Real Life Applications in the Airline Industry

Real Life Applications in the Airline Industry

Revenue Management as a
Competitive Weapon:
Real Life Applications in the
Airline Industry
Sergio Mendoza Corominas, PhD
Gte de Distribución y Revenue Management
LAN Airlines
[email protected]
http://www.linkedin.com/in/smendoza
Translog
Transportation & Logistics Workshop
Reñaca, Viña Del Mar, Chile
December 9th, 2009
Abstract
LAN Airlines’ strategy for growth over the last decade has been based on the creation of a
multicarrier-multihub network, with a high proportion of cargo to total revenue, a quite
unique model in the airline industry.
This configuration has become increasingly complex, raising huge challenges on many
operational and commercial processes. Hence, the optimization of a network like LAN
Airlines’ requires highly skilled teams along with the most powerful tools and high levels of
coordination and automation.
As the network grew in complexity, the short term revenue optimization process became
one of the most challenged ones. Given the impact of this process on the bottom line of the
business, several years ago LAN Airlines decided to invest the necessary resources to
reach the forefront of the Revenue Management practice. At present LAN Airlines holds the
latest technology available for Revenue Management in the airline industry and is one of
the five most profitable airlines in the world.
In our presentation we will explain what we mean by Revenue Management in the airline
business and how the most advanced airlines practice the Revenue Management
discipline. We will share some real life examples and discuss some developments beyond
traditional Revenue Management.
2
Index
Brief
Overview of
LAN
Example 3:
RM and the
impact of
promotions
RM concepts
in the Airline
Industry
Example 2:
Value Based
Segmentation
Some latest
developments
Example 1:
Flexible
Redemption
3
Index
Brief
Overview of
LAN
Example 3:
RM and the
impact of
promotions
RM concepts
in the Airline
Industry
Example 2:
Value Based
Segmentation
Some latest
developments
Example 1:
Flexible
Redemption
4
LAN is among the passenger airlines with the largest % of
cargo revenues over total revenues
LAN
34%
3%
Korean Air
29%
12%
22%
Cathay
19%
Singapore
Air France-KLM
53%
6%
64%
59%
19%
12% 8%
6%
80%
86%
79%
81%
BELF w/o
Cargo
70%
Cargo
Contribution
BELF w/
Cargo
73%
21%
4%6%
90%
Delta 3% 9%
88%
American
11%
70%
11%
6% 16%
Iberia
BELF Differential for long haul passenger +
cargo routes (2009E)
60%
BA 7% 7%
Qantas
Passenger and Cargo Combination
– Lower break-even load factors
– Increased diversification
Load Factor
41%
EVA
Cargo
Others
Note: BELF = Break-even load factor
Passenger
Source: Companies - Last Full Year reported.
5
LAN has developed a diversified business model, with three major
revenue streams: Cargo, Cabotage and International Passenger
Diversified Business Model
(% Operating Revenues)
Jan -Sep 2009
Others*
4%
Domestic Passenger
26%
Cargo
24%
46%
International Passenger
* Other Revenues includes Aircraft Leases, Logistic and Courier, Ground Services, Storage & Customs Brokerage, Duty
Free, etc.
