Micro 1.2 pdf

8/9/2019 Micro 1.2 pdf

1/18

(Realtime) dynamic pricing in an integrated

revenue management and pricing

environment: An approach to handling

undifferentiated fare structures in

low-fare markets

Dieter Westermann

Received (in revised form): 28th September, 2005

Lufthansa Systems Berlin GmbH, Fritschestrae 2728, 10585 Berlin, Germany

E-mail: [email protected]

Dieter Westermann has been an employee

of Lufthansa Systems Berlin since July

2004. He holds the position of General

Manager Strategic Projects in the Division

of Revenue Management. Dieter has

extensive experience in revenue manage-

ment business processes and systems as

well as in the area of pricing, reservations

and distribution. During his 15 years in

airline revenue management, he has

worked for carriers such as Lufthansa and

Swissair. During his last assignment at

Swiss International Air Lines as Vice

President Business Solutions, he was

responsible for the systems environment

for revenue management, pricing, reserva-

tions and inventory, and CRS distribution.

An important step during that time was the

successful implementation of an origin

and destination revenue management

system.

ABSTRACT

KEYWORDS: traditional pricing, dynamic

pricing, undifferentiated fare structures,

realtime distribution, willingness to pay,

passenger segmentation

This paper compares the traditional pricing pro-

cedure of network carriers using fare products

defined by rules and restriction with the concept

of dynamically adjusting prices based on current

market conditions. It describes why traditional

revenue management procedures and algorithms

fail in a fenceless environment typical of low-fare

carriers. The paper attempts to explain how a

dynamic pricing concept based on willingness to

pay could form the basis of a long-term revenue

management solution for various types of air-

lines, from low-fare carriers to network carriers.

The concept requires significantly more inte-

grated business processes between pricing and

revenue management and therefore demands a

new and future-oriented approach to revenue

optimisation.

GLOSSARY

O&D Origin and destination: part of a

passenger itinerary. Rather thansplitting the itinerary into pieces

defined by the physical stops of an

aircraft (leg) or by flight number

defined by the schedule of the

airline (seg-ment), it considers the

single passengers boarding and

landing airport.

Pa

Journal of Revenue and Pricing

Management, Vol. 4, No. 4, 200

pp. 389405

# Palgrave Macmillan Ltd,

14766930/06 $30.00

Journal of Revenue and Pricing Management Volume 4 Nu

8/9/2019 Micro 1.2 pdf

2/18

GDS Global distribution system:

computerised distribution

systems providing independent

availability, schedule, and

booking functionality for the

travel industry such as airlines,hotels and others.

ATO/CTO Airport and city travel offices:

sales offices at an airport or in

a city, which offer and sell

mainly the own airlines

product.

AVS/AVN Message to communicate seat

availability between the inven-

tory holding system of an

airline and the distribution

systems. The status is commu-nicated either in numeric form

or by specific codes defining

the current status.

BACKGROUND

Segmentation of demand

Airlines recognised decades ago that if they

could structure a pricing model that would

force the few elite travellers to pay the pre-

mium fares (and incidentally generate the

majority of the airlines revenue), the

remaining customer segments that were

more price sensitive could be attracted to

the airline with various levels of discounted

fares that were defined by a set of rules and

restrictions around the fare levels. The

lower the fare level, the more restrictive

the set of rules and restrictions.

The reasoning behind this is based on thetheory of market segmentation. As shown

in Figure 1, there are two reasons why

charging only a single price may result in

lower revenues. First, passenger segments

willing to pay higher prices will just pay

the price published. At the same time, pas-

sengers considering the price too high will

not purchase the product. By offering dif-

ferent products at different prices, the

market is being segmented. Higher prices

can be maintained by offering better butmore expensive products. At the same

time, less expensive products are being

offered to the consumers who are not will-

ing to accept the higher prices.

Airline product

A problem with the classical airline pro-

duct is that it is a homogenous product as

long as the passenger books a specific cabin

for transportation. Therefore, the airlines

had to create a methodology which, from

the customer perspective, would associate a

set of product descriptions that would

create heterogeneous products within the

FareFare

D6

D5

D4

D3

D2

D1

F6 F5 F 4 F 3 F 2 F 1

Lost passengersand hence lostrevenue

Multiple price points allow products tobe offered to multiple customersegments. More revenue is extractedfrom the market.

F1

PassengerDemand

D1

Figure 1: Market segmentation by offering multiple products at different prices

age 390

ealtime) dynamic pricing

8/9/2019 Micro 1.2 pdf

3/18

same cabin. The airline solution was to

define the airline product as more than just

a seat on an aircraft. The airline product

was defined as the fare level and the set of

rules and restrictions around that fare level.

In this business concept, the various seatsin an aircraft compartment can be defined

as different products depending on the

rules and restrictions that apply to the fare

purchased by the passenger. Typical rules

and restrictions around a fare are indicated

in Table 1.

