Dynamic pricing with real-time demand learning
Authors
Lin, Kyle Y.
Subjects
Pricing
Dynamic pricing
Forecasting
Learning
Revenue management
Dynamic pricing
Forecasting
Learning
Revenue management
Advisors
Date of Issue
2005
Date
Publisher
Language
Abstract
In many service industries, the firm adjusts the product price dynamically by taking into account the current product
inventory and the future demand distribution. Because the firm can easily monitor the product inventory, the success of
dynamic pricing relies on an accurate demand forecast. In this paper, we consider a situation where the firm does not
have an accurate demand forecast, but can only roughly estimate the customer arrival rate before the sale begins. As the
sale moves forward, the firm uses real-time sales data to fine-tune this arrival rate estimation. We show how the firm can
first use this modified arrival rate estimation to forecast the future demand distribution with better precision, and then
use the new information to dynamically adjust the product price in order to maximize the expected total revenue.
Numerical study shows that this strategy not only is nearly optimal, but also is robust when the true customer arrival
rate is much different from the original forecast. Finally, we extend the results to four situations commonly encountered
in practice: unobservable lost customers, time dependent arrival rate, batch demand, and discrete set of allowable
prices.
Type
Article
Description
The article of record as published may be located at http://dx.doi.org/10.1016/j.ejor.2005.01.041
Series/Report No
Department
Operations Research
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
European Journal of Operational Research, Volume 174, (2006), pp. 522–538
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.