Revenue Management as a Competitive Weapon: Real Life Applications in the Airline Industry
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
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
sergio.mendoza@lan.com
http://www.linkedin.com/in/smendoza
Translog
Transportation & Logistics Workshop
Reñaca, Viña Del Mar, Chile
December 9th, 2009Abstract
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.
2Index
Brief
Overview of
LAN
Example 3:
RM concepts
RM and the
in the Airline
impact of
Industry
promotions
Example 2:
Some latest
Value Based
developments
Segmentation
Example 1:
Flexible
Redemption
3Index
Brief
Overview of
LAN
Example 3:
RM concepts
RM and the
in the Airline
impact of
Industry
promotions
Example 2:
Some latest
Value Based
developments
Segmentation
Example 1:
Flexible
Redemption
4LAN is among the passenger airlines with the largest % of
cargo revenues over total revenues
EVA 41% 6% 53%
Passenger and Cargo Combination
– Lower break-even load factors
LAN 34% 3% 64% – Increased diversification
29% 12%
Korean Air 59%
BELF Differential for long haul passenger +
cargo routes (2009E)
Cathay 22% 19% 60%
Singapore 19% 11% 70%
11%
Load Factor
Air France-KLM 12% 8% 80% 81% 70%
BA 7% 7% 86%
Qantas 6% 16% 79% BELF w/o Cargo BELF w/
Cargo Contribution Cargo
Iberia 6% 21% 73%
American 4%6% 90%
Delta 3% 9% 88%
Note: BELF = Break-even load factor
Cargo Others Passenger
Source: Companies - Last Full Year reported. 5LAN 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
Cargo 26%
24%
46%
International Passenger
* Other Revenues includes Aircraft Leases, Logistic and Courier, Ground Services, Storage & Customs Brokerage, Duty
Free, etc.
6LAN’s passenger business is based on a multi-hub multi-
carrier model, which has leveraged regional growth
– Connected & Guayaquil
complementary hubs 2003 y 2009
Lima
– Greater utilization of assets 1999
– Better use of traffic rights
– Domestic routes feed Santiago
international network 1929
Buenos Aires
2005An increasingly diversified passenger revenue stream has
helped the company overcome multiple external crisis
Passenger Capacity
(% ASKs)
1998 2003 Jan-Sep 09
Dom. Perú Dom. Ecuador
Dom. Chile Dom. Argentina
0.3%
Dom. Chile 3% Dom. Perú
3% 8%
20% 9%
Dom. Chile
28% 14%
46%
72% 18% 59%
39%
23%
Regional Regional
International International
International
(Long Haul)
(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%
8High 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,
Utilization:
Day 2 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 LanPeru LanEcuado
10. (Chile)
Night, Day 4
9Good 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
Los Angeles Frankfurt
Miami Madrid
Cancun
Pta. Cana
Mexico City
Caracas
Merida
San Jose Medellin
Panama
Bogotá
Iquitos
Quito Tarapato
LAN Guayaquil Pucalpa
Manaos
Codeshare
Piura
Chiclayo Puerto Maldonado
Trujillo Cuzco
Lima La Paz Salvador
Papeete Belo Horizonte
Arequipa Vitoria
Easter Tacna
Island Arica Asunción
Rio de Janeiro
Iquique Sao Paulo
Antofagasta Salta Porto Alegre
Calama
Copiapo Curitiba
Sydney
La Serena
Auckland
Santiago Iguazú
Montevideo
Concepcion Buenos Aires
Temuco Rosario
Valdivia Cordoba
Osorno Mendoza
Bariloche
Pto. Montt Com. Rivadavia
Balmaceda
Pta. Arenas Rio Gallegos
Ushuaia
Alliances
Passenger + Cargo Network
700 destinations
Freighter Network
worldwide
10LAN’s strategy has resulted in a strong revenue growth
Operating Revenues 1993 – LTM Sep 09
US$ Million
4.400 4.283
4.000 CAGR
3.699
20% 3.525
3.600
3.200 3.034
2.800 2.506
2.400 CAGR
2.093
1%
2.000
1.639
1.600 CAGR 1.4251.428 1.454
24% 1.237
1.200 1.083
972
800 600 694
318 407
400
0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 LTM
IFRS Sep
09
Note: 2008 and 2009 under IFRS; previous years under Chilean GAAP.