The primary objective of this type of

customer segmentation was to establish

robust boundaries (the set of rules and

restrictions around each fare level) that

effectively prevented the higher farepaying passengers from buying-down into

the lower fare levels. The better the set of

restrictions were defined, the more pre-

cisely the airline could segment the custo-

mer marketplace.

Traditional airline revenue management

Customer segmentation based on the rules

and restrictions around the fare levels

meant that the airlines would be able to

track passenger booking behaviour at these

fare levels (booking classes), and this would

be the basis for generating forecasts of

future passenger demand. Passengers

unable to purchase the product (booking

class) desired, did not automatically buy-up

into another product. While there is defi-

nitely some buy-up behaviour, for the

most part the customers changed the dates

or time of travel or were lost to a competi-

tor airline. Even more importantly, owing

to the nature of the restriction set aroundthe fares, a customer segment is excluded

from being able to purchase an available

lower fare booking class product. Seg-

menting the passengers in this way allowed

the airlines to consider the demand for

each booking class as independent passen-

ger demand.

The combination of passenger demand

forecasting and multiple fare levels inevita-

bly led to the use of sophisticated algo-

rithms to determine the optimal mix ofpassengers at each of the fare levels on

board an aircraft, to maximise the total

revenue for the airline. Airline revenue

management was the end result.

Low-fare carriers

Over the last few years, there has been a

dramatic rise in the number and scale of

low-fare carriers. Low-fare carriers have,

to some extent, avoided complicated busi-

ness processes. They started from scratch

and are primarily focused on keeping costs

low (in comparison with the traditional

airlines) to allow low fares to be offered in

the market which significantly undercut

the established airlines, and yet still allow

Table 1: Example of fare levels by booking class and rules and restrictions

Booking class Fare Refund Rebook Min stay* Adv. purchase

Y 499

C 299 No

D 259 No Fee 50

R 199 No Yes

M 149 No No Yes 7 days

Q 119 No No Yes 14 days

W 99 No No Yes 21 days

*Can only be enforced in the case fares are not really offered as one-way fares.

Pa

Weste

8/9/2019 Micro 1.2 pdf

4/18

the low-fare carrier to be profitable.

Owing to their focus on costs and the fact

that some of them reduced their service to

very basic levels, terms such as low-cost

carriers (LCC) or no-frills carriers are com-

monly used. One part of their strategy isto reduce their network to simple point-to-

point traffic in domestic markets. Until

today, the majority of LCC have ignored

or have not specifically attempted to

exploit connecting traffic and the compli-

cated and therefore more costly interconti-

nental markets.

The LCC introduced a pricing model

that segments the market primarily by

charging different prices at different times

before departure. Within the family oflow-fare carriers, varying levels of sophisti-

cation exist on how to set the price level.

Another major difference from the tradi-

tional carriers is that they usually offer one-

way fares and therefore, by definition, do

not enforce any minimum stay condition.

As the low-fare carriers are primarily tar-

geting price-sensitive customers, such seg-

mentation of demand is, from their point

of view, not necessary. For a traditional

carrier, however, the minimum stay

restriction represents the most important

and effective segmentation criterion to

separate two groups of passengers busi-

ness and leisure with significantly differ-

ent consumer characteristics.

Increasing competition due to the high

number of low-fare carriers leads to the

prediction that these low-fare carriers will

eventually start looking into more sophisti-

cated ways of selling seats.

DESCRIPTION OF PROBLEM

Failure of the traditional revenue

management model

The traditional revenue management

model worked well as long as all players

were following the same rules. Traditional

network carriers, however, are now faced

with an increasing number of low-fare

markets defined by simplified fare

concepts primarily focusing on adapt-

ing price over time (undifferentiated fare

structures).

Undifferentiated fare structures

In this context, the term undifferentiated

has to be understood as a concept that does

not use explicit restrictions to achieve price

differentiation inside of the same passenger

segment. This means multiple booking

classes exist on the same O&D, which

differ only by fare level and do not have

different restrictions assigned.

It does not mean that there are no

restrictions at all. If these restrictions are

identical for multiple booking classes, how-

ever, only the fare makes the difference for

the consumer. From this perspective, these

classes are undifferentiated inside their

passenger segment. A simple example is

given in Table 2.

Table 2: Example of an undifferentiated fare structure

Booking class Fare Restrictions Comment

Y 499 None Fully flexibleC 299

D 259 Not refundable Optional list of fares for

R 199 Rebooking fee a passenger segment

M 149

Q 119

W 99

age 392


8/9/2019 Micro 1.2 pdf

5/18

Incorrect assumption of independence of

booking classes

The pricing and revenue management pro-

cesses and tools in place at traditional car-

riers have been designed to work with a

differentiated fare structure. In fact, theyrequire such a structure to be able to fore-

cast and optimise properly.

