Personalized Dynamic Pricing of Limited Inventories

被引:43
|
作者
Aydin, Goker [1 ]
Ziya, Serhan [2 ]
机构
[1] Univ Michigan, Dept Ind & Operat Engn, Ann Arbor, MI 48109 USA
[2] Univ N Carolina, Dept Stat & Operat Res, Chapel Hill, NC 27559 USA
关键词
REVENUE MANAGEMENT; SEASONAL PRODUCTS; STRATEGIES; DEMAND;
D O I
10.1287/opre.1090.0701
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Prior work has investigated time-and inventory-level-dependent pricing of limited inventories with finite selling horizons. We consider a third dimension-in addition to time and inventory level-that the firms can use in setting their prices: the information that the firm has at the individual customer level. An arriving customer provides a signal to the firm, which is an imperfect indicator of the customer's willingness to pay, and the firm makes a personalized price offer depending on the signal, inventory level, and time. We consider two different models: full personalization and partial personalization. In the full personalization model, the firm charges any price it wishes given the customer signal, while in the partial personalization model, the firm can charge one of two prices. We find that a mere correlation between the signals and customers' willingness to pay is not sufficient to ensure intuitive relationships between the signal and the optimal prices. We determine a stronger condition, which leads to several structural properties, including the monotonicity of the optimal price with respect to the signal in the full personalization model. For the partial personalization model, we show that the optimal pricing policy is of threshold-type and that the threshold is monotonic in the inventory level and time.
引用
收藏
页码:1523 / 1531
页数:9
相关论文
共 50 条
  • [21] OPTIMAL DYNAMIC PRICING OF INVENTORIES WITH STOCHASTIC DEMAND OVER FINITE HORIZONS
    GALLEGO, G
    VANRYZIN, G
    MANAGEMENT SCIENCE, 1994, 40 (08) : 999 - 1020
  • [22] Dynamic Assortment with Limited Inventories and Set-Dependent Revenue Functions
    Etesami, S. Rasoul
    2020 59TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2020, : 3567 - 3572
  • [23] Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
    Chen, Xi
    Simchi-Levi, David
    Wang, Yining
    MANAGEMENT SCIENCE, 2022, 68 (07) : 4878 - 4898
  • [24] Galactic Air Improves Ancillary Revenues with Dynamic Personalized Pricing
    Kolbeinsson, Arinbjorn
    Shukla, Naman
    Gupta, Akhil
    Marla, Lavanya
    Yellepeddi, Kartik
    INFORMS JOURNAL ON APPLIED ANALYTICS, 2022, 52 (03): : 233 - 249
  • [25] Personalized dynamic pricing policy for electric vehicles: Reinforcement learning approach
    Bae, Sangjun
    Kulcsar, Balazs
    Gros, Sebastien
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2024, 161
  • [26] Dynamic Pricing with Unknown Nonparametric Demand and Limited Price Changes
    Perakis, Georgia
    Singhvi, Divya
    OPERATIONS RESEARCH, 2024, 72 (06) : 2726 - 2744
  • [27] Are airline passengers ready for personalized dynamic pricing? A study of German consumers
    Krämer A.
    Friesen M.
    Shelton T.
    Journal of Revenue and Pricing Management, 2018, 17 (2) : 115 - 120
  • [28] Aromatics - Low inventories lift pricing
    McElligott, S
    CHEMICAL WEEK, 2000, 162 (11) : 34 - 34
  • [29] Bundle pricing of inventories with stochastic demand
    Bulut, Zumbul
    Gurler, Ulku
    Sen, Alper
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2009, 197 (03) : 897 - 911
  • [30] The Value of Personalized Pricing
    Elmachtoub, Adam N.
    Gupta, Vishal
    Hamilton, Michael L.
    WEB AND INTERNET ECONOMICS, WINE 2019, 2019, 11920 : 338 - 338