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Revenue Management System In Airline Industry

Submitted to xxxxxxx Submitted by xxxxxxxxx

A Dissertation report submitted in partial fulfillment of requirements for MBA (Aviation Management) April 2009 2

Chapter-1

Introduction

Revenue Management was invented by U.S. airlines in the 1980’s in response to a newly deregulated industry and to the increased competition that was created. Since then, it has been adopted by a variety of industries, and the list is constantly growing. But the basic concepts have been around for quite a long time

It was developed by the airlines to improve revenue performance in the face of increasing competition. It was obvious to the airlines that passengers could be divided into two broad categories, based on their travel behavior and their sensitivity to prices. There business and leisure travelers. Business passengers tended to make their travel arrangements close to their departure date and stay at their destination for only a short time. There was usually little flexibility in their plans, and they were willing to pay higher prices for tickets. Leisure travelers, on the other hand, booked their flights well in advance of their travel date. They stayed longer at their destinations and had much more flexibility in their plans. They would often decide not to travel rather than pay high fares, and flights often departed with empty seats.

The challenge to the innovators of revenue management was to devise a plan that would make the empty seats at the lower fare while preventing passengers who were willing to pay the higher fare from buying low-fare seats. Since low-fare passengers typically book in before higher fare customers, the revenue management system must forecast how many business passengers will want to book on a flight. Then it must set aside or protect these seats so that they will be available when the business passengers request them. 3

Objective of Dissertation

This research project examines the concept of Revenue Management in the airline industry. The objective of this dissertation is to understand the current and future trends in revenue management. Also exploring new revenue management challenges and strategies

Revenue Management (RM)

Background

In the early 1970s, some airlines began offering restricted discount fare products that mixed discount and higher fare passengers in the same aircraft compartments. For example, BOAC (now British Airways) offered earlybird bookings that charged lower fares to passengers who booked at least twenty- one days in advance of flight departure. This innovation offered the airline the potential of gaining revenue from seats that would otherwise fly empty; however, it presented them with the problem of determining the number of seats that should be protected for late booking, full fare passengers. If too few seats were protected, the airline would spill full fare passengers; if too many were protected, flights would depart with empty seats. No simple rule, like protecting a fixed percentage of capacity, could be applied across all flights because passenger booking behavior varied widely with relative fares, itineraries, season, day of week, time of day, and other factors. It was evident that effective control of discount seats would require detailed tracking of booking histories, expansion of information system capabilities, and careful research and development of seat inventory control rules. LITTLEWOOD (1972) of BOAC proposed that discount fare bookings should be accepted as long as their revenue value exceeded the expected revenue of future full fare bookings. This simple, two fare, seat inventory control rule (henceforth, Littlewood’s rule) marked the beginning of what came to be called yield management and, later, revenue management. In North America, the beginning of intensive development of revenue management techniques dates from the launch of American Airlines’ Super Saver fares in April of 1977, shortly before the deregulation of U.S. domestic and international airlines. Over the last twenty years, development of revenue management systems has progressed from simple single 4 leg control, through segment control, and finally to origin– destination control. Each of these advances has required investment in more sophisticated information systems, but the return on these investments has been excellent. In 1999, most of the world’s major air carriers and many smaller airlines have some level of revenue management capability. Other small airlines and international airlines in newly deregulated markets are beginning the development process. The success of airline revenue management was widely reported, and this stimulated development of revenue management systems for other transportation sectors and in other areas of the service sector.

Meaning

Revenue Management is the process of analysing and forecasting consumer demand in order to optimize inventory and pricing levels to maximize profitability.

Fig 1 Process of RM

In other words, to constantly analyse and forecast the remaining demand for a certain future product or event and subsequently adjusting the pricing levels in order to sell the right product at the right price to the right customer at the right time to maximize profitability.

It is important to note that Revenue Management addresses the revenue side of the equitation, not the cost side. There exists an inverse proportionality between Load Factor (Units Sold) and Average Yield (Unit Revenue) and Revenue Management will thus find the optimum balance between these factors in order to maximise Revenue or Income. 5

Profit = Operating Revenue – Operating expense

= RPK X Yield - ASK X Unit cost

or = ASK {Unit revenue – Unit Cost}

or = ASK {Average load factor X Yield – Unit Cost} Units Sold/

Unit Cost Load Factor

Average Revenue Yield/ Manageme nt Unit Revenue Fig.2 Relating terms of RM

The following examples explain RM:

. The ability to optimally match supply to demand at the best price points

. The ability to proactively affect this price point.

