Correspondence: John D. Quillinan, 10046 Fox Meadow Trail, Winter Garden, Florida, USA

¹is Executive Director of Analytics for Westgate Resorts, where his responsibilities include delivering analytics to improve resort profitability, and implementing a revenue management system. John has over 25 years of information technology and operational research experience. Revenue management, retail price optimization and data analytics have been John's focus the past 5 years. John's most recently implemented price test analytics were for Tractor Supply Company. Leveraging his revenue management background and strong project management skills, John ushered in a price optimization system for the merchandise line of business at Walt Disney Parks and Resorts. John also laid the groundwork for bringing in a placement optimization system. In addition to creating the vision and plans for retail revenue management at Disney's theme parks and resorts, he assembled a strong team to lead the company through the development, implementation and ongoing maintenance of these optimization systems. Before working for Disney, John was a Manager of Operations Research at Norfolk Southern Corporation, and led a team of programmer analysts that developed Intranet-enabled decision support applications. At Norfolk Southern, John and his team developed and implemented a corporate forecasting system that is still in use today to assist Marketing with forecasting volume and revenues. John also enjoyed a successful career in the airline industry, where he worked in the Operations Research organizations at United Airlines, Delta Air Lines and Trans World Airlines over the course of 10 years. John holds a Master of Science degree in Industrial and Systems Engineering from the University of Southern California Los Angeles and a Bachelor of Science in Industrial Engineering from the University of Tennessee Knoxville. He is a member of INFORMS, current Chair-Elect of the INFORMS Pricing and Revenue Management Section and a Past Chair of the INFORMS Aviation Applications Section.

Received 3 September 2009; Revised 3 September 2009; Published online 30 October 2009.

Abstract

This article is based on the presentation (Quillinan, 2008) delivered in Montreal at the INFORMS Pricing and Revenue Management Section Conference in June 2008. We will introduce the concept of data normalization in pricing. The fundamental first step in identifying optimal pricing is to determine the price–demand relationship. The demand of a product is influenced by many explanatory variables such as consumer traffic or number of visiting customers, demographics of consumers, weather, and product availability. In order to extract the price–demand relationship, one must isolate the effect of price on demand from the effects of other explanatory variables. We employ the normalization process to isolate the impact of the significant explanatory variables on demand. In our study, normalization occurred on a weekly level, and within demand zones. We aggregated daily demand and all explanatory variables – mostly visitation statistics and demographics for a theme park and resort weekly. We grouped store locations to create demand zones for the purpose of using only applicable explanatory variables. Using the techniques of normalization, model fit, as measured by adjusted R², improved dramatically when using all significant explanatory variables versus consumer traffic only.