究了退票机制下考虑策略性消费者的服务提供商动态定价问题。建立了一个基于消费者价值评价不确定的两周期动态定价模型,即在临近服务交付期时,消费者的价值评价得以实现,低端消费者会提出退票的要求。对比了是否实行退票机制时服务提供商的期望收益,研究结果表明,当提前销售期的销售量足够大,即使现场销售期高端消费者的比例较低,为消费者退票给予返款并再销售可以提高服务提供商的收益。
接下来研究了可召回机制下面向策略性消费者的航空公司动态定价问题,建立了传统动态定价模型与可召回机制下的动态定价模型。针对面向策略性消费者的航空公司动态定价问题,引入可召回票的销售机制,构造了一个考虑策略性消费者的单航班两周期动态定价模型,求解了可召回机制下的最优定价决策,并利用算例试验分析了可召回机制的有效性。研究发现,通过可召回机制缓解策略性消费者的影响,可以提高航空公司的收益。
然后研究了差价补偿机制下,考虑消费者策略行为的季节性产品季末出清动态定价问题。假设市场中同时存在策略性和短视性消费者,分析了零售商考虑策略性消费者的动态定价,以及差价补偿机制对零售商定价和收益的影响。研究表明策略性消费者延迟购买影响了零售商的定价决策并使得收益减少,而差价补偿机制可以消除策略性消费者的延迟购买行为,提高零售商的收益。
最后研究了快速补货机制下基于消费者策略行为的易逝品定价及订购量的决策问题。运用博弈论和消费者行为理论,研究了策略性消费者的等待行为对零售商决策和利润的影响,并引入快速补货机制来应对消费者策略行为。研究表明:当快速补货成本满足一定条件时,零售商可以实行快速补货减少期初订货量来缓解消费者策略行为的影响,并提高销售价格和期望利润。在本部分的扩展研究中,还基于高科技易逝品的再制造系统定价问题,将传统的快速补货模型扩展到再制造柔性补货模型。
Inter-temporal pricing is one important engine in revenue management, and its traditional application areas are airlines, hotels, car rental, travel routes and other service industries. In recent years, it has been widely used in retail, sports, entertainment event management, real estate, transportation and manufacturing industries. Inter-temporal pricing refers to a pricing model which changes prices over time, place or different consumers. Different pricing in time refers to prices of product change continuously according to the state of supply and demand over time.
With general inter-temporal pricing and development of information technology, more and more consumers’buying behavior becomes strategic. These strategic consumers will use information technology to obtain pricing and inventory information of firms, form rational expectations about the future price trend of firms and time their favorable purchase. Even if the current market price is lower than the product evaluation of the consumer, strategic consumers are still will wait for a low price to purchase to obtain greater consumer surplus. Therefore, Inter-temporal pricing in theoretical research and practical applications are faced with great challenge due to the existence of consumer strategic behavior.
The effects of strategic consumers on the inter-temporal pricing decision and income can not be ignored. Therefore, how to take consumer strategic behavior into account in decision-making model,to introduce operational mechanisms to mitigate the effects,and to induce consumers to purchase early, for improve the profit of firms, is very necessary for theoretical research and practical applications.
This thesis uses game theory and dynamic optimization theory, studies thoroughly the inter-temporal pricing and operational mechanisms in the presence of strategic consumers. We studies the topic through introducing callable mechanism,price-match,and quick response,from the aspects of service provider with given capacity and retailer without given capacity,based on the reviews of current researches of inter-temporal pricing and consumer behavior theory.
Fitst, we investigate the inter-temporal pricing with cancellation when the strategic customer’s value is uncertain. We establish a two-period inter-temporal pricing model,in which the consumers with low valuation will cancel the service before the delivery time of the service. The results show that, value uncertain and cancellation affect the optimal inter-temporal pricing strategy, and when there are enough realized sales in advance period and high-end customers in spot period, service provider can improve profits from refund for cancellation and resale.
Next, we study the problem of airlines’inter-temporal pricing in the presence of strategic consumers; this chapter employs a new callable mechanism. We construct a single-leg, two-period inter-temporal pricing model with strategic consumer behavior and solve the optimal pricing strategy under callable mechanism. We show that the callable mechanism can mitigate the affect of strategic consumers and improve the revenue of airlines, and the numerical simulations are given to illustrate the effectiveness of the callable mechanism.