. Getting the most revenue possible from your assets - particularly if they are perishable. If it is not sold today you loose the ability to attain that opportunity revenue.

. The effort to gather all possible data to try and reach this market knowledge so you can be proactive with your product or purchase and not be reactive to the market. It is your product or your money, why be reactive rather than proactive with the market?

. The ability to use this information to target different market segments through their unique discreet distribution channels, and therefore divide the market into segments you can target and reach at the right time, the right price and the right product without diluting your main market base.

. The ability to fulfill the idea in the "quotes" section from "The Art of War" 6

. A proven way to increase revenues from your existing market share without battling to take market share from competitors. This can be far more cost effective than fighting to increase revenues and profits from gaining market share from your competitors.

. A multimillion dollar a year industry embraced by the largest travel, energy and broadcasting companies. American Airlines has stated that Revenue Management increases its revenues by about 5% per year, which many years has been the difference in profits or losses. The Wall Street Journal has called RM the most powerful new business tool of the next century.

Problem and Challenges

The objective in revenue management is to maximize profits; however, airline short-term costs are largely fixed, and variable costs per passenger are small; thus, in most situations, it is sufficient to seek booking policies that maximize revenues. Also, although there is lower risk in accepting a current booking request than in waiting for later possible bookings, booking decisions are repeated millions of times per year; therefore, a risk- neutral approach is justified. Consider the arrival of a booking request that requires seats in an itinerary—one or more flights departing and arriving at specified times, within a specific booking class, at a given fare. The fundamental revenue management decision is whether or not to accept or reject this booking. A large computer reservations systems must handle five thousand such transactions per second at peak times, thus the decision must be reached within milliseconds of the request’s arrival. Not surprisingly, no current revenue management system attempts full assessment of each booking request in real time. Instead, precomputed aggregate control limits are set that will close the system for further bookings of specific types while leaving it open for others. The reservations system can quickly determine the open or closed status of a booking category and report back to an agent or customer without actually evaluating the request. The accept–reject decision can be restated as a question of valuation: What is the expected displacement cost of closing the incremental seats in the requested itinerary? To maximize expected revenues, the request should be satisfied only if the fare value of the requested itinerary 7

equals or exceeds the expected displacement cost. The apparent simplicity of this valuation problem is deceptive—a complete assessment must allow for all possible future realizations of the reservations process that could be influenced by the availability of any of the seats on any of the legs in the booking. Fully traced, this influence propagates across the entire airline network because a booking can displace potential bookings that will have subsequent impacts of their own. This influence also propagates forward in time because many affected itineraries will terminate later than the booking being considered. Also, a booking will normally have a return component at a later date with its own set of concurrent and downstream effects. Many other factors increase the complexity of the evaluation process. Table I lists some of them. As can be seen in Table I, the practical complexities of revenue management are daunting—we do not have space here to discuss all of them. As is always true, modeling, theoretical analyses, and implementation rely on assuming away many of these complicating factors and approximating others. It is important to remember that such approximations have yielded enormous revenue benefits for airlines and other enterprises. The performance of a given revenue management system depends, in large part, on the frequency and accuracy of updates to control limits and the number of distinct booking classes that can be controlled.

Table 1 Customer Behavior and Demand Revenue Factors Forecasting Fare values Demand volatility Uncertainty of fare value Seasonality, day-of-week variation Frequent flyer redemptions Special events Company or travel agent special Sensitivity to pricing actions vouchers Demand dependencies between booking Cancellation penalties or restrictions classes Variable Cost Factors Return itineraries Marginal costs per passenger Batch bookings Denied boarding penalties Cancellations Goodwill costs Censorship of historical demand data Fare Products 8

Defections from delayed flights Number of products Diversions Fences (restrictions) Go-Shows Problem Scale Group bookings Large airline or airline alliance; e.g., Interspersed arrivals United/Lufthansa/SAS ORION System: No-shows 4,000 flights and 350,000 passenger Recapture itineraries/day [see GARVEY (1997), Upgrades BOYD (1998)] Control System Problem Interfaces Booking lead time (often 300 days or more) Market strategy Number of controllable booking classes Code-sharing alliances Leg-based, segment-based, or full ODF Routing control Gate acquisition and schedule planning Distinct buckets, parallel nesting, or full Fleet assignment nesting Reservations systems connectivity Frequency of control updates Overbooking

