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.
This book will 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.
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. 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 final chapter covers the relevant aspects of behavioural 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.
Table of Contents
|Chapter 1 Theory of Pricing Analytics||
|Chapter 2 The Practice of Pricing Analytics||
|Chapter 3 Dynamic Pricing and Markdown Optimization||
|Chapter 4 Pricing in Business-to-Business Environments||
|Chapter 5 Customer Behavior Aspects of Pricing||
|Appendix A Dichotomous Logistic Regression||
|Appendix B Advanced Analytics Using R||