Real Life Applications in the Airline Industry
Transcripción
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. Co pa na ir Ry a LA N G 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 S D an AF -K LM U ni te d er ic Am Br iti 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!