ABSTRACT
This chapter defines price segmentation, and discusses the importance of segmentation in the marketing mix while also emphasizing the importance of price segmentation as an iterative process. It also discusses segmentation and consumer heterogeneity that is actually an expansion of the discussion about discrete choice models. The chapter describes the ways to develop price segments. It provides information on a pricing scenario. The chapter focuses on two classes of methods for developing segments and price elasticities, each using the pricing scenario as a base. Price responses will differ among hospitals depending on the a priori segment each hospital belongs to. Post hoc modeling uses data to develop the segments. There are two subordinate classes of methods available: unsupervised and supervised learning methods. The chapter also provides information on one approach to supervised learning sometimes used in segmentation: decision trees. It also describes the software that can be used for segmentation and elasticity estimation.