Source: Report on ‘Revenue Management: Research Overview and Prospects’ by JEFFREY I. MCGILL and GARRETT J. VAN RYZIN

Revenue Management Objectives

. Produce a demand forecast based on market data, commercial objectives, and calendar-related events

. Adjust demand forecasts based on environmental changes, management guidelines, and performance metrics

. Set up and manage an allocation strategy using the revenue management system tools in a systematic and efficient manner 9

. Prioritize administrative tasks 10

Chapter-2

Research Methodology

This research is Analytical with qualitative approach. In Analytical research, the researcher has to use facts or information already available, and analyze these to make a critical evaluation of the material. So, here in this project I have collected data, mainly from the secondary sources. In primary source, I have talked to one Revenue Management expert, Mr. Gary Parker, President of Revenue Management Training group, Canada. Based on the information from Mr. Gary and various secondary sources, I go through the revenue management scenario worldwide and able to analyze its current and futuristic trends. Also find new Revenue Management Strategies and challenges and to some extent try to predict future of the Revenue Management based on the industry experts views.

Literature Review

Davis M 1994, ‘yield management techniques are reportedly quite valuable. On an estimate American airline made an extra $500 million per year based on its yield management techniques’. According to an estimate the pricing system used by American airlines change the prices more then half a million per day.

Deneckere and McAfee 1996, ‘Revenue management techniques provide tools to use consumer surplus through dynamic pricing’. Nevertheless there is little doubt that dynamic price discrimination is economically important. The pricing system by most major airlines is opaque to the customer. The only thigh which customer came to know about the airline is the quality of service. By applying the dynamic pricing techniques and by providing best services to the customer, airlines can increase the revenue and also the loyalty of the customer to the brand.

Geraghty, Kevin 2004, ‘Revenue management offered us a way to capture revenues that were being left on the table. Revenue management implements the basic principle of supply and demand economics in a tactical way to generate incremental revenues.’ Airlines monitor through the use of specialized software how seats are being reserved and 11 react accordingly, for example by offering discounts when it appears as if seats will otherwise be vacant. During peak season airline charge the passenger as much as they can and offer products to those who are willing to pay more while in lean season various marketing techniques like free companion scheme, discounted one way fares, returned fares, excursion fares and basket scheme etc. are being offered by airline according to their marketing model.

Andy Boyd,2003,‘One-way airlines segregate customers is by imposing advance purchase requirements and Saturday night stays for cheap tickets. These restrictions act as ``gates'' that separate price-sensitive leisure travelers from time-sensitive business travelers. The challenge for other industries is to find the right gates’. The sensitivity of demand can be controlled to some extent if an airline has a good no of loyal customers and an ability to attract new customers even at the time of low demand.

Sengupta A,2006,‘Airlines that use revenue management periodically review transactions for services already supplied and for services that are to be supplied in future. They may review information such as past statistics, up coming events like sports, holidays, festival or unexpected past events such as terrorist attacks and other information SUCH as competitive information (including prices), seasonal patterns, and other pertinent factors that affect sales.’ The success of an airline depends upon the capability of the air line to forecast the future and deploy the maximum capacity on the routes with comparatively high demand but by taking the cost of operation into consideration.

Belobaba 2003, ‘Airline who is busy in maintaining its load factors by decreasing its fare with analyzing that it can harm its future policies. These airlines are more susceptible to go out of the picture.” Revenue management system provides enables airlines to satisfy customer and made profits to the greater possible extent without any lose of image. 12

Chapter-3

Key Areas of Revenue Management

1) Forecasting

2) Pricing

3) Seat Inventory Control

4) Overbooking

1) Forecasting

Forecasting is an important component of planning in any enterprise; but it is particularly critical in airline revenue management because of the direct influence forecasts have on the booking limits that determine airline profits. Forecasting for airline revenue management system is inherently difficult. Competitive actions, seasonal factors, the economic environment, and the constant fare changes are a few of the hurdles that must be overcome. In addition, the fact that most of the historical data is censored further complicates the problem.

The number of seats an airline can sell on a flight is determined by the booking limits set by the revenue management system. An airline continuous to accept reservation in a fare class until the booking limit is reached. At that point, the airline stops selling seats in that fare class. It also stops collecting valuable data. Demand for travel in that fare class may exceed the booking limits, but the data does not reflect this. So the data is censored or constrained at the booking limit.

