Demystifying Price Optimization: A Revenue Manager’s Guide
WHITE PAPER
SAS White Paper
Table of Contents
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
What Is Revenue Management? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
What Is Revenue Management Science?. . . . . . . . . . . . . . . . . . . . . . . 2
What Has Changed?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Taking Revenue Maximization Beyond the Travel Industry. . . . . . . . . 4
Price Optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Revenue Management in the Future. . . . . . . . . . . . . . . . . . . . . . . . . . . 6
How SAS® Is Different. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
For More Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Content for this paper was provided by Alex Dietz, Principal Industry Consultant for the SAS
Hospitality and Travel practice. He is a 25-year veteran of pricing and revenue management
solutions’ development and consulting in hospitality, travel and retail. Prior to joining SAS, Dietz
was Vice President of Revenue Management and Marketing for Raleigh-Durham based Midway
Airlines. He began his career with American Airlines and SABRE, where he oversaw development
of industry-leading revenue management systems for the airline industry. Dietz has a bachelor’s
degree in industrial engineering and operations research and a master’s degree in business
administration – both from Syracuse University.
Demystifying Price Optimization: A Revenue Manager’s Guide
Introduction
Revenue management has evolved into a very different science from what it was when
airlines first started using it in the mid-1970s. Today, revenue optimization is common
throughout travel and hospitality sub-industries including cruise lines, rental cars,
hotels, ferries and passenger railways. As the practice has expanded, the science
behind revenue management has been influenced by trends that invalidate important
assumptions that were built into the original models.
As analytic approaches to pricing have evolved, we at SAS have often heard 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 definition of revenue management. As
this paper explains, the question itself reflects a mindset based on historical business
practices.
SAS recognizes that today’s travel and hospitality marketplace encompasses aspects of
both revenue management and price optimization. To give customers the best possible
results, we recommend an analytical approach that takes into account both revenue
management and price optimization techniques.
What Is Revenue Management?
To truly understand revenue management, it’s important to start with today’s definition
and review how it has changed over time. Wikipedia defines revenue management as
“the application of disciplined analytics that predict consumer behavior at the micromarket 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 travel
revenue managers and executives. Practitioners and industry experts alike 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 no different from revenue
management, and might be better described as a specific methodology for applying
revenue management.
To get a better understanding of how price optimization evolved from being a separate
discipline into being a tool in the revenue management toolbox, we need to explore the
development of revenue management as a science.
1
SAS White Paper
What Is Revenue Management Science?
The application of mathematical models, specifically to travel pricing, can be traced
to the mid-1970s. Before then, the use of analytics in travel sales was limited to
approaches surrounding overbooking. But in the mid-’70s – after deregulation of the
US airline industry – a number of airlines introduced discounted fares that were sold into
the same compartment as the already-available full fares. These new discounted fares
generally required significant advance purchase: 21 days.
The new discounts allowed the airlines to fill seats that would have otherwise gone
unused on low-demand flights. But as a result, the airlines needed a method to
control the number of reservations taken on these reduced fares when demand for
late-booking, full-fare passengers was expected and when total demand from both
discounted and full fares would exceed capacity. Managing these sales required airline
systems’ inventory control capabilities to evolve so that analysts could control the
number of discounted sales on a day-by-day and flight-by-flight basis.
Next, the airlines formed yield management groups. The yield manager’s role was to
maximize revenue on each flight by predicting the sale of full-fare reservations and
controlling the number of discounted seats sold accordingly. The amount of space
to reserve for these future reservations was (and still is) referred to as protection. As
the industry evolved this practice, airlines developed increasingly complex fences
(restrictions placed on discounted fares) to distinguish these fares from full fares (and
each other). The fences reduced dilution (reduced revenue caused by a customer
purchasing a discounted fare that was not intended for his market segment). Airlines
enhanced their selling systems to enforce these restrictions as they were developed and
adapted. Examples of fences include:
• Advance reservations.
• Nonrefundable fares.
• 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 that would maximize revenue.
The business had evolved to the point where multiple protections would need to be
calculated for any given flight departure – because there were now many different
discounted fares available, each with a different value and customer target segment. In
effect, the mathematical problem involved determining the maximum number of each of
these different discounted products that should be sold to maximize revenue. The early
systems developed for this purpose were known as yield management systems, and
they mark the beginning of revenue management as a science.
