Data-Driven Repricing Strategies in Competitive Markets

Boissier, Martin; Schlößer, Rainer; Podlesny, Nikolai J.; Serth, Sebastian; Bornstein, Marvin; Latt, Johanna; Lindemann, Jan; Selke, Jan; Uflacker, Matthias

doi:10.1145/3109859.3109979

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Cited by 8 publications

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“…Finally, several simulation platforms have been designed to assess the performance of pricing policies, see, e.g., DiMicco et al (2003) or Boissier et al (2017).…”

Section: Literaturementioning

confidence: 99%

Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference

Geer

Boer

Bayliss

et al. 2018

J Revenue Pricing Manag

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This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17 th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.

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“…Finally, several simulation platforms have been designed to assess the performance of pricing policies, see, e.g., DiMicco et al (2003) or Boissier et al (2017).…”

Section: Literaturementioning

confidence: 99%