While some models exist that produce unbiased forecasts from censored data, it is preferable to unconstrain the censored observations so that they represent true demand. Then, the forecasting model may be chosen based on the structure of the problem rather than the nature of the data.

2) Pricing 13

Pricing has been around as long as people have traded. Pricing of product or services represents the knowledge and understanding of their product and ultimately determines the growth prospectus of the organization. If product and /or service is not exclusive or not providing the desired satisfaction to the customer or unable to give value for money, will not succeed. On the other hand if product and/or service has high level of customer satisfaction and high manufacturing /operating costs it will lead to increase in the time period of reaching its break even point and loss of revenue. Thus price is to be determined in such a way that firm can reap the profit of increase in demand and let customers to take the advantage of availability of product and/or service.

Static and Dynamic Pricing:

Pricing is major area of airline revenue management. Pricing is generally categorized into to parts static and dynamic.

In the static pricing, the price is set at being of the booking period. In the dynamic pricing, the price changes through out the booking period. Airline pricing in India is almost opaque and the end users of airline services are not aware of any of these complexities of the prices determination.

Price and quantity are determined by the interaction of demand and supply in the market. However, given the large number of buyers, firms can decide prices at which they will sell tickets. In fact, in the airlines sector, firms go in for third degree price discrimination and segment the market, charging a higher price to the market with a relatively inelastic demand (such as fares between business and economy class travelers, or between emergency travel and leisure travel by providing apex fares). The low cost airlines follow this different pricing strategy. Customers booking early with carriers such as Air Deccan will normally find much lower prices if they are prepared to commit themselves to a flight by booking early, on the justification that consumer’s demand for a particular flight becomes more inelastic the nearer to the time of the service.

Differential pricing in airline industry: 14

Airlines generally adopt differential pricing In order to utilize all the available seats in a particular time limit to generate maximize their revenue. A particular flight is divided into various classes of service (first, business and economy class). Different fare products are offered in economy/coach cabin class, with different restrictions, at different at different prices. Virtually every airline in the world offers multiple price points (even low fare carriers with simplified fare structures). Economic trade off in pricing decision of an airline constitutes of stimulation of new demand and diversion of existing demand to lower fares. Recent pricing difficulties of network airlines due in part to greater diversion of revenue than stimulation of demand because of increasing no of competitors.

Effective pricing of airline inventory:

Traditional ways of determining ticket price are not in pace with airline competitive pricing practices. Still it is very difficult to estimate exactly the price elasticity, willingness to pay, potential for stimulation and diversion. More over there is no practical tool for airline to determine “optimal” prices. Some air lines are now implementing “Pricing decision support system” which work on the basis of historical demand and prices. It is primarily a monitoring of price change. The most dominant practice is to match low fare to fill planes and retain market share.

Price elasticity

Willingness to pay

Pricing of Potential for stimulation and diversion Product

Price elasticity

Fig 3 Requirements for effective pricing in Airline 15

3) Seat Inventory control

Inventory control system basically means the procedure by which the product or the finished commodity is being controlled in the market. The system depends upon the commodity that is being offered by the producer, if the commodity is perishable the inventory system is different from that of for durable goods. But when we talk about service industry we don’t think of a commodity rather we think of a service, for e.g. like in Aviation industry. They don’t offer a tangible commodity rather they offer a product which can be experience but can't be touched or felt. As we talk about FMCG market we get a tangible commodity in hand but when we compare it with Aviation sector we get an intangible commodity in terms of experience.

In the Commodity industry, inventory system has different procedures like first-in-first- out and last-in-first-out. The market demand, fashion, tastes and preferences of customers, durability of the commodity, etc affect this but in Aviation industry things are bit different from the commodity market. It has number of seats as the product in a passenger airline and space to carry load in cargo airline. In this scenario the rate or the price of the commodity is the only thing that regulates the inventory.

In the aviation industry, the companies take care of the inventory in two different forms, nested and non-nested. The low cost carrier is incorporating non-nested form while the full service carriers are taking the nested approach.

Non-nested approach says that there would no difference between the fares of the seats as they are all the same while the nested system of airline inventory control system says that even if the seats are of the same class still they have a scope to earn more revenue as compared to other seats in the class.