Because the traditional forecasting and

optimisation algorithms assume indepen-

dence of booking classes enforced by rules

and restrictions, these two key components

of the revenue management systems do not

produce appropriate results in undifferen-

tiated markets.

Demand forecastingThe top chart in Figure 2 illustrates the

impact of undifferentiated pricing on fore-

casting demand per booking class. At

lower fares, the observed demand is at least

the same as or higher than that observed at

the next higher fare level, because a consu-

mer who accepts a specific price will cer-

tainly always purchase the same product at

any available lower price. The traditional

forecaster algorithms originally implemen-

ted and still primarily in production today,

however, assume no dependency between

passenger demand in the various booking

classes, and forecast each class separately, asshown in the lower chart.

Revenue optimisation

Even if the forecaster is able to consider

this interdependence of demand correctly,

a traditional revenue management optimi-

ser would still not function in an appropri-

ate manner, because the optimisers would

still offer seats to lower fare classes because

of the implicit assumption that higher fare

demand does not book down into thelower fare classes. Without any restriction

in place, however, in reality a consumer

will always purchase the lowest available

fare.

The airlines have been aware, even

before the massive introduction of undif-

ferentiated fare structures, that the assump-

tion of class independence of demand was

In case of non-segmented demand a bookingclass is just a different price and therefore thedemand at a lower fare always includes thedemand at higher fare

Demand

CC$ 299$ 299

DD$ 259$ 259

RR$ 199$ 199

MM$ 149$ 149

QQ$ 119$ 119

In case of a traditional fare structure eachbooking class is an independent productwith its own demand

Demand

CC$ 299$ 299

DD$ 259$ 259

RR$ 199$ 199

MM$ 149$ 149

QQ$ 119$ 119

Figure 2: Impact of undifferentiated fare structures on demand per booking class

Pa

Weste

8/9/2019 Micro 1.2 pdf

6/18

artificial and not really appropriate. There

have been several approaches in previous

years to consider buy-down and/or sell-up

in revenue management algorithms. The

current revenue management systems,

however, were successful in generatingincremental revenue even under this

assumption of independent demand. Con-

sequently, there was never sufficient eco-

nomic pressure to get the attention of the

airlines to address this demand assumption.

Other aspects of the revenue management

problem (for example O&D forecasting)

received higher attention by the airlines

and by the various vendors of airline rev-

enue management products.

Spiral-down effect of total revenue

Because a revenue management forecaster

uses historical observations concerning the

number of bookings by class, and the opti-

miser uses the forecasts to determine the

number of seats to be allocated to each

class, the effects described above result in

an inevitable spiral-down of total revenue

in a market with undifferentiated fares.

The buy-down behaviour leads to more

bookings in lower classes than expected by

the forecaster. By considering this shift in

the demand, the next forecast loop would

predict more demand in lower classes and

less demand in higher classes, which

would make the optimiser allocate more

seats to the lower class and protect fewer

seats in the higher class. This process

would repeat itself until it reached a situa-

tion in which all the seats were being

offered and sold at the lowest undifferen-

tiated fare level.

Observation capabilities of current

revenue management systems

The problem for globally operating net-

work carriers is even more complicated,

because they simultaneously serve domestic

point-to-point traffic, connecting continen-

tal traffic and intercontinental markets.

Therefore they may be confronted with

undifferentiated as well as differentiated

markets across their network.

The majority of all revenue management

systems in production are leg-based or seg-

ment-based systems. They do not storePNR information or booking counts by

markets. From the historical booking data

stored in the revenue management system,

the airlines are typically unable to identify

an undifferentiated market from a differen-

tiated market as long as the booking is

done in the same class on the same leg or

segment. Other indicators in the booking

data have to be used by a forecaster to

identify whether a booking has been made

based on one fare structure or another. Sol-ving the spiral-down effect without having

the capability of observing how much of

the business is being effected by the buy-

down behaviour may only be possible as

an approximation.

Some network carriers have moved to

true PNR-based revenue management

systems. They have the capability of separ-

ating the two different market types,

because they store the passenger demand

information at a very detailed level (Figure

3). Although these carriers have primarily

implemented such systems to solve the net-

work optimisation problem, they can use

them as the basis for addressing the revenue

management forecasting and optimisation

Undifferentiated

Undifferentiated

Dem

and

Dem

and

AA

BB

CC

DD

O&D Booking Class

Differentiated

Differentiated

Demand

Demand

A-Pax

B-Pax

C-Pax

D-Pax

AA

BB

CC

DD

Figure 3: Storage of booking data by market in

O&D revenue management systems

age 394


8/9/2019 Micro 1.2 pdf

7/18

challenges in undifferentiated fare structure

environments.

