This discipline had its origins in the development of yield management in the airline industry in the 1980s, and has since spread to many other sectors and pricing contexts, including yield management in other travel industry sectors, media, retail, manufacturing and distribution.
These programs provided model-based support to answer the central question faced by deregulated airlines: "How many bookings should I accept, for each fare product that I offer on each flight departure that I operate, so that I maximize my revenue?"
The yield management programs provided dramatic financial benefits to their early adopters in the early- to mid-1980s, and the approach spread rapidly to firms in the related sectors of hotel, rental car, and cruise line industries.
While there are important differences between these industries, the dominant drivers of the solutions were the perishable nature of the resource being sold, demand patterns that were time-variable, and the limited capacity available for sale.
For a good overview of pricing science methods and applications related to yield or revenue management, see Phillips [1] and the references cited therein.
Yield management has been applied successfully to broadcast and cable television, online media, oil and gas producers, sporting and theatrical providers, apartment and timeshare rental properties, credit card, and retail settings.
Even airlines and other early practitioners began to revisit their original assumption that prices were a "given," a simple input to their optimization technology.
As the applications of scientific methods to these business problems expanded, the discipline of pricing science became more rigorous and methodological.
Initially, statistical and optimization methods were adapted by practitioners and theoreticians from the engineering and operations research disciplines.
The forecasting problem reflects the fact that the pricing decisions are intended to affect purchase events over some future time horizon.
The optimization problem reflects the mathematical complexity required to reach feasible and practical pricing solutions.
is the aggregate of related series (e.g. all itineraries serving a particular origin-destination, or all sizes and colors of the particular style of shirt),
The most well-known (and likely, most broadly applied) heuristic method for a large class of yield management problems is known as the Expected Marginal Seat Revenue (EMSR) algorithm.
One organization of the types of technology is to consider (a) general purpose tools used to implement some Pricing Science techniques; (b) use of localized technology, typically standard office tools, configured to utilize Pricing Science methods; and (c) specialized, enterprise-class software designed and developed for this purpose.
In some businesses, pricing decisions are supported using forecasting and optimization methods executed on an as needed basis using general purpose analytic tools.
This approach is often seen in large enterprises which have quantitative analysts familiar with the tools and, to various degrees, with Pricing Science methods, or which retain specialized consultants to perform the analysis.
In many enterprises, the technology used to support pricing and related decisions, using the methods described above, are standard office applications for data management, reporting and analysis.
Some very large enterprises have implemented and evolved very elaborate processes of data acquisition and manipulation, using such technology.
As the developers and users of these technologies are, for the most part, generalists, there may be frequent issues of quality, reliability and extensibility of such processes.
Since yield management began to take roots in the 1980s, a number of highly specialized enterprise software providers have grown up to meet the needs of businesses that have taken advantage of the margin enhancement opportunities afforded by the methods.
In addition, these providers generally supply specialized expertise in pricing science applications and methods.