Then, we assume that there are strategic and myopic customers in the market with heterogeneous valuation, analysis the dynamic pricing model with strategic customers, and the impact of price match policy. We find that the delayed buying of strategic customers affect the retailer's pricing strategy and make profit decreased. The price match policy can be a mechanism to eliminate the delay in the purchase of strategic customers, and improve the retailer's earnings.
At last, we study the pricing and order quantity decisions of perishable product in the presence of strategic consumers. This chapter uses game theory and consumer behavior theory, and studies the waiting behavior of the strategic consumers how to affect the retailer’s decisions and expected profit. Then we use a mean of quick response to mitigate the impact of strategic consumer behavior. The study result shows that, when the cost of quick response belongs to a relatively mild range, the retailer can reduce the original order quantity through quick response to mitigate the impacts, take higher price and earn more expected profit. The expanded research investigates the remanufacturing flexible replenish based on pricing of the remanufacture of high tech perishable goods。
引文
②How to Get a Kindle on the Cheap,10 Things Not to Buy in 2010. www.SmartMoney.com http://www.smartmoney.com/spending/technology/how-to-get-a-kindle-on-the-cheap/#ixzz0g2Makyzy http://www.smartmoney.com/spending/budgeting/10-Things-Not-to-Buy-in-2010/
②Retailers try to train shoppers to buy now. www.usatoday.com. http://www.usatoday.com/money/industries/retail/2006-09-25-sales-shopping-usat_x.htm.
④诚久保障服务条款.国美电器. www.gome.com.cn. http://www.gome.com.cn/affiche-51301-218630.htm.
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Feng Y, Gallego G. 1995. Optimal starting times for end-of-season sales and optimal stopping times for promotional fares[J]. Management Science, 41(8): 1371-1391.
Feng Y, Xiao B. 1999. Maximizing revenues of perishable assets with a risk factor[J]. Operation Research, 47(2): 337-341.
Feng Y Y, Gallego C. 2000. Perishable asset revenue management with Markovian time dependent demand intensities[J]. Management Science, 46(7): 941-956.
Feng Y Y, Xiao B C. 2000a. A continuous-time yield management model with multiple prices and reversible price changes[J]. Management Science, 46(5): 644-657.
Feng Y Y, Xiao B C. 2000b. Optimal policies of yield management with multiple predetermined prices[J]. Operation Research, 48(2): 332-343.
Ferdows K, Lewis M A, Machuca J A D. 2004. Rapid-Fire Fulfillment: Zara's Secret for Fast Fashion[J]. Harvard Business Review, 82(11): 104-110.
Fisher M, Rajaram K, Raman A. 2001. Optimizing Inventory Replenishment of Retail Fashion Products[J]. Manufacturing & Service Operations Management, 3(3): 230-241.
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Gallego G, Kou S G, Phillips R. 2008. Revenue Management of Callable Products[J]. Management Science, 54(3): 550-564.
Gallego G, Van Ryzin G. 1994. Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons[J]. Management Science, 40(8): 999-1020.
Gallego G, Van Ryzin G. 1997. A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management[J]. Operation Research, 45(1): 24-41.
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Levin Y, Mcgill J, Nediak M. 2010. Optimal Dynamic Pricing of Perishable Items by a Monopolist Facing Strategic Consumers[J]. Production and Operations Management, 19(1): 40-60.
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Elmaghraby W, Keskinocak P. 2003. Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions[J]. Management Science, 49(10): 1287-1309.
Feng Y, Gallego G. 1995. Optimal starting times for end-of-season sales and optimal stopping times for promotional fares[J]. Management Science, 41(8): 1371-1391.
Feng Y, Xiao B. 1999. Maximizing revenues of perishable assets with a risk factor[J]. Operation Research, 47(2): 337-341.
Feng Y Y, Gallego C. 2000. Perishable asset revenue management with Markovian time dependent demand intensities[J]. Management Science, 46(7): 941-956.
Feng Y Y, Xiao B C. 2000a. A continuous-time yield management model with multiple prices and reversible price changes[J]. Management Science, 46(5): 644-657.