11And consistent profitability despite multiple market shocks
Operating Income and Net Income 1993 – LTM Sep 09
Financial Crisis +
Increasing Salmon Crisis +
9/11 & Argentine Crisis Fuel Prices Swine Flu
(US$ millions)
Recession
650 $620
600
550
500 $459
450 $413
400 $336
350 $303 $308
300 $241
250 $215
$172$164
200 $142$147
150 $112
$80 $64 $83 $62 $84
100 $34 $46 $38 $44 $31 $51 $48 $48 $50 $31
50 $11 $0 $15 $6 $25 $11
0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 LTM
IFRS Sep 09
Operating Net
Income Income
LAN Airlines has been consistently profitable under the current administration
12LAN Operates with High Efficiency Levels
EBITDAR Margin Industry comparison
30%
2 6 ,2 %
25% 2 2 ,5 % 2 2 ,9 %
2 0 ,4 %
19 ,0 %
Ebitdar Mg. (%)
20%
14 ,4 % 14 ,8 %
15%
11,5 %
9 ,2 % 9 ,6 %
10% 8 ,6 %
6 ,2 % 6 ,3 %
3 ,9 % 3 ,9 % 4 ,5 %
5% 3 ,4 %
0%
h
a
an
L
ir
an
e
LM
pa
N
s
e
ta
d
l
t
M
ta
O
s
i
or
ay
es
na
lu
te
LA
er
el
TA
iti
ic
G
re
Co
en
-K
tb
ap
ni
rw
hw
Ib
a
D
er
Br
Ko
Ry
in
Je
U
AF
ng
Am
Ai
ut
nt
Si
So
S
Co
U
Source: Companies. Information for LTM September 09. 13Index
Brief
Overview of
LAN
Example 3:
RM concepts
RM and the
in the Airline
impact of
Industry
promotions
Example 2:
Some latest
Value Based
developments
Segmentation
Example 1:
Flexible
Redemption
14The 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
15But demand is a stochastic process…this is
the first challenge of RM !
the future is stochastic, thanks God:
we have free will! God doesn’t
play dice with
the universe
Though we would like a predictable
future, (Einstein never believed in) Quantum
Mechanics continues being the most accepted
and proven theory in Physics !
The universe is probabilistic, our future is
governed by stochastic phenomena that can’t
be predicted in a deterministic manner
16Moreover, 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!!
17What 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
18In 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
19This requires a robust and systematic
process
Max Expected Net Revenue
1. Load 2. Optimize 4. Optimize
3. Forecast 5. Diagnostic
database prices availability
Daily and Analysis and Free demand at the Compute optimum Monitor
weekly modeling of level of origen- filling of flights Diagnose
loading of demand destination and Compute bid Adjust
self and Demand market, path, time prices for each leg
market segmentation via of day, fare class Load bid prices in
information fare restrictions Fares Reservation and
(bookings, pr and value Constrained Distribution
ices, events, attributes demand Systems
seasons, etc) Proactive pricing Revenue Inventory control:
Reactive pricing accept fares >= bid
prices
Database Demand Analyst Flight Analyst
Flight Analyst Route Manager
Administrator Tariff Administrator Demand Analyst
202. Optimize In free markets there are several levels
prices
of sophistication in pricing strategies
Innovative
Traditional Sophisticated
1 2 3 4 5
Pricing based Pricing based on Pricing based Pricing based Costs based
on market costs or pricing on demand or on attributes or on prices
price by markup willingness to value based
pay segmentation
Description Prece = price of main Price = Price = willingness to Price = value of Continuously
competitor dir var cost*markup pay bundled attributes or reduce price in
menu of attributes order to assure
best price
Pros Simple Simple Enables extraction of Enables extraction of Assures competitivity
Never noncompetitive Assures profitability in consumer surplus consumer surplus Forces innovation
the transaction Enables demand Enables Entry barrier
stimulation differentiation Loyalty
Empowers the use of Friendly with Strong demand
customer databasis consumer stimulation
and targeted pricing
Cons/risks No demand stimulation No demand stimulation Might be not friendly Analytically, technically Might quickly
No surplus extraction No surplus extraction with customer and communicationally eliminate competitors
No differentiation No differentiation Risk of generating a complex Commoditization of
No positioning No useful if dir var cost is price umbrella product
No leadership a low % of total cost (fex Invites competitors
No assurance of SW, tickets, etc)
profitable transaction2. Optimize Single tariff models are sub-optimal: they do not
prices reach all customers and they do not take
advantage of consumer surplus
“Traditional Pricing”
Demand
Unsatisfied demand
Revenue = P * Q Demand Curve
Consumer surplus
Q
P
Price
Junio 20092. Optimize Price optimization is based on the fact that