Inventory control of undifferentiated fare

structures

Control of inventory, especially in legacydistribution channels (such as the GDS) is

performed by booking class seat alloca-

tions. Each booking class on a flight seg-

ment level has a seat availability assigned,

either numeric (09 seats) or non-numeric

(eg A=available, C=closed). In the case

of undifferentiated fare structures, how-

ever, a booking class has to be understood

as a possible price step in a range of fare

levels that may apply at different times

during the booking period of the flight.The lowest available class therefore repre-

sents the current price available to the con-

sumer. Referring to the example from

above, a price of 199 in the respective pas-

senger segment could appear as shown in

Table 3.

Under the assumption that an airline

would be able to forecast non-independent

passenger demand and optimise seat alloca-

tions appropriately, the problem remains

of how to control the sale of seat inven-

tory. The traditional static class allocation

in the GDS also does not support separat-

ing an undifferentiated market from a

traditional market. Inventory controls are

segment based, and therefore they can only

play one role at a time. They can be used

either to control the best current price for

the undifferentiated market or to control

the booking class availability of differen-

tiated markets. But they can never fulfil

both purposes at the same time, which isillustrated by the example in Figure 4.

The simple network in Figure 4 consists

of two legs and three O&Ds. Only three

booking classes are used (A, B and C) in all

markets. The markets AAACCC and

BBBCCC are traditional markets in

which the classes have very different

restrictions. Owing to low-fare competi-

tion on leg AAABBB, the carrier intro-

duced an undifferentiated structure so that

the three classes only differ by fare. There-fore, the lowest available booking class

defines the current available price.

Consider the scenario in which the com-

Conflict :C openAAA-CCC may displace BBB-CCC

howeverC closed Price is at uncompetitive $ 99

A = $ 159

B = $ 99

C = $ 59BBBBBB

AAAAAACCCCCC

A9B9

C9A9B

9C9

A9B

9C0

Figure 4: Example of inventory control problem

Table 3: Mapping of current price to booking class availability

Booking class Fare Non-numeric

availability

Numeric

availability

Restrictions Comment

Y

C

D

R

M

Q

W

499

299

259

199

149

119

99

A

A

A

A

C

C

C

9

9

9

9

0

0

0

None

Not refundable

Rebooking fee

Fully flexible

Optional list of

fares for a

passenger segment

Pa

Weste

8/9/2019 Micro 1.2 pdf

8/18

peting low-fare carrier has $59 as the cur-

rently available fare, and the network car-

rier wants to match this fare level.

From the traditional airline perspective,

the decision to match the $59 fare level

means that the airline must open C class onleg AAABBB.

In the scenario in which high demand

exists on the long-haul leg BBBCCC, the

network carrier may want to protect this

leg against connecting traffic from AAA

CCC in C class. Therefore, the network

airline would really like to keep C class

closed on leg AAABBB.

This creates a clear conflict between two

business strategies. As class C has been

opened to ensure a price of $59, the con-necting traffic from AAACCC in class C

may now displace BBBCCC traffic.

It is recognised that, in this scenario,

O&D-based control techniques offer a

solution to the problem. Using a realtime

evaluation enables the separation between

undifferentiated and differentiated markets

on the fly whenever an availability or

booking request is received. Markets for

which undifferentiated fare structures are

being used are identifiable by their O&D.

As a consequence, the O&D control cap-

able airline can treat these cases in the

appropriate way.

Figure 5 shows the difference between

offline and realtime inventory control. In

the left-hand diagram, the offline aspect isoutlined. The airline calculates class avail-

ability using a revenue management system

and sends the results to the distribution sys-

tems in advance. These numbers are used

to display seat availability until the airline

sends updated availability figures. In the

scenario of no fences, the undifferentiated

demand will buy-down to the lowest avail-

able class. In the right-hand diagram, each

individual request will be passed on to the

airline for evaluation purposes. This allowsthem to reply to such a request in the best

way.

(REALTIME) DYNAMIC PRICING

Increasing dynamics and competition in the

airline industry demand a faster and more

flexible way of distributing prices to the

end consumer. The pressure, however, has

never been high enough to force the tradi-

tional carriers away from their relatively

static traditional pricing process, which

Leg-based Off-Line Control

AA BB CC DD

RM SystemBooking Class

AvailabilityAirline

Airline

Offline Process

Undifferentiated tends to bookdownlimited control

O&D-based Real-Time Control

AA BB CC DD AA BB CC DD

O&D optimalClass Availability

Low Fare competitiveClass Availability

Airline

Airline

Real-Time Process

Undifferentiateddemand

Differentiateddemand

?