2
Demystifying Price Optimization: A Revenue Manager’s Guide
The most involved yield management systems attempted to account for complexity
due to the network effect: specifically, the problem of forecasting demand for a specific
segment (or leg) of an itinerary, when many different kinds of itineraries (all with different
values to the airline) also flowed over it. The question was critical to growing hub-andspoke carriers where significant percentages of passenger itineraries included a stop at
the carrier’s hub.
What Has Changed?
The original formulations for revenue management science were based on how the
airlines ran their businesses. Fenced fare structures were critical components of this
business model, because they clearly separated different customer groups with distinctly
different values. And, although these fences differed across disparate customer groups,
a key modeling assumption was that these different values were relatively stable over
time. (This assumption arose in part because of the fairly small computing power of
older systems compared to today’s systems.) This assumption of stability allowed yield
management systems to assume a level of independence between customer groups.
For example, the systems assumed that a customer for a 21-day, nonrefundable
advance purchase (AP) fare with a Saturday night stay requirement would not purchase
a full fare if the 21-day AP fare was sold out – and that (similarly) a full-fare customer
had no interest in that 21-day AP fare if it was available. 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 happened to utilize the same inventory unit
(the airline seat) in doing so.
What is revenue management, and how did it evolve?
Application of disciplined analytics that predict consumer behavior at the
micromarket level and optimize product availability and price to maximize revenue.
1980s
Airlines introduce
discounted fares
& revenue
management
1990s
Revenue management
spreads through
travel & hospitality
industries
2000s
Limitations of
inventory optimization
solutions result in
trend toward price
optimization
Figure 1: How did revenue management get to where it is today?
Three important trends have emerged since these original methodologies were
developed:
• Low-cost airlines introduced simplified fare structures with fewer fares and
significantly reduced fencing. Today, these simplified fare structures dominate
most air travel markets – effectively invalidating the assumption of demand
independence made in revenue management models.
3
SAS White Paper
• Revenue management has been introduced into markets where strict fences on
rates or fares never existed. Hotels, rental cars, cruise lines and others have rate
or fare structures that do not contain strict fences – so the assumption of demand
independence is again problematic.
• Rates and fares in these simplified structures have become increasingly dynamic.
Revenue managers now frequently manage the price at which rates and fares are
sold on a day-to-day basis.
These trends have taken travel and hospitality practices far afield from the expectations
of the original revenue management scientists who modeled the airline revenue
management problem. Of course, revenue management scientists have tried to
overcome the underlying limitations in the original construct of the problem – generally,
they have done this by introducing changes to forecasting and optimization approaches
that allow for some measure of interdependence of demand for different fares or rates.
Consequently, we see common references to “sell up” or “buy down” probabilities
associated with forecasting and optimization in such systems.
There are also other business elements generally required for the application of this
original revenue management approach:
• Fixed available capacity – capacity cannot be increased 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.
These underlying assumptions continue to be valid for travel companies, so they have
not caused the same types of concerns as the other trends.
Taking Revenue Maximization Beyond the Travel Industry
While many travel and hospitality companies were struggling to make revenue
management approaches work, other industries were considering how to maximize
their own revenue and profitability – and recognizing that the airlines’ original revenue
management approach simply would not work for them. Generally, the lack of fit was
due to an inability to meet one of the required business elements described previously.
Imagine if you were a retailer trying to apply revenue management techniques:
• 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 – if you don’t sell a shirt one day, it
will be there to sell the next day.
4
Demystifying Price Optimization: A Revenue Manager’s Guide
• You don’t have a fixed capacity – if you start selling more milk, you can buy more
milk to sell.
• All of your customers pay the same price – they come in, see the price tag on the
item or shelf, and that’s what they all pay.
The bottom line: Your business fails to meet every one of the elements that are needed
for the application of traditional revenue management. But does that mean retailers
can’t use analytical modeling and/or operations research techniques to maximize
revenues? Of course not! They just need a different approach.
