Pricing Segmentation and Analytics

Pricing analytics uses historical sales data with mathematical optimization to set and update prices offered through various channels in order to maximize profit. A familiar example is the passenger airline industry, where a carrier may sell seats on the same flight at many different prices. Pricing analytics practices have transformed the transportation and hospitality industries, and are increasingly important in industries as diverse as retail, telecommunications, banking, health care and manufacturing. The aim of this book is to guide students and professionals on how to identify and exploit pricing opportunities in different business contexts. The first chapter looks at pricing from an economist's viewpoint, beginning with the basic concept of price elasticity and how it differs at the product, firm, and industry levels as well as the short term versus long term. Next, the common assumptions regarding the customer population's willingness-to-pay is discussed along with the price response curves that result from this assumption. Basic price optimization techniques are then explored with extensions provided for alternative objective functions and constrained supply. The second chapter looks at these same topics, but from a more practical standpoint, with examples provided from several consulting projects. The third chapter is on dynamic pricing, with a special emphasis on the most common application: markdown pricing. Similar to the first two chapters, both the theory and the application aspects will be covered. The fourth chapter covers the new field of customized pricing analytics, where a firm responds to a request-for-bids or request-for-proposals with a customized price response. In this situation, the firm will only have historical win/loss data and traditional methods involving price elasticity do not apply. The pricing analytics methodology along with several case studies will be provided. The final chapter covers the relevant aspects of behavioral science to pricing. Examples include the asymmetry of joy/pain that customers feel in response to price decreases/increases. A set of best pricing practices will be presented that are based on these behavioral responses. Finally, the appendix will contain the details needed to build and implement a pricing analytics system in practice.