6
LAN’s passenger business is based on a multi-hub multicarrier model, which has leveraged regional growth
– Connected &
complementary hubs
Guayaquil
2003 y 2009
Lima
– Greater utilization of assets
1999
– Better use of traffic rights
– Domestic routes feed
international network
Santiago
1929
Buenos Aires
2005
An increasingly diversified passenger revenue stream has
helped the company overcome multiple external crisis
Passenger Capacity
(% ASKs)
1998
Jan-Sep 09
2003
Dom. Perú
Dom. Chile
Dom. Chile
3%
3%
20%
28%
39%
18%
72%
59%
Dom. Ecuador
Dom. Argentina
0.3%
Dom. Perú
8%
9%
Dom. Chile
14%
46%
23%
Regional
International
Regional
International
(Long Haul)
International
(Long Haul)
Growth in ASK (Jan-Sep09 vs. Jan-Sep08): +10%
International (Long Haul)
+ 4%
Regional
+ 6%
Chile domestic
+14%
Peru domestic
+23%
Argentina domestic
+66%
8
High utilization of Long Haul fleet increases return on assets
Boeing 767 Rotation
High utilization achieved through
aircraft rotation throughout the
region
Schedule
1. Night, Day 1
2. Morning, Day 2
3. Afternoon,
Day 2
Utilization:
13 hours/day
4. Night, Day 2
5. Morning, Day 3
6. Afternoon, Day 3
7. Night, Day 3
8. Morning, Day 4
9. Afternoon, Day 4
Lan Airlines
10. (Chile)
Night, Day 4
LanPeru
LanEcuado
9
Good world coverage through partners in passenger & cargo
networks
LAN is one of the leading passenger and cargo operators in Latin America
Toronto
New York
Amsterdam
Houston
Frankfurt
Los Angeles
Miami
Madrid
Cancun
Pta. Cana
Mexico City
Caracas
Merida
San Jose
Panama
Medellin
Bogotá
Quito
Guayaquil
LAN
Manaos
Codeshare
Piura
Chiclayo
Trujillo
Papeete
Easter
Island
Sydney
Auckland
Lima
Arequipa
Tacna
Arica
Iquique
Antofagasta
Calama
Copiapo
La Serena
Santiago
Concepcion
Temuco
Valdivia
Osorno
Pto. Montt
Balmaceda
Pta. Arenas
Alliances
Iquitos
Tarapato
Pucalpa
Puerto Maldonado
Cuzco
La Paz
Salvador
Belo Horizonte
Vitoria
Asunción
Rio de Janeiro
Sao Paulo
Porto Alegre
Salta
Curitiba
Iguazú
Montevideo
Buenos Aires
Rosario
Cordoba
Mendoza
Bariloche
Com. Rivadavia
Rio Gallegos
Ushuaia
Passenger + Cargo Network
Freighter Network
700 destinations
worldwide
10
LAN’s strategy has resulted in a strong revenue growth
Operating Revenues 1993 – LTM Sep 09
US$ Million
4.283
4.400
CAGR
20%
4.000
3.600
3.034
3.200
2.800
2.506
CAGR
1%
2.400
2.000
CAGR
24%
1.600
1.200
800
400
3.699
3.525
972
318 407
1.083
1.237
1.4251.428 1.454
2.093
1.639
600 694
0
1993 1994 1995 1996
1997 1998 1999 2000
2001 2002 2003 2004 2005
2006 2007
2008
IFRS
LTM
Sep
09
Note: 2008 and 2009 under IFRS; previous years under Chilean GAAP.
11
And consistent profitability despite multiple market shocks
Operating Income and Net Income 1993 – LTM Sep 09
Financial Crisis +
Salmon Crisis +
Swine Flu
Increasing
Fuel Prices
9/11 & Argentine Crisis
(US$ millions)
Recession
$620
650
600
550
$459
500
$413
450
400
$336
$308
$303
350
300
$241
$215
250
$172$164
200
$142$147
$112
150
$83
$84
$80 $64
$62
100
$44 $31 $51 $48
$46 $38
$48 $50
$34
$31
$25
$11
50 $11 $0 $15 $6
0
1993
1994
1995
1996
1997
1998
1999
2000
Operating
Income
2001
2002
2003
2004
2005
2006
2007
2008
LTM
IFRS Sep 09
Net
Income
LAN Airlines has been consistently profitable under the current administration
12
LAN Operates with High Efficiency Levels
EBITDAR Margin Industry comparison
30%
2 6 ,2 %
25%
2 2 ,5 %
2 2 ,9 %
14 ,8 %
14 ,4 %
15%
11,5 %
8 ,6 %
10%
5%
3 ,4 %
3 ,9 %
3 ,9 %
9 ,2 %
9 ,6 %
6 ,3 %
6 ,2 %
4 ,5 %
Source: Companies. Information for LTM September 09.