Request

Reply

Figure 5: Offline versus realtime inventory control

age 396


8/9/2019 Micro 1.2 pdf

9/18

pre-defines products and prices and pro-

vides this information in advance to the

various distribution channels. The pro-

blems described in the previous sections

now dramatically change the business situa-

tion, as the traditional carriers will eitherlose market share or control over their

inventory in the event that they do not

adjust their forecasting and optimisation

algorithms and procedures. The section

below describes how a dynamic pricing

concept fits into the requirements of var-

ious airline types, ranging from pure low-

fare carriers to network carriers facing

low-fare competition. It attempts to

explain how such a concept addresses the

challenges of undifferentiated fare struc-tures and ensures that revenue management

continues to function appropriately.

Future-pricing model for airlines:

Willingness to pay

The future-pricing concept is constructed

on the assumption that different segments of

passengers exist with a different willingness

to pay, which can and need to be identified

at the time of purchase. In theory, there

could be many customer segments, as long

as unique distinctions can be defined which

identify each segment from the others, and a

group of customers is formed with a differ-

ent willingness to pay.

The grouping by willingness to pay is a

very important characteristic, which needs

to be enforced by the segmentation criteria.

In the scenario in which segmentation isweak or even artificial and does not sepa-

rate groups of customers with different

willingness to pay, all consumers will

inevitably buy-down in all situations in

which multiple prices are offered simulta-

neously. Compared with a traditional pri-

cing structure, which consists of many

different products, the number of customer

segments defined for such a concept will be

kept small (eg 23).

For each of the passenger segments, therewill be a range of prices out of which the

optimal price needs to be determined.

Depending on the price level and the

volume of passengers willing to accept the

price changes, the higher the price, the

lower the number of customers willing to

accept that price. Conceptually, this can be

understood as: the volume of passengers

multiplied by the respective price defines

the expected total revenue for the airline.

As shown by Figure 6 the price that results

in the highest total revenue should be

charged to that passenger segment.

025

5075

100125150175200225250

39 69 99 129

159

189

219

249

279

309

339

369

399

429

459

489

519

549

579

Fare

ExpectedDema

02000

4000

6000

8000

10000

12000

14000

16000

18000

Total

ExpectedRevenue

Demand

Total Rev

Optimal Price 139

sell 112 Seats

Figure 6: Pricerevenue curve in monopoly situation and unlimited capacity

Pa

Weste

8/9/2019 Micro 1.2 pdf

10/18

In a scenario in which there are more seats

available than demand at the optimal price,the price should not be reduced to stimulate

additional demand. Figure 7 shows that the

total revenue will reduce, because stimula-

tion can only be achieved with an over-pro-

portional price decrease. Conversely,

however, under capacity constraints, the

price needs to be increased to throttle down

demand. The proper amount can also be

picked from the curve by simply finding the

price point that represents the highest

possible volume still fitting in the available

capacity.

Over time, closer to departure the will-

ingness to pay usually increases. This phe-

nomenon is comparable with the

willingness by people to accept higher prices

for Christmas gifts the closer the holiday

period comes. The risk of ending-up with-

out a gift supersedes the disutility of paying

a higher price. This behaviour can also be

observed in the airline industry. The need to

be at a destination on an agreed date is

higher the closer the agreed date is fromtoday. This results in different demand

curves for the same passenger segment for

the same flight event over time (Figure 8).

The examples above are simplified as

they try to illustrate the concept of dyna-

mically defining prices based on willingness

to pay. The examples ignore forecasting

uncertainties and assume a monopoly situa-

tion and therefore are not meant to replaceany more scientific algorithms.

Low-fare carriers in principle apply an

approach based on willingness to pay,

although some may use simple rules to

determine the current price. The important

concept is that the LCC change price over

time for a particular product. They do not

define multiple products with unique

prices and then decide on a mix of these

products to be offered.

Monitoring the competition

The world of dynamic pricing is different

from traditional pricing in that the focus is

primarily on defining the optimal price

and making this best price available to the

market place.

The consumer willingness to pay,

under the assumption that the consumer

has certain knowledge about all prices, is

heavily dependent on the alternative travel

options available. Therefore, it is a must in

this environment to monitor the prices ofthe competition constantly and include this

information when determining the current

price.

Definition of realtime dynamic pricing

Dynamically changing prices does not

necessarily mean it is done every few sec-

0255075

100125150

175200225250

39 69 99 129

159

18921

924

927

930

933

936

939

9429

459

48951

954

957

9

Fare

ExpectedD

emand

0200040006000800010000

12000140001600018000

Tota

l

Expected

Revenue

Demand

Total RevReduced Capacity 60

increase to new

Optimal Price 229

Any Capacity above

Optimal Capacity 112

leave at Optimal Price 139

Figure 7: Price determination under capacity constraints

age 398


8/9/2019 Micro 1.2 pdf

11/18

onds. Some carriers may find it sufficient to

change a price according to simple business

rules such as fixed days before departure or

when certain booking thresholds have been

reached.