prices
demand is originated in a diversity of customers
Customers that travel for
Business customers of large leisure/tourism or visit relatives
corporations
No buscan flexibilidad sino que
Busca flexibilidad principalmente un buen precio
Compra a última hora y quiere Ellos saben cuándo quieren
encontrar siempre un asiento viajar, normalmente planifican con
disponible (preferente) tiempo Compran con anticipación
Quiere acumular kms en un Vacations Permanecen en destino por lo
Flexibility programa de cliente frecuente Visit friends menos el fin de semana
Large Permanece corto tiempo en and/or family
businesses Presupuesto bastante limitado
destino
Empresa paga el pasaje y tiene
presupuesto para pagar más a
cambio de todos estos beneficios
Customers that look for a
Business customers of small unique price opportunity
companies
Hay un considerable porcentaje
Busca flexibilidad y conveniencia de clientes que no viajarían si no
Está dispuesto a pagar por fuese por una oportunidad única de
esto, pero tiene un presupuesto precio
limitado Otro grupo que sí viaja
El dueño de la empresa decide y Opportunity
aumentaría su frecuencia de viaje
Flexibility paga su pasaje Stimulation si encuentra buenas oportunidades
Small Impulsive
businesses
de precio
demand
Limited budget2. Optimize Segmentation is achieved by applying restrictions
prices 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 >y
Tourist Bajos >z días noche sáb
Fare restrictions applied to ethnic and touristic segments reduce revenue
dilution from business customers 24
Junio 20092. Optimize Based on these behavioral features we
prices
build the differential fare structure
Fare Price ADVP Round Sat Night % Non Ref
Class Trip? Stay
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 “Differential pricing” allows us to
prices
compound the airplanes with an optimum
mixture of fares
With “differential pricing” we reserve a
number of seats for each demand
“segment”
Seats for Seats for Seats for ethnic Seats for
demand tourits customers business
stimulation cutomers
Z% Demand
W% X% Y%
stimulation
Demand
“Differential pricing”
Average Fare =
Z%*Pz + Y%*Py + X%*Px + W%*Pw
W
Revenue =
X
Z*Pz + Y*Py + X*Px + W*Pw
Y
Z
Pw Px Py Pz Price
Junio 20092. Optimize A robust reactive pricing improves our
prices
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-Market2. Optimize A smart proactive pricing ensures a good
prices “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 the
demand, taking into account the competitive situation
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
generation3. 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 expected reality given actions implemented and
decisions taken3. 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
etcForecasts present many limitations and
3. Forecast
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
etc4. 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: San Francisco
Revenue we do not collect due to Tokyo Los Angeles
accepting a passenger in a given path
For ex: a Los Angeles-BsAs pax might
displace a Los Angeles-Lima pax
We should accept a pax in the network paying Quito
a net fare if this net fare is greater than all the Lima
displacement cost (“bid price”)
Competing passengers:
Long Haul – Low Yield Buenos Aires
vs Santiago
Short Haul - High Yield
Junio 20094. Optimize
availability
LAX
LA 600 Fares EZE-LAX Fare Classes
BP = US$500 US$ 799 > BP = US$ 700 US$ 799 B
> US$ 699
US$ 699 M
LIM
US$ 599 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” openMore 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%
7-12%
Net Segmentaction Reactive and Availability Overbooking Demand
Incremental via fare Proactive Optimization Forecasting
Revenue restrictions Pricing
34
(1) Data based on LAN’s research, PODS-MIT simulations and available literatureIndex
Brief
Overview of
LAN
Example 3:
RM concepts
RM and the
in the Airline
impact of
Industry
promotions
Example 2:
Some latest
Value Based
developments
Segmentation
Example 1:
Flexible
Redemption
351. 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
Forecasts: Probability of sell-up / willingness to pay
1
Prob of Sell-up
Higher
fare f
1−
0.8
fareQ FRAT5s
psup Q −> f
= (1) 1− FRAT 5 0.6
2 0.4
0.2
0
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
Optimization: Fare Adjustment and Convex Hull Fare Ratio
Leg Buckets
Frontera Eficiente
Convex hull/efficient frontier
Restricted Unrestricted 2000
Fare Fare B
K
Structure Structure 1500 H
Revenue
Y
1000
Pj
500
Pi-Displ.
