Introduction to normalization of demand data – The first step in isolating the effects of price on demand

This article is based on the presentation 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.