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pa
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ir
Ry
a
LA
N
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O
L
M
tb
lu
e
Je
TA
es
Co
t
nt
in
en
ta
l
Ko
re
an
Si
ng
ap
or
e
ut
hw
ay
s
So
Ai
rw
el
ta
U
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an
AF
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LM
U
ni
te
d
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Am
Br
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s
a
h
0%
Ib
er
i
Ebitdar Mg. (%)
2 0 ,4 %
19 ,0 %
20%
13
Index
Brief
Overview of
LAN
Example 3:
RM and the
impact of
promotions
RM concepts
in the Airline
Industry
Example 2:
Value Based
Segmentation
Some latest
developments
Example 1:
Flexible
Redemption
14
The dream that inspires Revenue
Management
What would we do if we had a crystal ball to:
anticipate how much demand we will have for a given
product/service, at current price, during the next
weekend?
anticipate how much will demand change if we
increase/decrease the price in x%?
anticipate my competitor’s reaction in the market?
anticipate how the exchange rate will continue to
fluctuate?
….etc
15
But demand is a stochastic process…this is
the first challenge of RM !
the future is stochastic, thanks God:
we have free will!
Though we would like a predictable
future, (Einstein never believed in) Quantum
Mechanics continues being the most accepted
and proven theory in Physics !
God doesn’t
play dice with
the universe
The universe is probabilistic, our future is
governed by stochastic phenomena that can’t
be predicted in a deterministic manner
16
Moreover, the airline business presents a
combinatorial challenge
On top of uncertainty, we face a huge number of variables for
which we could daily take relevant decisions:
>350 daily departures
X
365 days of future flights
X
> 2 relevant origin-destinations per flight
X
> 2 relevant markets per origin-destination
X
> 4 demand segments per market
¡¡Over 2.000.000 daily combinations!!
17
What is Revenue Management?
A core business discipline that
maximizes the profitability of assets through a
dynamic business process cycle for
demand forecasting,
price optimization and
the optimization of product availability
18
In the Airline Industry…
We maximize short term expected net revenues through:
(1) Modeling and segmenting the expected demand
(2) Defining competitive fare structures associated to the demand
segmentation
(3) Forecasting the demand
(4) Optimally assigning capacity to the expected demand of each
segment
19
This requires a robust and systematic
process
Max Expected Net Revenue
1. Load
database
2. Optimize
prices
Daily and
weekly
loading of
self and
market
information
(bookings, pr
ices, events,
seasons, etc)
Analysis and
modeling of
demand
Demand
segmentation via
fare restrictions
and value
attributes
Proactive pricing
Reactive pricing
Database
Administrator
Demand Analyst
Tariff Administrator
3. Forecast
Free demand at the
level of origendestination and
market, path, time
of day, fare class
Fares
Constrained
demand
Revenue
Flight Analyst
Demand Analyst
4. Optimize
availability
Compute optimum
filling of flights
Compute bid
prices for each leg
Load bid prices in
Reservation and
Distribution
Systems
Inventory control:
accept fares >= bid
prices
Flight Analyst
5. Diagnostic
Monitor
Diagnose
Adjust
Route Manager
20
2. Optimize
prices
In free markets there are several levels
of sophistication in pricing strategies
Innovative
Traditional
1
Pricing based
on market
price
2
Pricing based on
costs or pricing
by markup
Sophisticated
3
Pricing based
on demand or
willingness to
pay
4
Pricing based
on attributes or
value based
segmentation
5
Costs based
on prices
Description
Prece = price of main
competitor
Price =
dir var cost*markup
Price = willingness to Price = value of
Continuously
pay
bundled attributes or reduce price in
menu of attributes
order to assure
best price
Pros
Simple
Never noncompetitive
Simple
Assures profitability in
the transaction
Enables extraction of
consumer surplus
Enables demand
stimulation
Empowers the use of
customer databasis
and targeted pricing
Cons/risks
No demand stimulation
No surplus extraction
No differentiation
No positioning
No leadership
No assurance of
profitable transaction
No demand stimulation Might be not friendly
with customer
No surplus extraction
Risk of generating a
No differentiation
No useful if dir var cost is price umbrella
a low % of total cost (fex Invites competitors
SW, tickets, etc)
Enables extraction of
consumer surplus
Enables
differentiation
Friendly with
consumer
Assures competitivity
Forces innovation