Price Optimization
Price optimization approaches were developed to suit industries whose characteristics
don’t match that of the original revenue management approaches. In these businesses
(retail is a good example) products are made available at a single price to a broad
market of customers. In this broad market, each customer has a different ability or
willingness to pay, and the business can change the product’s price over time. If the
price is raised, it begins to exceed the price that most customers are willing to pay – so
demand decreases. If the price is lowered, more customers are willing to pay for it – so
demand increases. This is simple microeconomic theory.
Using a price optimization approach, product demand is estimated as a function of
price. So the price becomes the decision variable rather than the protection value that is
used in maximizing revenues or margin. In businesses that use price optimization, there
are usually no advance reservations or separation by segment, so there’s no one to
“protect” sales for. In addition, inventory may not be constrained – so there’s no reason
to protect inventory, either. Since the market is not be segmented by price, the question
becomes “what is the optimal price to charge the overall market to maximize return,
accounting for the fact that demand will change as the price changes?”
The retail industry has widely adopted price optimization approaches, but these
approaches are also used by other industries. Decision support solutions that utilize
price optimization approaches (like the SAS Revenue Optimization Suite for retail1) need
to estimate demand and its sensitivity to price (i.e., price elasticity) for a wide variety of
products, and often from a wide variety of channels or stores. The calculation of price
sensitivity on a large-scale, automated basis introduces new technical challenges that
these solutions have had to overcome – much research has been invested in this area
over the last decade or so.
1 SAS Revenue Optimization Suite: sas.com/industry/retail/revenue-optimization/index.html
5
SAS White Paper
Revenue Management in the Future
The trends that have taken hold following the initial development of revenue
management methods have undermined traditional revenue management science,
because they invalidate important assumptions built into the models.
Today, rather than selling a series of clearly fenced fares, many travel companies –
including airlines and passenger rail lines – offer different fare families. Fare families are
distinguished from each other using a set of purchase restrictions (e.g., refundable) or
benefits (e.g., variations in frequent flyer points earned, or standby benefits). In effect,
each fare family represents a different fare product, and the price for each fare family can
be changed on a day-to-day and flight-by-flight basis. Fare families represent a two-fold
problem for the traditional revenue management approach:
• There is no independence of demand for fares within a fare family – all of these
fares have the same set of rules and restrictions, and so the assumption of
independence fails completely for different fares within a fare family. Plus, the
choice of higher or lower prices is known to influence demand – and this effect
needs to be modeled in some manner.
• The differential in restrictions and benefits between fare families is known to be
relatively weak, so when price differentials between fare families are too large on
a given flight, a customer will “jump” from choosing one fare family to choosing
a different fare family. Accommodating this complex type of behavior requires a
demand modeling approach that considers demand for each flight as a function of
both the availability and pricing of each fare family on the flight.
Consider the example of a roadside hotel. This business meets the primary conditions
of revenue management (fixed capacity, perishable product and advance reservations);
but it probably does not have clearly fenced segments. Rather, this business chooses
one of several possible rates to charge on any given date, and that generally available
rate services the vast majority of its customers. Once again, there is no independence of
demand between these rates – they all have the same set of characteristics, so the hotel
guest will always select the least expensive one.
These types of “priceable” products represent a significant portion of today’s travel
and hospitality market. Most travel and hospitality firms have such a product, and for
many of them, it is a significant portion of their business. For priceable products, a price
optimization approach seems to be a good fit for the business problem. Because with
this approach the selection of an optimal price is the primary output of the model, and
price elasticity is directly modeled.
6
Demystifying Price Optimization: A Revenue Manager’s Guide
In the airline industry, two new approaches to accounting for these changes have
evolved:
• Continued investigation into estimating up-sell (within fare families) and cross-sell
(between fare families) to inform revenue management models. This methodology
continues to represent the current state-of-the-art for revenue management
modeling for airlines.
• Investigation into the use of choice models as a more robust approach, which
accounts for fare family rule and benefit differences – and how these are valued
versus price differentials between the fare families. While theoretically attractive,
these investigations have not yet borne fruit in terms of actual applications.
In the hospitality industry (including hotels and cruise lines), priceable products make up
a significant portion of the market – but not all products are priceable. Many customers
purchase rates and fares that are controlled not through price, but through availability.
For example, many promotions are managed this way, and some segments can be both
priced and managed via availability.
