@article{SchlosserRichly2019,
  author    = {Schlosser, Rainer and Richly, Keven},
  title     = {Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects},
  series = {Journal of revenue and pricing management},
  volume    = {18},
  journal   = {Journal of revenue and pricing management},
  number    = {6},
  publisher = {Palgrave Macmillan},
  address   = {Basingstoke},
  issn      = {1476-6930},
  doi       = {10.1057/s41272-019-00206-5},
  pages     = {451 -- 464},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{SchlosserBoissier2018,
  author    = {Schlosser, Rainer and Boissier, Martin},
  title     = {Dealing with the dimensionality curse in dynamic pricing competition},
  series = {Computers \& Operations Research},
  volume    = {100},
  journal   = {Computers \& Operations Research},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0305-0548},
  doi       = {10.1016/j.cor.2018.07.011},
  pages     = {26 -- 42},
  year      = {2018},
  abstract  = {Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments can be time-consuming. We analyze stochastic dynamic pricing models under oligopoly competition for the sale of perishable goods. To circumvent the curse of dimensionality, we propose a heuristic approach to efficiently compute price adjustments. To demonstrate our strategy's applicability even if the number of competitors is large and their strategies are unknown, we consider different competitive settings in which competitors frequently and strategically adjust their prices. For all settings, we verify that our heuristic strategy yields promising results. We compare the performance of our heuristic against upper bounds, which are obtained by optimal strategies that take advantage of perfect price anticipations. We find that price adjustment frequencies can have a larger impact on expected profits than price anticipations. Finally, our approach has been applied on Amazon for the sale of used books. We have used a seller's historical market data to calibrate our model. Sales results show that our data-driven strategy outperforms the rule-based strategy of an experienced seller by a profit increase of more than 20\%.},
  language  = {en}
}