Pj-PEcost 0
0 5 10 15 20 25 30
Demanda
Buckets2. We are trying to use competitive information in a
systematic way, but just matching availability isn’t good…
AL1 (EMSRb) matches AL3 (LCC)
emsrb leg
davn path
Closure Matching
davn leg
Open Matching
AL2 (O&D) matches AL3 (LCC)
davn path
davn leg
38
(1) PODS, May 2009. Network of 4 competitors, semirestrictedHowever, 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
39Index
Brief
Overview of
LAN
Example 3:
RM concepts
RM and the
in the Airline
impact of
Industry
promotions
Example 2:
Some latest
Value Based
developments
Segmentation
Example 1:
Flexible
Redemption
403. 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
Fare table
[kms]
U2
From a single price U1
level to a differential FFP request
T3
pricing model
T2
T1
Fare table
[US$]
U2
U1
T3
Fare Value
Adjusted
Fare Value > Bid Price O&D RM
T2
T1 Yes No
Accept request Reject request
Transfer economic cost to FFP
Immediate allocation of revenue for business unit
42Results of the new FFP have been very compelling…
Automatic process, immediately allocates revenue to business units
Increased Economic cost of seat automatically covered by algorithm
Revenue for RM has incentive to help the program
Business Units
Higher demand because of higher flexibility of FFP
Higher average fare because of fare mix
FFP becomes an efficient distribution channel
FFP becomes
Sell very cheap when economic cost of seat is very low
efficient
distribution Sell expensive when economic cost of seat is high
channel Efficient and effective way for demand stimulation
Higher possibilities to redeem in full flights
Increased More overall seats available
Customer
Considerable growth in number of redeemed tickets and burned kms
Satisfaction
Kms accrued became more valuable for customers
Increases long term loyalty (hopefully!)
Increased More partners interested in our FFP
Revenues from Willing to pay more for accrual in their businesses
Partners
More revenues for LANIndex
Brief
Overview of
LAN
Example 3:
RM concepts
RM and the
in the Airline
impact of
Industry
promotions
Example 2:
Some latest
Value Based
developments
Segmentation
Example 1:
Flexible
Redemption
44En 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
lossTraditional fare structures and display were for
experts...
FAMILIA TARIFARIA FULL FLEXIBILIDAD FLEXIBILIDAD PROGRAMADA ECONOMICA SUPER ECONOMICA PROMOCIONAL
OW RT RT RT RT RT
Tipo de Viaje Ida Ida y regreso Ida y regreso Ida y regreso Ida y regreso Ida y regreso
NEESP005
SEESP005
SEELE005
NEESP010
VEELE005 QEESP014
YEEFF001 MEEFX003 LEEFX004 SEESP010 SEESP007
BASE DE TARIFA BEEFF002 SEELE006 QEESP008 QEESP015
HEEFF001 KEEFX003 MEEFX004 NEESP011 SEESP017
VEELE006 QEESP016
NEESP012
VEELE007
SEESP012
SEESP013
Anticipación de Reserva - 2 días 4 días 7 días - 21 días
4 días ó 24 horas
2 días ó 24 horas después de 24 horas después de la
Anticipación de Compra (1) - después de hecha la 24 horas después de la reserva
hecha la reserva reserva
reserva
2 noches ó 1 2 noches ó 1
3 noches ó 1 noche de 4 noches ó 1
Estadía Mínima - 1 noche noche de 5 noches noche de 1 noche de sábado
sábado noche de sábado
sábado sábado
Paradas Intermedias (Stopovers) Ilimitadas 1 en cada sentido 1 en toda la ruta No permite No permite
Combinaciones (sólo
- Permite Permite Permite No permite No permite
dentro de misma familia tarifaria)
Cambio de Vuelo (2), de Fecha o de Ruta (3) Permite Permite Permite Permite No permite
Cobro - - $ 10.000 (4) Según tabla (4) - (5)
Devoluciones (boletos vigentes) (6) Permite Permite No permite No permite No permite
Cobro $ 20.000 $ 20.000
Reserva de Asiento Permite 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. (5) Cobro para categoría Super Económica, depende del mercado:
Para venta y origen de viaje en Arica y Balmaceda, tarifas con TL de 24 horas, serán 72 horas después de la reserva. PMCBBA, BBAPUQ y interregionales con fare basis QEESP008: multa $40.000.-
(2): Cambio de Vuelo para el mismo día: Regulación aplica para reserva confirmada (respetando disponibilidad de clase). SCLCCP, SCLESR, SCLCPO, SCLZCO, SCLZAL, SCLZOS, SCLPMC y otros interregionales con fare basis N
Para todas las tarifas (aún cuando no lo permita), pasajero puede presentarse en el aeropuerto stand-by sin cobro. SCLARI, SCLIQQ, SCLCJC, SCLANF, SCLBBA y SCLPUQ: multa $80.000.-
(3) Reemisiones: Regulación aplica para familia tarifaria igual o superior, con boleto vigente (hasta 6 meses de emitido). (6): Vigencia de los boletos: 6 meses desde su fecha de emisión.