Entry barrier
Loyalty
Strong demand
stimulation
Analytically, technically Might quickly
and communicationally eliminate competitors
complex
Commoditization of
product
2. Optimize
prices
Single tariff models are sub-optimal: they do not
reach all customers and they do not take
advantage of consumer surplus
“Traditional Pricing”
Demand
Unsatisfied demand
Demand Curve
Revenue = P * Q
Consumer surplus
Q
P
Price
Junio 2009
Price optimization is based on the fact that
demand is originated in a diversity of customers
2. Optimize
prices
Customers that travel for
leisure/tourism or visit relatives
Business customers of large
corporations
Busca flexibilidad
Compra a última hora y quiere
encontrar siempre un asiento
disponible (preferente)
Quiere acumular kms en un
programa de cliente frecuente
Permanece corto tiempo en
destino
Empresa paga el pasaje y tiene
presupuesto para pagar más a
cambio de todos estos beneficios
Flexibility
Large
businesses
Vacations
Visit
friends
and/or family
Customers that look for a
unique price opportunity
Business customers of small
companies
Flexibility
Small
businesses
Limited budget
Busca flexibilidad y conveniencia
Está dispuesto a pagar por
esto, pero tiene un presupuesto
limitado
El dueño de la empresa decide y
paga su pasaje
No buscan flexibilidad sino que
principalmente un buen precio
Ellos saben cuándo quieren
viajar, normalmente planifican con
tiempo Compran con anticipación
Permanecen en destino por lo
menos el fin de semana
Presupuesto bastante limitado
Opportunity
Stimulation
Impulsive
demand
Hay un considerable porcentaje
de clientes que no viajarían si no
fuese por una oportunidad única de
precio
Otro grupo que sí viaja
aumentaría su frecuencia de viaje
si encuentra buenas oportunidades
de precio
2. Optimize
prices
Segmentation is achieved by applying restrictions
that reflect and/or induce the behaviour of these
various types of customers
Each demand segment is offered an ad-hoc “fare product” built using “fare
restrictions”
Price/WTP
Advanced purchase
Length of Stay
Business
Altos
baja
corta
Ethnic
Bajos
>x días
Tourist
Bajos
>z días
>y
noche sáb
Fare restrictions applied to ethnic and touristic segments reduce revenue
24
dilution from business customers
Junio 2009
2. Optimize
prices
Based on these behavioral features we
build the differential fare structure
Fare
Class
Price
ADVP
Round
Trip?
Sat Night
Stay
% Non Ref
Y
$800
--
--
--
--
B
$475
3 días
Sí
--
50 %
M
$350
7 días
Sí
Sí
100 %
Q
$240
14 días
Sí
Sí
100 %
Business passengers that do not want to stay a Saturday night will
buy M or Q
The RM system protects demand in Y and B, but maintains classes
M and Q open without loosing revenue
A basic assumption in “classic revenue management” is the
independence of demand in different fare classes (segments)
2. Optimize
prices
“Differential pricing” allows us to
compound the airplanes with an optimum
mixture of fares
With “differential pricing” we reserve a
number of seats for each demand
“segment”
Seats
for
demand
stimulation
W%
Seats
tourits
X%
for
Seats for ethnic
customers
Seats
for
business
cutomers
Y%
Z%
Demand
stimulation
Demand
“Differential pricing”
Average Fare =
Z%*Pz + Y%*Py + X%*Px + W%*Pw
Revenue =
Z*Pz + Y*Py + X*Px + W*Pw
W
X
Y
Z
Pw
Junio 2009
Px
Py
Pz
Price
2. Optimize
prices
A robust reactive pricing improves our
competitive positioning
Basic rules of an adecuate reactive pricing:
1. Assure competitivity of bottom fares
Fare levels
Fare restrictions
In all distribution channels
In availability of inventory
2. Maintain the reactive pricing policies and the price match rules
updated and consistent
3. Monitor the competition
4. Minimize Time-To-Market
2. Optimize
prices
A smart proactive pricing ensures a good
“revenue share” in the market, enhancing
profitability
The basic rules of a robust reactive pricing process:
1. Define balanced fare differences
2. Define fare restrictions that segment effectively
demand, taking into account the competitive situation
the
3. Implement promotional activities that stimulate demand in
depressed markets or low load factor flights
4. Periodically review fare class mapping, fare levels and fare
restrictions in order to always ensure a good revenue
generation
3. Forecast
Forecasts have two fundamental
objectives
1. Determine the optimum stock/availability for sale
Usually business customers buy very late, just a few days before departure, so if we knew
with certainty 5 business passengers will buy 3 days before departure wouldn’t we keep
those seats protected for them from being sold to leisure customers who are willing to pay
much less and buy much longer in advance?