In the non-nested system the prices of the tickets increases as the time of the flights decreases while in the nested form of inventory management the seats in form groups, when a group is sold out then the tickets are sold in the second group. The system states 16 if the seats are sold in say “K” class that has a lower price band then the no. of seats in the mother class i.e. say “Y” class will decrease. It simply means that if a seat is sold in the sub-class so it will affect the mother class i.e. number of seats will decrease.

Nowadays the low cost carriers consider the non-nested approach, as they don’t have any class differentiation so it would be easier to maintain the inventory system as compared to the nested approach that is being considered by full service carrier as they have the different class considerations for e.g. first class, business class and economy class with spot fares.

The Computer Reservation System (CRS) and Global Distribution System (GDS) are managing the nested and non-nested inventory control system through different service providers in India (like Yatra.com, Makemytrip.com etc) and all over the world (like Galileo, Amadeus, etc).

In the low cost carrier reservation system seats are booked on a general basis irrespective of the classes as there are no classes while in the full cost carriers the seats are booked considering the classes with fares. Since there is only one class in the model so we take it as non-nested approach as it doesn’t make any difference but in case of nested approach since the classes are different.

The FCC’s seat booking system is such that if a seat is booked in the mother class it won't effect the number of seats in sub-classes but if the seat is booked in any of the sub- classes then it will effect the number of seats in mother class. As far as LCC’s is concerned these seats booked never affect any mother class or sub-class, as they don’t have any class in differentiation.

Seat Inventory control structures

The Nested Approach

In order to aid the airline's seat inventory control and overbooking policy, airline officials employ the revenue/yield management concept. In a nested structure, requests for higher fare classes can be accommodated if seats are available in lower fare classes. 17

The availability for each discount class is controlled through nesting. Nesting makes subsets of the seats available to various levels of discount fares. Smaller subsets are available to lower-valued discount classes than to full fare or moderate-discount classes. If the fare classes are controlled independently, it would be possible to sell a low-revenue reservation and simultaneously turn away a high-revenue passenger. (This situation would happen when all the seats allocated to the high-revenue class are sold, while some seats were still available to a lower-revenue class.) Nesting simplifies the maintenance of discount fares by automatically ensuring that a low-value seat is never available when a higher-valued fare is closed to additional sales. The use of nesting means that forecasting too much demand in a higher-revenue class causes poor discount allocation only for lower-revenue classes. If deep-discount seats are available for sale, so are the moderate and full-fare seats. If no moderate discount seats are available, then neither is deep- discount seats. When full-fare seats are not available, no seats are available (the flight has reached its overbooking level).

The nested structure can be further divided into two: the static nested structure and the dynamic nested structure.

In the static nested structure, booking limits are set at the beginning of the booking period.

In the dynamic nested structure, booking limits are updated during the booking period depending on the actual booking status. 18

Fig.4 Traditional leg and segment inventory control structures

Consider each of these scenarios:

. With non-nested controls, the booking classes are independent of each other. This implies that situations could arise when a booking class with a lower fare value (eg V) is open for sale while a higher fare class (eg Y) is closed for sale. For this reason, non-nested controls are rarely used in practice. It can be practical only if demand for each booking class can be forecast with absolute certainty, which is impossible to do.

. Parallel nesting controls are an improvement over non-nested controls, because all the lower-valued booking classes are nested into the higher class, Y, guaranteeing that Y has always the same or higher availability than the lower classes. As the fare classes below Y in the hierarchy are independent of each other, however, it is likely that a lower-valued class (eg Q) is open for sale, while a higher-valued fare class (eg B) is closed for sale, causing revenue dilution.

. Serial nesting controls, by virtue of their serial nested hierarchy based on fare class value, guarantees that a lower-valued class will never be open when a 19

higher-valued class is closed for sale. It is frequently used in conjunction with segment limits or segment close indicators.

. Mixed nesting controls are frequently used by airlines who wish to control consolidator, cruise line or promotional traffic with a guaranteed allocation. In this scenario, this low-yielding traffic is parallel nested into Y class.

. Hybrid nesting is a variant where there are two or more independent nesting structures in a cabin, which are kept separate. In the example, both Y and R fare classes are at the same level in the hierarchy for a cabin. This is sometimes used to manage demand from different channels separately. It suffers from the problem that, when a fare hierarchy is sold out, seats cannot be borrowed from the second fare class hierarchy without manual intervention.