The highest level of flexibility and

sophistication is reached when a carrier is

in the position to adjust prices dynamically

based on an individual market situation or

tactical decisions even in a GDS without

having to distribute new fares.

Such enhanced usage is best described by

the term realtime dynamic pricing

(RTDP), as it contains the key components

in a comprehensive way.

. Realtime: The prices are calculated and

distributed online and in realtime to

consumers on all major distribution

channels to allow the most flexible

approach.

. Dynamic: The decision of which price to

display to the consumer is dynamically

influenced by the availability of seats,

the expectation of competing demand

and its willingness to pay, the prices of

competitors, alternatives for the

consumer and other relevant and obser-

vable criteria. The logic to come up

with the dynamic price can vary from

basic to highly sophisticated.

. Pricing: The revenue management and

pricing departments focus much more on

the price at a particular point in time

rather than number of seats to be offered

for a pre-defined price. Therefore pricing

plays an even more important role andintegrates its decision process very closely

with revenue management.

Realtime dynamic pricing components

and methods

A RTDP solution comprises the following

components and methods:

0

25

5075

100

125

150

175200

225

250

39 59 79 99 119

139

159

179

199

219

239

259

279

299

319

339

359

379

399

419

439

459

479

499

519

539

559

579

Fare

ExpectedDemand

0

1800

36005400

7200

9000

10800

1260014400

16200

18000

Total

ExpectedRevenue

Dem(t0)

Dem(t1)

Rev(T0)

Rev(t1)

Assumption : Willingness-to-pay increases from t0 to t1 whiletotal demand-to-come decreases closer to departure (t0 to t1)

Maximum Revenue

at t0 Maximum Revenue

at t1

Optimal setting at t0 Sell 112 at 139

Optimal setting at t1 Sell 81 at 289

Explanation : Dem(t0) = Unconstrained demand to come per price level between t0 and t1Dem(t1) = Unconstrained demand to come per price level between t1 and t2

Figure 8: Willingness to pay changes over time

Pa

Weste

8/9/2019 Micro 1.2 pdf

12/18

(1) forecaster algorithm to enable the calcu-

lation of unconstrained forecasts by

considering potential dependencies

between booking classes

(2) pricing optimisation to determine the

optimal price based on forecasteddemand patterns of the various

passenger segments and their willing-

ness to pay

(3) optimiser algorithm considering price

steps (not prices) and corresponding

volumes competing with traditional

segmented booking classes in mixed

markets

(4) integrated pricing information to

enhance price optimisation by

including passenger choice models,competition information and other

pricing relevant information.

(5) dynamic price engine for realtime and

context-based evaluation of avail-

ability, sell and cancel requests from

various distribution channels such as

GDS, web portals and direct reserva-

tions system terminals (ATO/CTO

and call centres)

(6) reservations control procedures, in order

to guarantee that bookings correspond

to the dynamic price offer, eg enfor-

cing ticketing deadlines.

GROUPING AIRLINES BY FARE

STRUCTURES

As mentioned above, not all carriers have

the need to implement dynamic pricing in

a realtime mode. The identification of

what a carrier needs to handle undifferen-

tiated fare structures first requires a look at

how their business model is being impacted

by such fare structures. Based on thisreview, together with current revenue

management capabilities, a concept for a

solution can be defined. The level of

sophistication of a concrete implementation

can, of course, vary.

In principle, four groups of airlines have

been identified in respect of how they

adjust their business model as a result of

the low-fare challenge and, in addition,

how much they wish to make use of the

opportunity of being able to perform

dynamic pricing in a competitive airline

environment.Depending on the situation, these airlines

have to solve different revenue manage-

ment problems. The four groups are

explained in more detail below and in

Figure 9.

Group 1 includes airlines applying the

low-fare business model across the entire

network. They do not publish different

fare products but change the price for a

flight over time. The revenue management

problem for them can be defined as thetask to calculate:

. the optimal price at a particular point in

time in order to maximise total revenue.

This pure low-fare carrier model can be

expanded by splitting up the demand into

multiple segments (eg by distribution chan-

nel) and starting to charge individual prices

for each of these segments. The key is that,

for each segment, only one price is being

offered at a particular point in time.

Group 2 includes network carriers that

responded to a low-fare challenge in the

respective competing markets with an undif-

ferentiated fare structure without being able

to differentiate them from the traditional

markets while using leg/segment-based rev-

enue management. In the low-fare markets,

this may result in the spiral-down effect as

described earlier. Attempts to avoid the

spiral-down effect may have a negative

impact on traditional booking streams acrossthe network owing to the limited inventory

control capabilities of such carriers.

The problem to solve here is:

. how many seats to assign to each booking

class in order to maximise total network

revenue and at the same time avoid the buy-

age 400


8/9/2019 Micro 1.2 pdf

13/18

down behaviour in the undifferentiated

markets.