These changes cause much of the confusion surrounding revenue management and
price optimization approaches. In fact, today’s travel and hospitality marketplace has
aspects of both revenue management and price optimization problems. Hybrid analytic
approaches combining aspects of both revenue management and price optimization
approaches are the best solution for today’s marketplace. The hybrid approach also
allows for market changes that will continue to take place over time.
How SAS® Is Different
At SAS, we have developed modeling approaches that allow for optimization of both
priceable and yieldable rates and fares in SAS Revenue Management and Price
Optimization Analytics. These models can forecast demand for both price-elastic,
priceable fares, as well as those that cannot be priced. Our optimization approach
calculates both optimal price (for priceable fares) and optimal protection levels (for fares
or rates that must be managed via availability).
Traditional Revenue Management SAS® Revenue Management and Price
Solution Approach
Optimization Analytics
Limited number of demand
models and parameters.
Automated forecast model selection from a broad
portfolio of models, and automated parameter
optimization per product and segment.
Price elasticity ignored or treated
as a post-demand modeling
process.
Price elasticity incorporated directly into demand
modeling.
Decomposed pricing and
inventory control optimization.
Integrated pricing and inventory control
optimization across product classes.
Figure 2: A comparison of a traditional revenue management approach and
SAS® Revenue Management and Price Optimization Analytics.
7
SAS White Paper
Finally, and perhaps most importantly, our optimization approach treats pricing and
availability decisions as one integrated problem – and makes calculations accordingly.
After all, changes in price can affect demand, which affects optimal protection for
yieldable products. Similarly, changes in the availability of yieldable products influences
decisions regarding the amount to charge for priceable rates and fares. Revenue
management and price optimization – the two must work together.
Conclusion
The major advantage of SAS’ hybrid approach is that it models the problem as it
exists – and does not require significant business shifts or related technology changes
to maximize revenues. Optimizing yieldable and priceable products together enables
companies to develop pricing and inventory approaches that suit the needs of their
market, but are still supported by high-performance analytical modeling. The approach
also offers flexibility in supporting new sales and marketing approaches in the future.
Other hybrid approaches tackle pricing and yielding as separate decisions – first
calculating optimal protection levels, and then separately calculating optimal pricing
using the protections output from the first step as input to that decision. At SAS, we
have found that this approach of arbitrarily breaking the problem into multiple steps
results in suboptimal results. And there are revenue benefits to an approach that
considers the entire business model. Our testing shows significant revenue benefits
of the integrated analytics approach used in SAS Revenue Management and Price
Optimization Analytics versus that used in other solutions – on the order of 2 percentage
points in revenue growth.
This is the future of revenue management and price optimization: flexible, integrated
models that allow businesses to be modeled as they actually exist. No more round
pegs in square holes. No more shoehorning our businesses into processes that support
analytics, or attempting to utilize analytics that don’t really fit our businesses. Let the
analytics come to the business.
8
Demystifying Price Optimization: A Revenue Manager’s Guide
For More Information
View the webcast titled New Beginnings in Revenue Management:
sas.com/reg/web/corp/2138309
Learn more about SAS® Revenue Management and Price Optimization Analytics for
hospitality and travel: sas.com/industry/hospitality/revenue-management/index.html
Visit The Analytic Hospitality Executive blog: blogs.sas.com/content/hospitality
Bibliography
McGill, J.I. and Van Ryzin, G.J. (1999). Revenue Management: Research Overview and
Prospects. Transportation Science 33: 233–256.
Fiig, Isler, Hopperstad and Olsen (2012). Forecasting and Optimization of Fare Families.
Journal of Revenue and Pricing Management, Vol. 11, 3, 322-342.
Koushik, D. Higbie, J. and Eister, C (2012). Retail Price Optimization at InterContinental
Hotels Group. Interfaces, Vol. 42, No. 1, 45-57.
9
About SAS
SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market.
Through innovative solutions, SAS helps customers at more than 60,000 sites improve performance and deliver value by making better
decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW®.
SAS Institute Inc. World Headquarters +1 919 677 8000
To contact your local SAS office, please visit: sas.com/offices
SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA
and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies.
Copyright © 2013, SAS Institute Inc. All rights reserved. 106156_S100361_0113