Types a) Single-Leg Seat Inventory Control (Optimization Techniques)

Utilizing demand forecasts for individual flight legs, fare class yield management (FCYM) systems use optimizers which determine seat allocation for the set of fare classes on each leg within a network. The most commonly used fare class mix allocation is the idea of serial “nesting” of fare classes – a problem first solved by Littlewood at BOAC for the case of two fare classes. As opposed to allocating seats in partitioned fare classes, nesting instead protects seats in high fare classes by limiting the number of seats sold in lower fare classes based on a forecast of demand for each class, as well as the expected seat revenue.

Belobaba extended the nested seat allocation problem from Littlewood’s two classes to multiple fare classes with the Expected Marginal Seat Revenue (EMSR) heuristic in his Ph.D. thesis40. This algorithm employs leg-based demand forecasts by fare class to produce leg-based seat protection levels for nested booking classes. EMSR determines 20 booking limits based upon the expected marginal revenue – the probability of selling an additional seat in a given fare class multiplied by the revenue gained from selling that seat. As the number of seats protected in a particular fare class increases, the probability of selling that next seat decreases; thus, the booking limit for a fare class is determined when the EMSR is equal to the fare of the next lower class. Belobaba’s updated EMSRb algorithm protects joint upper classes from the next fare class just below, and has become somewhat of an industry standard for establishing booking limits on a flight leg basis. In order to simplify calculations for joint classes, the fare class demands are assumed normal and independent– assumptions which are violated when the fare class restrictions are eased.

To calculate the optimal protection levels:

Define Pi(Si ) = probability that Xi > Si,

where Si is the number of seats made available to class i,

Xi is the random demand for class i

Concept of protecting seats can be demonstrated for 2 fare classes using a simple marginal revenue analysis approach:

Protect another seat for full fare demand if

Pr [spill of full fare demand] > R2/R1

where

R2 = discount fare,

R1 = full fare, and spill means we fill up the plane with too many discount passengers and thus must reject 1+ full-fare pax.

The expected marginal revenue of making the Sth seat available to class i is:

EMSRi(Si ) = Ri * Pi(Si ) where Ri is the average revenue (or fare) from class I

The optimal protection level, π 1 for class 1 from class 2 satisfies: 21

EMSR1(π 1) = R1 * P1(π 1 ) = R2

Once π 1 is found, set BL2 = Capacity - π 1

Consider the following flight leg example:

Table 2

Fare Class Avg. Demand Std. Dev. Fare Y 10 3 10000 M 15 5 7000 B 20 7 5000 Q 30 10 3500

To find the protection for the Y fare class, we want to find the largest value of π Y for which

EMSRY(π Y ) = RY * PY(π Y ) > RB

EMSRY(π Y ) = 1000 * PY(π Y ) > 700

PY(π Y ) > 0.70

where PY (π Y ) = probability that XY > π Y

If we assume demand in Y class is normally distributed with mean, standard deviation given earlier, then we can create a standardized normal random variable as (XY - 10)/3.

Next, we use Excel or go to the Standard Normal Cumulative Probability Table for different “guesses” for π Y. For example,

Prob { (XY -10)/3 > (7 - 10)/3 } = 0.841 for π Y = 7,

Prob { (XY -10)/3 > (8 - 10)/3 } = 0.747 for π Y = 8,

Prob { (XY -10)/3 > (9 - 10)/3 } = 0.63 for π Y = 9, 22

So, we can see that π Y = 8 is the largest integer value of π Y that gives a probability > 0.7 and therefore we will protect 8 seats for Y class

How many seats to protect jointly for classes 1 and 2 from class 3?

The following calculations are necessary:

= + X 1,2 X1 X 2 σ = σ2 +σ2 ˆ1,2 ˆ1 ˆ2

R * X 1 +R * X = 1 2 2 R1,2 X1,2 = + > P1,2 (S) Pr ob (X1 X 2 S)

To find the protection for the Y and B fare classes from M, we want to find the largest value of π YB that makes

EMSRYB(π YB ) =RYB * PYB(π YB ) > RM

Intermediate Calculations:

RYB = (10*1000 + 15 *700)/ (10+15) = 820

X Y ,B = X Y + X B =10 +15 = 25 σ = σ 2 +σ 2 = 2 + 2 = = ˆY ,B ˆY ˆ B 3 5 34 5.83