Maximisation of revenue may become a

theoretical goal here, as the carrier is facing

conflicting results when it comes to con-

trolling the inventory. Therefore, in prac-

tice, only an improvement in the current

situation is possible compared with using

algorithms based on the incorrect assump-

tion of booking class independence.

Group 3 represents the network carriers

that did not react to the low-fare competi-

tion by matching their model completely

in these markets. Aware of the lack of con-

trol the new price models create when

using traditional revenue management sys-

tems, some carriers, such as Lufthansa, at

first limited themselves to a few booking

classes, which they only published in therespective low-fare markets.

This requires a different revenue man-

agement problem to be solved. Revenue

management has to determine:

. in order to maximise total network revenue,

how many seats to assign to each of the

traditional fare classes, how many seats to

assign to the low-fare market and at which

current optimal price they should be sold.

Group 4 includes the sophisticated net-

work carriers applying an O&D control

mechanism. They are able to differentiate

and maintain both business models across

their network in the various markets as

required. This enables them to match fully

the low-fare business model in the compet-

ing markets.

Their revenue management problem

looks like this:

. in order to maximise total network revenue,

how many seats to assign to each product in

the traditional markets, and what optimal

price to charge for each segment in a

dynamic priced market.

Group 4 can apply the full scope of RTDP

capabilities to their undifferentiated busi-

ness or any other market they feel appro-

priate. At the same time, they are in a

position to achieve additional revenue

benefits by making use of sophisticated

AA

BB

CC

DD

A-Pax

B-Pax

C-Pax

D-Pax

How many mixed A-D tomaximise total revenue

ANDavoid buy-down?

A-Pax

B-Pax

AA

BBFare

Restriction

How many A, B?How many for step price

ANDat what price?

A-Pax

B-Pax

C-Pax

D-Pax

AA

BB

CC

DD

O&D Control

How many A -D?

At what price?

?

12

43

At what price(s) ?

Figure 9: Four variations of the revenue management problem

Pa

Weste

8/9/2019 Micro 1.2 pdf

14/18

O&D revenue management in their tradi-

tional markets.

Approaches to a solution per group

As there are four generic revenue manage-ment problems to be solved, different

approaches to the problem are required.

Similarity exists between the problems,

however, so that a part of the solution may

be applicable to each of the business

models. Primarily, the revenue manage-

ment, pricing and reservations system are

affected. To enable the realtime part of the

evaluation, a new dynamic price engine

needs to be provided. The following para-

graphs describe solutions for each of the

groups.

Pure dynamic pricing

This assumes that the airline is a Group 1

carrier and applies step pricing across its

entire network.

In case the airline wants to go beyond

manual overrides or a rule-based approach,

an enhanced revenue management forecas-

ter needs to estimate the number of passen-

gers willing to pay the various price levels

at a particular point in time before depar-

ture. Such a forecaster estimates demand

for each booking class by considering their

interdependencies. In cases where multiple

passenger segments are used, observing

occurrence and estimating demand at each

of the passenger segments and its price

levels are required.

A pricing optimisation module calculates

the price level, which maximises the total

revenue of the airline. When demand is

being split into two or more segments, adifferent valid price for each of them has to

be determined by the system.

In the case where a carrier distributes via

the GDS, the price(s) must be converted

into the respective booking class availabil-

ities and sent to the GDS by an AVS/AVN

message.

Mixed business model for leg-based

carrier

For airlines using a leg-based revenue man-

agement system but applying both business

models in different parts of their network

(Group 2), the situation becomes muchmore complicated. As a booking class

could contain both types of customer seg-

ments (undifferentiated fare customers and

traditional differentiated fare customers),

the class seat allocations need to be calcu-

lated in a way that considers buy-down

behaviour of parts of the passenger

demand. To be able to perform such a cal-

culation, the forecaster needs to alert the

optimiser as to how much of the demand

in a booking class is undifferentiated andwhich may buy-down if a lower fare pro-

duct is available. To ensure best possible

results, frequent recalculations and inven-

tory updates have to be done in this envir-

onment.

As for Group 1, standard leg/segment

class availability controls the inventory.

Dynamic pricing using special booking

classes

Group 3 includes some of the major Euro-

pean traditional carriers. In responding to

the low-fare challenge, they often steer the

low-fare classes with a rule-based concept

in a more or less manual and static way.

To improve this approach, some

enhancements to the current revenue man-

agement system are necessary.

For the undifferentiated classes, the fore-

casting problem is identical to the pure

dynamic pricing scenario described for

Group 1. In this case, however, the differ-

entiated traditional booking classes alsoexist and compete for seats on the same air-

craft. The optimisation step has to be a

combined logic, considering the demand

and values of the traditional classes, and at

the same time include the optional prices

for the undifferentiated classes and their

associated forecasted demand. Based on

age 402


8/9/2019 Micro 1.2 pdf

15/18

8/9/2019 Micro 1.2 pdf

16/18

undifferentiated markets by dynamic

pricing and revenue maximisation by

O&D revenue management).

