Here at the Analytic Hospitality Executive, we are often asked the question “How is price optimization different from revenue management?”  It’s a simple question that has become increasingly difficult to answer – largely due to the evolution of revenue management practices, and the very definition of revenue management. In this post I will explore the practice of hospitality revenue management, it’s origins in the airlines and how it was changed for use in hospitality. In my next post, I will explore how price optimization has evolved and how hospitality organizations can use it to gain competitive advantage.

What is revenue management?

To answer our question, one needs to start with today’s definition of revenue management, and see how it has changed over time.  Today, Wikipedia defines Revenue Management as “the application of disciplined analytics that predict consumer behavior at the micro-market level and optimize product availability and price to maximize revenue growth”.  This definition is very broad– but probably well in line with the thinking of most hospitality revenue managers and executives.  Most hospitality staff today think of revenue management not as a science but as a wide set of business practices with a common goal.  By this definition, price optimization is not different from revenue management – it would be better described as a specific methodology for applying revenue management.

So, to get a better understanding of this difference between price optimization and revenue management, we need to delve into history a bit, and understand the development of revenue management as a science.

The origins of revenue management

Experts agree that modern revenue management was born in the airline industry, where airlines experimented with reduced fares available with significant advance purchase, and subsequently needed a method to control the number of reservations taken on these reduced fares when demand for late booking, full fare passengers was expected.  The amount of space to reserve for these future reservations was (and still is) referred to as protection.  As the airline industry evolved this practice it developed increasingly complex fences (restrictions placed on discounted fares) to distinguish these fares from full fares (and each other), and to reduce dilution (reduced revenue caused by a customer purchasing a discounted fare that was not intended for his market segment).  Examples of such fences include:

• Advance reservation
• Advance purchase
• Non-refundability
• Minimum length of stay (including the infamous “Saturday night stay” requirement)
• Maximum length of stay
• Day of week applicability

As this business process evolved, mathematicians began to model the problem – based on the manner in which the business was being run at that time (ca. 1985).  Their goal was to find the mathematically optimal protection levels (the business by this point had evolved to the point where multiple protections would need to be calculated for any given flight departure) that would maximize revenue.  In effect, the mathematical problem was to determine the maximum amount of each of these different discounted products should be sold in order to maximize revenue.  This is the beginning of revenue management as a science.

What has changed?

As noted above, the original formulations for revenue management science were based on the manner in which the airlines ran their business.  A critical component of this business was the fences noted above, which clearly separated out different customer groups that had distinctly different values – values that were relatively stable over time.  This allowed the modelers to assume a level of independence between customer groups (e.g. the assumption that a customer for a 21 day, nonrefundable advance purchase fare with a Saturday night stay requirement would not purchase a full fare if the 21 day advance purchase fare was sold out, and that similarly a full fare customer had no interest in that 21 day advance purchase fare)  – in effect this assumption meant that each different fare type could be treated as a separate product, which serviced a separate market of customers, and which just happened to utilize the same inventory unit (the airline seat) in doing so.

Two important things have happened since these original methodologies were developed:

1. Low cost airlines introduced fare structures with significantly reduced fencing, and expanded significantly – effectively invalidating the assumption of demand independence made in these revenue management models
2. Revenue management science has been introduced into markets where strict fences never existed.  Hotels, rental cars, and so on have rate structures that do not contain strict fences – and so the assumption of demand independence is again problematic

Of course, revenue management scientists have tried to overcome this underlying limitation in the original construct of the problem, via changes to forecasting and optimization approaches which allow for some measure of the interdependence of the demand for different fares or rates.  Consequently, you will see common references to “sell up” or “buy down” probabilities associated with forecasting and optimization in such systems.

I should also note here that there are other business elements generally required for the application of this original revenue management approach, as well:

• Fixed available capacity – capacity cannot be increased in order to accommodate surplus demand
• Product perishability – the product loses its value to a customer after some point in time, and cannot be sold thereafter
• Advance reservations – advance reservations are accepted for the product

Taking revenue maximization beyond Hospitality and Travel

At the same time that many hospitality and travel companies were struggling to make these revenue management approaches work, other industries were considering how to maximize their own revenue and profitability – and recognizing that the revenue management approach originally taken by the airlines simply would not work for them.  Generally, the lack of “fit” would be due to an inability to meet one of the required business elements that I have listed above.  Imagine, for a moment, trying to apply revenue management techniques if you are a retailer:

• You don’t take advance reservations for your product – your customers come to your store and purchase the product while they are there
• Most of your products aren’t really perishable (unless you only sell produce and meat – in which case your products are obviously very perishable)
• You don’t have a fixed capacity – if you start selling more milk, you can buy more milk to sell, and finally
• All of your customers (pretty much) pay the same price – they come in see the price tag on the item or shelf, and that’s what they all pay

For retailers, their business fails to meet every one of the essential elements that I’ve noted above, but it certainly does not that mean that a retailer can’t also use science to maximize their revenues. Retailers need a different approach, and that’s where price optimization comes in. In my next post, Revenue Management vs. Price Optimization: Part Two, I will explore how price optimization approaches were developed, and how price optimization can be applied in a hospitality context to improve the ability to maximize revenues.

For more information on trends in pricing, including price optimization, download the New Pricing Techniques for Hospitality and Gaming white paper.