2. Diagnose the future performance of routes in relation to expected
demand, fares, margins, etc, in order to take commercial and strategic
decisions that will improve the expected performance and increase
expected profitability
Thanks to forecasts we can drive the business “looking through the
windshield”, as opposed to “looking through the rearview mirror”
A 10% improvement of demand forecast errors induces a 1% improvement in net
revenues
Forecasts should reflect
decisions taken
expected reality given actions implemented and
3. Forecast
Some features of the forecasts
Mathematical models:
Bayesian models (good for small integer numbers)
Forecast achievable demand/bookings at day of flight, by fare
class, O&D, path, point of sale (POS), time of day
Forecast constrained demand by flight
Forecast show-up rate
Forecast cancellation rates
Using:
Seasonality
Holidays
Special events
Influences
etc
3. Forecast
Forecasts present many limitations and
challenges
Information of competitors not directly incorporated
Codeshare demand
Sudden/unplanned change of itineraries
Multiple causes of volatility
etc
Perform many readings before the flight
Frequently recalculate predictive models’ parameters
Continuously clean the history in database
Work flights in great detail
etc
4. Optimize
availability
What are the OD’s and fares (classes) we should accept at every moment in order
to maximize the expected net revenue over the network?
Displacement cost:
Revenue we do not collect due to
accepting a passenger in a given path
Tokyo
Los Angeles
San Francisco
For ex: a Los Angeles-BsAs pax might
displace a Los Angeles-Lima pax
We should accept a pax in the network paying
a net fare if this net fare is greater than all the
displacement cost (“bid price”)
Quito
Lima
Competing passengers:
Long Haul – Low Yield
vs
Short Haul - High Yield
Buenos Aires
Santiago
Junio 2009
4. Optimize
availability
LAX
LA 600
BP = US$500
US$ 799 > BP = US$ 700
> US$ 699
LIM
Fares EZE-LAX
US$ 799
US$ 699
US$ 599
Fare Classes
B
M
Q
LA 2428
BP = US$200
EZE
Bid Price EZE-LAX = US$ 500 + US$ 200 = US$ 700
We would only show “B” class open and thus, customer will have to pay US$ 799 for a
seat in EZE-LAX. The RM system associates a “Fare Value” to each fare class, which
corresponds to the net expected value associated to that fare class:
Farevalue (Clase=B, OD=XY, Routing =WZT) >= Bid Price => Fareclass “B” open
More than 50% of the benefit of RM in the airline comes from
a good demand segmentation via fare structures
Decomposition of potential benefits of RM in the airline business(1)
23-35%
15-20%
Pricing
16-23%
1-3%
5-8%
Capacity Management
1-2%
1-2%
Net
Incremental
Revenue
Segmentaction
via fare
restrictions
Reactive and
Proactive
Pricing
Availability
Optimization
(1) Data based on LAN’s research, PODS-MIT simulations and available literature
Overbooking
7-12%
Demand
Forecasting
34
Index
Brief
Overview of
LAN
Example 3:
RM and the
impact of
promotions
RM concepts
in the Airline
Industry
Example 2:
Value Based
Segmentation
Some latest
developments
Example 1:
Flexible
Redemption
35
1. The aggressive penetration of Low Cost Carriers with
simplified fare structures broke basic assumption of
demand independence
Few or no fences/fare restrictions:
“Classic revenue management” obsolete
Spiral down effect with classical forecasting algorithms
High revenue dilution (15% at least)
New way of forecasting demand and optimizing availability:
Formulate a sell-up model (negative exponential)
Compute willingness to pay (WTP)
Recalculate forecasts by fare level
Fare adjustment (K Isler & T Fiig, AGIFORS, Cape Town, 2005)
Simplified markets (ie unconstrained fare structures) imposed
a whole new challenge in forecasting and optimization
psup
Q −> f
= (1)
2
 fare f

1−
fareQ 

1− FRAT 5
Prob of Sell-up
Forecasts: Probability of sell-up / willingness to pay
Optimization: Fare Adjustment and Convex Hull
1
Higher
FRAT5s
0.8
0.6
0.4
0.2
0
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
Fare Ratio
Leg Buckets
Frontera Eficiente
Convex hull/efficient frontier
Unrestricted
Fare
Structure
Pj
2000
K
B
1500
Revenue
Restricted
Fare
Structure
H
Y
1000
500
Pi-Displ.