SOME WORDS ABOUT COSTSIn commodity markets, the manufacturer

with the lowest unit cost of production

tends to be the long-term survivor. Con-

sequently, in the airline environment in

which many of the competitors are deliber-

ately emphasising the commodity nature of

an airline seat, very strict cost control is

vital. Therefore, the total cost impact of

implementing a RTDP solution must be

considered. Two areas have been identified

as primary cost drivers.

Forecasting and optimisation

The costs of such modifications are primar-

ily driven by the level of sophistication and

are usually covered by a one-time imple-

mentation fee and some yearly main-

tenance cost. As most of the revenue

management systems are today running on

either Windows or Unix platforms, hard-

ware upgrade is not usually a significant

cost driver.

Inventory control

When distributing over a GDS, seamless

availability and other products are

required.

As GDS normally charge nothing or

relatively reasonable amounts for imple-

menting their additional products and ser-

vices, this is usually not an issue.

Depending on the airlines reservations

system and its capabilities, it may be neces-

sary to implement the realtime interfaces tothe GDS. Implementation cost could be

high, as most of these systems are main-

frame based (eg TPF, USAS). An alter-

native is to implement this interface as part

of the dynamic price engine, which would

reside on a modern platform such as

UNIX or JAVA.

For day-to-day usage, GDS and airline

reservations systems may either charge pre-

mium booking fees or a fee per message

sent (transaction based). The former is an

all or nothing option and the number of

messages does not really matter. In caseswhere a transaction based price model is

used, there are many options available to

the savvy airline to keep the volume under

control. For example, realtime evaluation

is only required for requests traversing a

market with undifferentiated fare struc-

tures. In addition, unproductive channels

producing massive numbers of low-fare

search messages should be provided with

standard availability. For them, realtime

evaluation can be limited to sell and cancelrequests.

CONCLUSIONS

Market segmentation by fare rules and

restrictions to gain additional revenue no

longer works efficiently in an undifferen-

tiated market, because revenue manage-

ment systems have been designed for a

different business model. The increasing

number of low-fare carriers forces tradi-

tional carriers in these markets to follow

their flexible pricing approach in order to

remain competitive. The failure of tradi-

tional revenue management under such cir-

cumstances has made some airlines revert

to manual control of their seat inventory

and question the function of revenue man-

agement.

Some top airline managers even explain

the current situation by stating that the tra-

ditional approach has always been the

wrong path. By also introducing simpli-

fied structures, their airlines are now realis-ing this and listening to the customers

needs.

Although there is some truth in such

statements, they are not fully describing

the economic competition situation. Tradi-

tional fare structures worked well as long

as all players used them, and they gener-

age 404


8/9/2019 Micro 1.2 pdf

17/18

ated a lot of revenue for the airlines over

the years. It is true, however, that the net-

work carriers have had to adapt and learn

from low-fare carriers about how to imple-

ment and control more rational pricing. In

addition, it must be recognised that thecurrent airline business models are not

static, and the LCC are themselves evol-

ving their business to extract revenue more

efficiently from each market.

To be able to support this, the network

airlines must adapt their traditional static

approach to pricing to become a dynamic

process. In addition, revenue management

and inventory control systems need to be

enhanced to produce correct seat control

results in these markets. As network car-riers are faced with a mixture of both pri-

cing models, their solution must be more

complicated. Some of them, however, can

build on highly sophisticated and existing

O&D revenue management systems. With

reasonable efforts, they can introduce an

RTDP concept. This will allow them to

introduce undifferentiated fare structures

flexibly without losing control over their

inventory. At the same time, they can con-

tinue to achieve additional revenue in their

traditional markets by applying their O&D

control concepts. This enables the opportu-

nity of moving dynamic pricing into

deregulated but still traditionally priced

markets.There are, of course, challenges to be

managed to achieve such a result. Solving

the forecasting and optimisation issues are

part of them but can be managed. In deed,

algorithms exist and have been implemen-

ted already at some carriers, with different

levels of sophistication.

The bigger challenge is how to adapt the

business processes and make sure that the

revenue management departments are

changing their mindsets to concentratemuch more on prices than on class seat

allocations. In addition, meaningful user

interfaces need to be provided to employ-

ees to allow them do a good job. Perform-

ance measures need to be adapted to ensure

that results are always monitored to allow

the identification of weak areas and to sup-

port a continuous improvement of the

work done by the revenue management

and pricing analysts.

Pa

Weste

8/9/2019 Micro 1.2 pdf

18/18

Documents

Micro 1.2 pdf