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Dynamic pricing with Bayesian demand learning and reference price effect

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  • Cao, Ping
  • Zhao, Nenggui
  • Wu, Jie

Abstract

In this paper, we consider a seller selling a single product over a finite horizon, with the objective of maximizing the expected total discounted revenue by dynamically adjusting posted prices. One distinct feature of our problem is that the customers’ arrival rate is unknown to the seller and will be learned in a Bayesian method. Moreover, arriving customer’s purchase behavior is affected by reference price. We formulate this problem as a Bayesian dynamic programming. First, we analyze the structural properties of the optimal revenue function and the optimal pricing policy. We find that the problem can be substantially simplified in the case of sufficient inventory and demand learning can be decoupled from pricing decision. Then, we investigate the value of market size (customers’ arrival rate) and the effect of the reference price. Furthermore, we conduct several numerical examples to justify our theoretical results, examine the influence of demand learning, and find that ignoring the reference price effect will lose substantial revenue.

Suggested Citation

  • Cao, Ping & Zhao, Nenggui & Wu, Jie, 2019. "Dynamic pricing with Bayesian demand learning and reference price effect," European Journal of Operational Research, Elsevier, vol. 279(2), pages 540-556.
    • Handle: RePEc:eee:ejores:v:279:y:2019:i:2:p:540-556
    DOI: 10.1016/j.ejor.2019.06.033
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    References listed on IDEAS

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    7. Colombo, Luca & Labrecciosa, Paola, 2021. "Dynamic oligopoly pricing with reference-price effects," European Journal of Operational Research, Elsevier, vol. 288(3), pages 1006-1016.
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