Pj-PEcost
0
0
5
10
15
Demanda
Buckets
20
25
30
2. We are trying to use competitive information in a
systematic way, but just matching availability isn’t good…
emsrb leg
davn path
davn leg
AL2 (O&D) matches AL3 (LCC)
davn path
davn leg
38
(1) PODS, May 2009. Network of 4 competitors, semirestricted
Closure Matching
Open Matching
AL1 (EMSRb) matches AL3 (LCC)
However, using competitor´s information to adjust
the forecast(1) shows promizing results
Fare Monitoring and Fare Publication
Pricing
DataBase
Lowest available
competitor fare
(1) PODS, Oct 2009
39
Index
Brief
Overview of
LAN
Example 3:
RM and the
impact of
promotions
RM concepts
in the Airline
Industry
Example 2:
Value Based
Segmentation
Some latest
developments
Example 1:
Flexible
Redemption
40
3. Our new FFP model(1) provides more transparency
and more alternatives for the customer
(1) Launched in August 2009
The new FFP business model makes every redemption
transaction accountable for its economic cost, using Bid
Price control
From a single price
level to a differential
pricing model
Fare table
[US$]
U2
Fare Value
U1
T3
T2
T1
Fare table
[kms]
U2
U1
T3
T2
T1
FFP request
Adjusted
Fare Value
>
Yes
Accept request
Bid Price
O&D RM
No
Reject request
Transfer economic cost to FFP
Immediate allocation of revenue for business unit
42
Results of the new FFP have been very compelling…
Increased
Revenue for
Business Units
FFP becomes
efficient
distribution
channel
Increased
Customer
Satisfaction
Increased
Revenues from
Partners
Automatic process, immediately allocates revenue to business units
Economic cost of seat automatically covered by algorithm
RM has incentive to help the program
Higher demand because of higher flexibility of FFP
Higher average fare because of fare mix
FFP becomes an efficient distribution channel
Sell very cheap when economic cost of seat is very low
Sell expensive when economic cost of seat is high
Efficient and effective way for demand stimulation
Higher possibilities to redeem in full flights
More overall seats available
Considerable growth in number of redeemed tickets and burned kms
Kms accrued became more valuable for customers
Increases long term loyalty (hopefully!)
More partners interested in our FFP
Willing to pay more for accrual in their businesses
More revenues for LAN
Index
Brief
Overview of
LAN
Example 3:
RM and the
impact of
promotions
RM concepts
in the Airline
Industry
Example 2:
Value Based
Segmentation
Some latest
developments
Example 1:
Flexible
Redemption
44
En the era of 0% comision, service fees and high
penetration of internet, the customer took
control…
Decisions are now...
Customer looks for...
Convenience
&simplicity
Free
Reliability
Transparency
Informed
Added value
Having control
If we did nothing...
Dilution
Confusion
Competitivity
loss
Traditional fare structures and display were for
experts...