Feng Y Y, Xiao B C. 2000b. Optimal policies of yield management with multiple predetermined prices[J]. Operation Research, 48(2): 332-343.
Ferdows K, Lewis M A, Machuca J A D. 2004. Rapid-Fire Fulfillment: Zara's Secret for Fast Fashion[J]. Harvard Business Review, 82(11): 104-110.
Fisher M, Rajaram K, Raman A. 2001. Optimizing Inventory Replenishment of Retail Fashion Products[J]. Manufacturing & Service Operations Management, 3(3): 230-241.
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Gallego G, Kou S G, Phillips R. 2008. Revenue Management of Callable Products[J]. Management Science, 54(3): 550-564.
Gallego G, Van Ryzin G. 1994. Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons[J]. Management Science, 40(8): 999-1020.
Gallego G, Van Ryzin G. 1997. A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management[J]. Operation Research, 45(1): 24-41.
Gary L.L, Ed. 1995. Optical Methods of Engineering Analysis[M]. Cambridge/New York, Cambridge University Press.
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Heching A, Leung Y T. 2005. Product Pricing in the e-Business Era. Supply Chain Management on Demand[M], Springer Berlin Heidelberg: 65-96.
Hurlbut T. 2005. The Markdown Blues [EB/OL], http://www.inc.com/articles/2005/01/markdowns.html. Jerath K, Netessine S, Veeraraghavan S K. 2010. Revenue Management with Strategic Customers: Last-Minute Selling and Opaque Selling[J]. Management Science, published online before print February 12, 2010, DOI: 10.1287/mnsc.1090.1125.
Kincaid W M D, Darling D A. 1963. An inventory pricing problem[J]. Journal of Mathematical Analysis and Application, 7: 183-208.
Lai G, Debo L G, Sycara K. 2009. Buy Now and Match Later: Impact of Posterior Price Matching on Profit with Strategic Consumers[J]. Manufacturing & Service Operations Management: msom.1080.0248.
Lai G, Sycara K, Debo L, et al. 2006. An analysis on price matching policy[A]. The 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet[C]. Fredericton, Canada,ACM.
Levin Y, Mcgill J, Nediak M. 2007. Price Guarantees in Dynamic Pricing and RevenueManagement[J]. Operation Research, 55(1): 75-97.
Levin Y, Mcgill J, Nediak M. 2009. Dynamic Pricing in the Presence of Strategic Consumers and Oligopolistic Competition[J]. Management Science, 55(1): 32-46.
Levin Y, Mcgill J, Nediak M. 2010. Optimal Dynamic Pricing of Perishable Items by a Monopolist Facing Strategic Consumers[J]. Production and Operations Management, 19(1): 40-60.
Liu Q, Van Ryzin G J. 2008. Strategic Capacity Rationing to Induce Early Purchases[J]. Management Science, 54(6): 1115-1131.
Liu X, Tang O and Huang F P. 2008. Dynamic pricing and ordering decision for the perishable food of the supermarket using RFID technology[J]. Asia Pacific Journal of Marketing and Logistics, 20(1): 7-22.
Mcwilliams G. 2004. Minding the Store: Analyzing Customers, Best Buy Decides not All Are Welcome[J]. Wall Street Journal. 2004-11-08 (A1).
Merrick A. 2001. Priced to move: Retailers attempt to get a leg up on markdowns with new software[J]. Wall Street Journal. 2001-8-7 (A1).
Narasimhan C. 1989. Incorporating Consumer Price Expectations in Diffusion Models[J]. Marketing Science, 8(4): 343-357.
Nocke V, Peitz M. 2007. A theory of clearance sales[J]. Economic Journal, 117(522): 964-990.
O'donnell J. 2006. Retailers try to train shoppers to buy now[J]. USA Today: September 25.
Ovchinnikov A, Milner J M. 2005. Strategic Response to Wait-or-Buy: Revenue Management through Last Minute Deals in the Presence of Customer Learning[J]. University of Toronto working paper.
Peng Z-Q, Xiong Y. 2008a. Pricing strategy of service provider under buyer-driven pricing model[A]. International Conference on Engineering, Services and Knowledge management[C].DaLian, China,IEEE.
Peng Z-Q, Xiong Y. 2008b. Value uncertain in advance selling: the impact of offering refunds for cancellations[A].International Conference on Service Operations and Logistics, and Informatics[C]. Beijing, China,IEEE.
