Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects
- 55 Downloads
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.
KeywordsDynamic pricing competition Data-driven price anticipation e-Commerce Dynamic programming Response strategies
- Amazon Marketplace Web Service (amazon mws) Documentation. 2019. Amazon.com. http://docs.developer.amazonservices.com/en_UK/dev_guide/index.html. Accessed 18 Jan 2019.
- Amazon Push Notification Information. 2019. Amazon.com. http://docs.developer.amazonservices.com/en_UK/notifications/Notifications_Overview.html. Accessed 18 Jan 2019.
- Babaioff, M., N. Nisan, and R. Paes Leme. 2014. Price Competition in Online Combinatorial Markets. In Proceedings of the 2014 World Wide Web Conference.Google Scholar
- Chen, L., A. Mislove, and C. Wilson. 2016. An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace. World Wide Web Conference 2016 (1339–1349): 2016.Google Scholar
- Chenavaz, R., and C. Paraschiv. 2018. Dynamic Pricing for Inventories with Reference Price Effects. Economics E-Journal 12 (64): 1–16.Google Scholar
- Ito, S., and R. Fujimaki. 2017. Optimization Beyond Prediction: Prescriptive Price Optimization. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017), 1833–1841.Google Scholar
- Levin, Y., J. McGill, and M. Nediak. 2009. Dynamic Pricing in the Presence of Strategic Consumers and Oligopolistic Competition. Operations Research 55: 32–46.Google Scholar
- Phillips, R.L. 2005. Pricing and Revenue Optimization. Palo Alto: Stanford University Press.Google Scholar
- Profitero Price Intelligence. 2014. Amazon Makes more than 2.5 Million Daily Price Changes. https://www.profitero.com/2013/12/profitero-reveals-that-amazon-com-makes-more-than-2-5-million-price-changes-every-day/. Accessed 22 May 2018.
- Schlosser, R. 2019. Stochastic Dynamic Pricing with Strategic Customers and Reference Price Effects. In 8th International Conference on Operations Research and Enterprise Systems (ICORES 2019), 179–188.Google Scholar
- Schlosser, R., and M. Boissier. 2017. Optimal Repricing Strategies in a Stochastic Infinite Horizon Duopoly. Communications in Computer and Information Science, vol. 884, 129–150. New York: Springer.Google Scholar
- Schlosser, R., and M. Boissier. 2018a. Dynamic Pricing Competition in E-Commerce: A Data-Driven Approach. In Proceedings of 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2018 (KDD 2018), 705–714.Google Scholar
- Yabe, A., S. Ito, and R. Fujimaki. 2017. Robust Quadratic Programming for Price Optimization. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), 4648–4654.Google Scholar