FULL FLEXIBILIDAD
FAMILIA TARIFARIA
Tipo de Viaje
PROMOCIONAL
RT
RT
RT
RT
RT
Ida y regreso
Ida y regreso
Ida y regreso
Ida y regreso
Ida y regreso
BEEFF002
MEEFX003
KEEFX003
-
Anticipación de Compra (1)
-
Estadía Mínima
-
-
SEELE005
VEELE005
SEELE006
VEELE006
VEELE007
2 días
4 días
2 días ó 24 horas después de
hecha la reserva
4 días ó 24 horas
después de hecha la
reserva
2 noches ó 1
noche de
sábado
1 en cada sentido
Permite
Permite
Permite
$ 20.000
Permite
LEEFX004
MEEFX004
1 noche
Ilimitadas
(sólo
SUPER ECONOMICA
Ida
Anticipación de Reserva
Paradas Intermedias (Stopovers)
Combinaciones
dentro de misma familia tarifaria)
Cambio de Vuelo (2), de Fecha o de Ruta (3)
Cobro
Devoluciones (boletos vigentes) (6)
Cobro
Reserva de Asiento
ECONOMICA
OW
YEEFF001
HEEFF001
BASE DE TARIFA
FLEXIBILIDAD PROGRAMADA
NEESP005
SEESP005
NEESP010
SEESP010
NEESP011
NEESP012
SEESP012
SEESP013
SEESP007
SEESP017
QEESP008
QEESP014
QEESP015
QEESP016
-
21 días
7 días
24 horas después de la reserva
3 noches ó 1 noche de 4 noches ó 1
sábado
noche de sábado
5 noches
2 noches ó 1
noche de
sábado
24 horas después de la
reserva
1 noche de sábado
1 en toda la ruta
No permite
No permite
Permite
Permite
No permite
No permite
Permite
Permite
$ 20.000
Permite
Permite
$ 10.000 (4)
No permite
Permite
Según tabla (4) - (5)
No permite
No permite
No permite
Permite
No permite
No permite
(1): Anticipación de Compra: Para venta y origen de viaje en Punta Arenas, no se exigirá Anticipación de Compra.
Para venta y origen de viaje en Arica y Balmaceda, tarifas con TL de 24 horas, serán 72 horas después de la reserva.
(2): Cambio de Vuelo para el mismo día: Regulación aplica para reserva confirmada (respetando disponibilidad de clase).
Para todas las tarifas (aún cuando no lo permita), pasajero puede presentarse en el aeropuerto stand-by sin cobro.
(3) Reemisiones: Regulación aplica para familia tarifaria igual o superior, con boleto vigente (hasta 6 meses de emitido).
Desde 6 a 12 meses: Para categorías Full Flexibilidad multa 25% de la tarifa. Para otras categorías, 50% de la tarifa.
Después de 12 meses: No permite reemisión.
(4) Si cambio o devolución se realiza desde el día del vuelo en adelante, se cobrará $10.000 adicionales.
(5) Cobro para categoría Super Económica, depende del mercado:
PMCBBA, BBAPUQ y interregionales con fare basis QEESP008: multa $40.000.SCLCCP, SCLESR, SCLCPO, SCLZCO, SCLZAL, SCLZOS, SCLPMC y otros interregionales con fare basis N
SCLARI, SCLIQQ, SCLCJC, SCLANF, SCLBBA y SCLPUQ: multa $80.000.(6): Vigencia de los boletos: 6 meses desde su fecha de emisión.
Niños e Infantes con asiento: pagan el 67% de la tarifa Adulto. Infantes sin asiento: no pagan.
Estadía Máxima: 6 meses, desde la fecha de inicio de viaje.
Equipaje libre de cargo: 20 kilos en todas las rutas.
But we found that transparency and simplicity
induce revenue dilution (or “buy-down”)…
~10%
Buy-down
Customers have more free choices
However, not
everything is so
bad !
Demand goes where it wants to
We become more competitive
Flights get more balanced
Airline sells what RM made available for sale
Value Based Segmentation is a way to increase
voluntary “up-sell”
More attributes higher price
The customer, freely
informed, can decide
what available fare
family to buy, as a
function of the value
added attributes
…which imposes new challenges on us
What attributes to use?
Some attributes:
KMs LANPASS
Seat reservation
Preferent seats
Changes
Refunds
Preferential Check-in
Implicit or explicit?
Bundled or umbundled?
How to price these attributes consistently
with traditional pricing?
What fare differences are acceptable among
fare families?