Ringbom S. Shy O. 2008. Refunds and collusion in service industries[J]. Journal of Economics and Business, 60(6): 502-516.
Rozhon T. 2004. Worried merchants throw discounts at shoppers[J], The New York Times. Shen Z-J M, Su X. 2007. Customer Behavior Modeling in Revenue Management and Auctions: A Review and New Research Opportunities [J]. Production and Operations Management, 16(6): 713-728.
Shugan S M, Xie J. 2000. Advance pricing of services and other implications of separatingpurchases and consumption[J]. Journal of Service Research, 2(3): 227–239.
Shugan S M, Xie J. 2004. Advance selling for services[J]. California Management Rev., 46(spring): 37–54.
Shugan S M, Xie J. 2005. Advance-selling as a competitive marketing tool[J]. Internat. J. Res. Marketing, 22: 351–373.
Silverstein M J, John B. 2006. Treasure Hunt: Inside the Mind of the New Consumer[M], Portfolio.
Smith S A, Achabal D D. 1998. Clearance Pricing and Inventory Policies for Retail Chains[J]. Management Science, 44(3): 285-300.
Stokey N L. 1979. Intertemporal Price Discrimination[J]. The Quarterly Journal of Economics, 93(3): 355-371.
Stokey N L. 1981. Rational Expectations and Durable Goods Pricing[J]. The Bell Journal of Economics, 12(1): 112-128.
Su X. 2007. Intertemporal Pricing with Strategic Customer Behavior[J]. Management Science, 53(5): 726-741.
Su X. 2009. Consumer Returns Policies and Supply Chain Performance[J]. Manufacturing & Service Operations Management, Forthcoming.
Su X. 2009. A Model of Consumer Inertia with Applications to Dynamic Pricing[J]. Production & Operations Management, Forthcoming.
Su X. 2009. Optimal Pricing with Speculators and Strategic Consumers[J]. Management Science, mnsc.1090.1075.
Su X, Zhang F. 2008. Strategic Customer Behavior, Commitment, and Supply Chain Performance[J]. Management Science, 54(10): 1759-1773.
Talluri K T, Ryzin G J V. 2005. The Theory and Practice of Revenue Management[M]. New York, Springer.
Vincent M. 2008. Disclosing Inventory to Mitigate the Impact of Strategic Waiting on Profits[J]. SSRN Working Paper.
Wilson J G, Anderson C K, Sang-Won K. 2006. Optimal booking limits in the presence of strategic consumer behavior [J]. International Transactions in Operational Research, 13(2): 99-110.
Xie J, Gerstner E. 2007. Service Escape: Profiting from Customer Cancellations[J]. Marketing Science, 26(1): 18–30.
Xie J, Shugan S M. 2001. Electronic Tickets, Smart Cards, and Online Prepayments: When and How to Advance Sell[J]. Marketing Science, 20(3): 219-243.
Xie J, Shugan S M. 2008. Advance Selling Theory[M]. Northampton, Edward Elgar Publishing.
Y Narahari C V L R, Ravikumar K, Sourabh S. 2005, Dynamic pricing models for electronicbusiness[J]. Sādhanā, 30(Part 2 & 3, April/June): 231-256.
Yin R, Aviv Y, Pazgal A, et al.. 2009. Optimal Markdown Pricing: Implications of Inventory Display Formats in the Presence of Strategic Customers[J]. Management Science, 55(8): 1391-1408.
You P-S. 2003. Dynamic pricing of inventory with cancellation Demand[J]. Journal of the Operational Research Society, 54: 1093–1101.
You P-S, Chen T C. 2007. Dynamic pricing of seasonal goods with spot and forward purchase demands[J]. Computers and Mathematics with Applications, 54: 490–498.
Zhang D, Cooper W L. 2008. Managing clearance sales in the presence of strategic customers[J]. Production and Operations Management, 17(4): 416-431.
Zhao W, Zheng Y-S. 2000. Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous Demand[J]. Management Science, 46(3): 375-388.
Zhou Y, Fan M, Cho M. 2006. On the threshold purchasing behavior of customers facing dynamically priced perishable products[C]. Presentation at Informs Pittsburgh National Meeting.