Dynamic Pricing Strategies under a Finite Time Horizon
Joan Morris, Amy Greenwald, and Pattie Maes
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.