2,4
óptimo
11,3%
12%
10%
1,6
8%
6,8% 7,4%
6,1%
6%
actual
1,2
0,8
3,7% 3,5%
4%
2,2%
2%
1,8% 1,6%
0,4
Diferencia de tarifa entre familias [US$]
60 - 65
55 -60
50 - 55
45 - 50
40 - 45
35 - 40
30 - 35
25 - 30
20 - 25
15 - 20
10 -15
0,0
5 - 10
0%
0 -5
% Upsell
2,0
y = 0,29e-0,24x
R² = 0,965
Ingreso de up-sell por trasacción [US$]
14%
La diferencia de tarifa actualmente
está definida en base a
segmentación tradicional, basada
principalmente en disposición a
pago por comportamiento de
consumo
Esta diferencia se reduce en el
tiempo a medida que se agota el
inventario de la familia inferior
Esto impacta directamente la
probabilidad de up-sell en el
tiempo
Discrete choice models based on “Random Utility
Theory”(1) help us with these challenges
Utility function of
the customer
K
U i = ∑ β k ⋅ X ik + ξ
k =1
U = Función de utilidad para alternativa i
β = Peso del atributo k
X = Valor del atributo k en la alternativa i
i
k
ik
Determine the β’s with
max likelihood from
surveys of discrete
choice
50
(1) Domencich & McFadden, 1975
Index
Brief
Overview of
LAN
Example 3:
RM and the
impact of
promotions
RM concepts
in the Airline
Industry
Example 2:
Value Based
Segmentation
Some latest
developments
Example 1:
Flexible
Redemption
51
In the onset of the crisis, demand stimulation became one
of the most important weapons for survival
The bad news:
Financial crisis
Swine flu
X Big fall in business traffics
X Big fall in touristic traffic
X Big fall in cargo demand, especially from the
salmon industry
Salmon crisis
The good
news:
Big fall of oil price
Opportunity for fare
reduction &dd stimulation
Opportunity for
renegotiating with
So we initiated some very aggressive promotions to
re-stimulate demand for the rest of the year…
…in international and domestic routes…
…taking full advantage of our product, our FFP
and our strong partnerships…
Promotional activities and demand stimulation are
essential to our commercial process, but are they
profitable?
The unknowns:
How profitable are price changes and promotions?
What are the right price levels?
How do substitue destinations interact?
How to optimally allocate our promotion budget per destination?
We have used econometric models based on simultaneous
equations to model the relationship between demand and
price
Q
Q
Historic data
P
Q
Intersection points
P
Q
Capacity curves
P
Demand Curves
P
These models help us understand sensitivity of demand
to price variations as a function of time to departure…
Daily demand vs Price as a function of time to departure
The functional form
∂Q
= − 0.0062 ∗ time to departure + ...
∂Price
Which means that every 30
additional days of anticipation
the price promotion will
produce 0.2 additional pax per
1UF (app 35US$) additional
price discount
…or the impact of the investment in the promotion
on demand, as a function of the anticipation of the
promotion…
Incremental sales as a function of expenditure in
promotion and anticipation (1)
80
Days before
departure
5 mo
Incremental sales
Venta Incremental (Pax)
[pax]
70
3 mo
60
2 mo
50
1 month
40
Saturation
30
15 days
20
10
Inversión Publicitaria Semanal (U.F.)
Weekly expenditure [UF]
(1) A 20% de descuento en precio
2,800
2,600
2,400
2,200
2,000
1,800
1,600
1,400
1,200
1,000
800
600
400
200
0
-
Price elasticities as funtion of the week of the year help us
decide when it is more convenient to start a promotion
Elasticity and Promotional Investment for the period 2006-2007
Inversión de A en destino M
Valor absoluto de elasticidad tprom4a_4uf
4
1200
3,5
1000
600
2
1,5
400
1
200
0,5
0
0
Semanas año 2006-2007
Inversión en U.F.
800
2,5
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
Elasticidad
3
Conclusions
Revenue management platforms and processes provide
considerable value to the airline business
Well used (best practices & innovation), RM becomes a
strategic weapon and a competitive advantage
The RM discipline is far from stagnant, we envision years of
interesting applied research and new developments that will
help the best practicing airlines maintain a profit advantage
Thank you!