Learning and pricing in an Internet environment with binomial demands

This paper considers the problem of setting prices dynamically to maximise expected revenues in a finite horizon model in which the demand parameters are unknown. At each decision epoch, the manager chooses a price and observes a binary response (buy or not) for each consumer visiting the website during that period. This paper focuses on comparing several easy to implement good pricing policies. A Taylor series expansion of the future reward function explicity illustrates the trade–off between short–term revenue maximisation and future information gains and suggests a pricing policy referred to as a one–step look ahead rule. A Monte Carlo study compares several different pricing strategies and shows that the one–step look ahead rule dominates other policies and produces good short term performance.