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. Niños e Infantes con asiento: pagan el 67% de la tarifa Adulto. Infantes sin asiento: no pagan.
(4) Si cambio o devolución se realiza desde el día del vuelo en adelante, se cobrará $10.000 adicionales. Estadía Máxima: 6 meses, desde la fecha de inicio de viaje.
Equipaje libre de cargo: 20 kilos en todas las rutas.But we found that transparency and simplicity
induce revenue dilution (or “buy-down”)…
~10%
Buy-down
Customers have more free choices
However, not Demand goes where it wants to
everything is so We become more competitive
bad ! Flights get more balanced
Airline sells what RM made available for saleValue Based Segmentation is a way to increase
voluntary “up-sell”
More attributes higher price
The customer, freely
informed, can decide
what available fare
family to buy, as a
function of the value
added attributes…which imposes new challenges on us
What attributes to use? Some attributes:
KMs LANPASS
Implicit or explicit? Seat reservation
Preferent seats
Bundled or umbundled? Changes
Refunds
How to price these attributes consistently Preferential Check-in
with traditional pricing?
What fare differences are acceptable among
fare families? La diferencia de tarifa actualmente
14% 2,4 está definida en base a
Ingreso de up-sell por trasacción [US$]
óptimo
12% 11,3% segmentación tradicional, basada
y = 0,29e-0,24x 2,0
R² = 0,965
principalmente en disposición a
10%
1,6 pago por comportamiento de
consumo
% Upsell
8%
6,8% 7,4% actual
1,2
6,1%
6%
0,8
Esta diferencia se reduce en el
3,7% 3,5%
4% tiempo a medida que se agota el
2,2%
2%
1,8% 1,6% 0,4 inventario de la familia inferior
0% 0,0
Esto impacta directamente la
5 - 10
0 -5
10 -15
15 - 20
20 - 25
25 - 30
30 - 35
35 - 40
40 - 45
45 - 50
50 - 55
55 -60
60 - 65
probabilidad de up-sell en el
Diferencia de tarifa entre familias [US$] tiempoDiscrete choice models based on “Random Utility
Theory”(1) help us with these challenges
K
Utility function of U i = ∑ β k ⋅ X ik + ξ
the customer k =1
U = Función de utilidad para alternativa i
i
β = Peso del atributo k
k
X = Valor del atributo k en la alternativa i
ik
Determine the β’s with
max likelihood from
surveys of discrete
choice
50
(1) Domencich & McFadden, 1975Index
Brief
Overview of
LAN
Example 3:
RM concepts
RM and the
in the Airline
impact of
Industry
promotions
Example 2:
Some latest
Value Based
developments
Segmentation
Example 1:
Flexible
Redemption
51In the onset of the crisis, demand stimulation became one
of the most important weapons for survival
The bad news: Financial crisis
X Big fall in business traffics
Swine flu
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 withSo 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 Historic data Q Capacity curves Q Demand Curves
P P P
Q Intersection points
PThese 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) Days before
80
departure
70 5 mo
Incremental sales
60
3 mo
Venta Incremental (Pax)
2 mo
50
[pax]
1 month
40
30
Saturation 15 days
20
10
-
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
2,200
2,400
2,600
2,800
Inversión Publicitaria Semanal (U.F.)
Weekly expenditure [UF]
(1) A 20% de descuento en precioPrice 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
3
Inversión en U.F.
800
2,5
Elasticidad
2 600
1,5
400
1
200
0,5
0 0
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
Semanas año 2006-2007Conclusions 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!
You can also read
