Journal of Revenue and Pricing Management

, Volume 18, Issue 6, pp 451–464 | Cite as

Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects

  • R. SchlosserEmail author
  • K. Richly
Research Article


Online markets have become highly dynamic and competitive. Many sellers use automated data-driven strategies to estimate demand and to update prices frequently. Further, notification services offered by marketplaces allow to continuously track markets and to react to competitors’ price adjustments instantaneously. To derive successful automated repricing strategies is challenging as competitors’ strategies are typically not known. In this paper, we analyze automated repricing strategies with data-driven price anticipations under duopoly competition. In addition, we account for reference price effects in demand, which are affected by the price adjustments of both competitors. We show how to derive optimized self-adaptive pricing strategies that anticipate price reactions of the competitor and take the evolution of the reference price into account. We verify that the results of our adaptive learning strategy tend to optimal solutions, which can be derived for scenarios with full information. Finally, we analyze the case in which our learning strategy is played against itself. We find that our self-adaptive strategies can be used to approximate equilibria in mixed strategies.


Dynamic pricing competition Data-driven price anticipation e-Commerce Dynamic programming Response strategies 



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Copyright information

© Springer Nature Limited 2019

Authors and Affiliations

  1. 1.Hasso Plattner InstituteUniversity of PotsdamPotsdamGermany

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