Dynamic pricing strategies under a finite time horizon

Abstract

In the near future, dynamic pricing will be a common competitive maneuver. In this age of digital markets, sellers in electronic marketplaces can implement automated and frequent adjustments to prices and can easily imagine how this will increase their revenue by selling to buyers "at the right time, at the right price." But at present, most sellers do not have an adequate understanding of the performance of dynamic pricing algorithms in their marketplaces. This paper addresses this concern by analyzing the performance of two adaptive pricing algorithms. We study the behavior of these algorithms within the Learning Curve Simulator, a platform for analyzing dynamic pricing strategies in finite markets assuming various buyer behaviors. The goals of our research are twofold: (i) to explore the use of simulation as a tool to aid in the development of dynamic pricing strategies; and (ii) to explicitly identify the market conditions under which our example strategies, Goal-Directed and Derivative-Following, are successful.