Dynamic pricing is a technique used by many firms to offer goods and service at a price that is adjusted over time to (a) match supply with demand, (b) respond to shifting demand patterns, and (c) achieve customer segmentation. Since the 1990’s, an increasing number of firms have used dynamic pricing to increase their revenue.
Small companies are typically price takers who can at best target niche markets through their pricing policies. Further, large companies typically have monopolistic or quasi-monopolistic market power to set prices which much of the same industry will need to follow. Hence, middle market companies are in a unique position with respect to their pricing opportunities and decisions. They need to adjust their prices dynamically, in response to changing customer demand, in order to be competitive. This work provides managers of middle market firms with a toolkit for making better pricing decisions and also justifying those decisions to their various stakeholders.
It is well documented that numerous middle market companies use dynamic pricing solutions provided by software providers such as JDA, PROS, SAS, and Oracle. We provide three specific applications from the middle market:
Example 1
Ohio based NewPage is the leading maker of coated paper products in North America. NewPage needed to improve its pricing analysis and data visibility in order to become more competitive in a changing market. In 2010, NewPage implemented the PROS Pricing Solution Suite. This helped NewPage gain insight into its pricing data, so senior managers can make better business decisions.
Example 2
Continental Airlines Cargo is a mid-sized cargo service provider. JDA's revenue management and pricing optimizer has boosted this company's revenue by 2.5%, or $1m per year.
Example 3
Dollar Thrifty Automotive Group manages approximately 250 Dollar Rent A Car and 340 Thrifty Car Rental locations in the U.S. In its first year utilizing JDA's Rental Car Revenue Management System, the company improved its profit by more than $7m million by leveraging the solution to more accurately forecast demand and reduce “turn away'' business.
We consider a typical middle market firm that sells a given inventory of a product over a short selling season. The firm needs to decide what price to use in each time period. The pricing decisions across time are interdependent and need to be made jointly, in order to maximize total profit. While making these decisions, the firm is faced with one complicating factor. Given the price of the product, the demand for the product is uncertain. We consider a situation where only an interval for future demand, such as between 100 and 150 units, is known. Moreover, decisions based on intervals are likely to be much more robust, i.e. reliable in the worst case, than those based on an inaccurate probability distribution. Given the complexity of the pricing problem, the simple rules or experience-based approaches commonly used in industry do not work well. As an alternative, we develop more reliable optimization models and solution approaches to generate optimal or near optimal pricing decisions. One implication of this work is that obtaining very accurate demand forecasts may not be essential for achieving good pricing policies.