TY  - JOUR
A1  - Schlosser, Rainer
A1  - Richly, Keven
T1  - Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects
JF  - Journal of revenue and pricing management
N2  - 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.
KW  - Dynamic pricing competition
KW  - Data-driven price anticipation
KW  - e-Commerce
KW  - Dynamic programming
KW  - Response strategies
Y1  - 2019
U6  - https://doi.org/10.1057/s41272-019-00206-5
SN  - 1476-6930
SN  - 1477-657X
VL  - 18
IS  - 6
SP  - 451
EP  - 464
PB  - Palgrave Macmillan
CY  - Basingstoke
ER  -