Non-parametric optimal service pricing: a simulation study

In this paper, we study a price discovery algorithm for searching the optimal price for a service with price-sensitive demand. The customer's response to price is unknown and the customer arrival process follows an arbitrary point process. This algorithm is suitable for new services with no prior knowledge or historical data available. Furthermore, there is no information about objective function as well. We take on a simulation study and discuss the sensitivity and robustness of this procedure with respect to different arrival processes and customer response functions and also provide the comparative statics with simple price learning algorithm. We prove that price discovery algorithm is a better convergent as it reduces stochastic error at each step. The main focus of this research is to provide some guidance for the selection of sample sizes based on the test significance and the measure of its power when actual mean and variance for revenue are unknown.