The protection level for Y+B classes satisfies:

820 * PYB(π YB ) > 500

PYB(π YB ) > .6098

Again, we can make different “guesses” for π YB. 23

Prob { (XYB -25)/5.83 > (20 - 25)/5.83 } = 0.805 for π YB = 20,

Prob { (XYB -25)/5.83 > (22 - 25)/5.83 } = 0.697 for π YB = 22,

Prob { (XYB -25)/5.83 > (23 - 25)/5.83 } = 0.633 for π YB = 23,

Prob { (XYB -25)/5.83 > (24 - 25)/5.83 } = 0.5675 for π YB = 24,

So, we can see that π YB = 23 is the largest integer value of π YB that gives a probability > 0.6098 and therefore we will jointly protect 23 seats for Y and B class from class M!

Suppose we had an aircraft with authorized booking capacity 80 seats, our Booking Limits would be:

BLY = 80

BLB = 80 - 8 = 72

BLM = 80 - 23 = 57

Basic modeling assumptions for serially nested classes: a) demand for each class is separate and independent of demand in other classes. b) demand for each class is stochastic and can be represented by a probability distribution c) lowest class books first, in its entirety, followed by the next lowest class, etc. d) booking limits are only determined once (i.e., static optimization model) b) Origin–Destination Control

O&D controls with virtual nesting Virtual nesting was first developed (Smith, 1986) to control inventory by O&D. Vinod, B. (1995),‘Origin and Destination Yield Management’ Massachusetts institute of technology, described that the use of “virtual buckets” to compare network value of local and connecting fare classes is one approach to network OD control. DAVN couples OD forecasting with leg-based seat inventory control, and uses a deterministic linear program (LP) with an objective of network revenue maximization to calculate a “pseudo fare” for 24 each fare class in the network; this pseudo fare corrects the regular fare for network displacement effects. By grouping each leg’s pseudo fares into similar sets, or buckets, and then optimizing booking limits for those buckets, the airline has a mechanism for maximizing revenue while accounting for displacement costs over its network. The objective of a nested inventory control structure is to ensure that a lower-valued service (O&D, one-way itinerary) class is not open for sale when a higher-valued service class is closed for sale. This statement is not completely true, since service classes that are mapped to the same virtual bucket have the same availability, even though there may be fare differences between them. 25

O&D controls with continuous nesting A growing number of airlines are using continuous nesting controls to manage seat inventory. Continuous nesting controls (Vinod, 1995) are called bid price controls. The bid price can be defined as the opportunity cost of not having an incremental seat on a flight leg in the network. Alternatively, the bid price can be defined as the incremental total revenue that can be derived if one excess unit of capacity is available. The excess revenue would be obtained by accepting a different mix of passengers by service (origin, destination, routing) and class in the network. The bid price is also frequently interpreted as the minimum acceptable fare on a flight leg. Bid price controls with a bid price and gradient provide the flexibility of detailed control by service class without introducing a significant overhead to reservations processing. The total bid price for a service is simply the sum of the individual bid prices of the flight legs that make up the service. If the total bid price is less than the fare, the request is accepted. Otherwise it is rejected. Therefore, financial availability is determined by the net contribution, given by

Faresc is the fare for service s and class c, λ j is the bid price for flight leg j; s ϵ S, where S is the collection of services in the network. If the net contribution is positive, the reservation request is accepted. Otherwise, it is rejected. In addition, the incremental bid price or gradient is used to adjust the bid price when bookings and cancellations occur. The gradient can be interpreted as the change in bid price for a unit change in available seats. With each new booking, the bid price is increased by the bid price gradient. With each cancellation, the bid price is decreased by the bid price gradient. Therefore, if a request is accepted, the bid price is updated by adding the gradient as follows 26 where λ j is the current bid price of leg j, λ+ j is the new bid price on leg j, Δj is the gradient on leg j, and SeatsBookedj represents the number in party for the booking request. Similarly, if a cancellation occurs, the bid price is subtracted by the gradient as follows

Note that this assumes that the relationship of the gradient to the change in capacity is linear. This, however, is an approximation that can only be applied for small changes in available seats. A better alternative is to generate a bid price vector from the network optimisation model, based on the assumption that primal feasibility exists as bookings are accepted. By storing the bid price curve on the flight inventory record, the linear assumption can be relaxed. An argument can be made that frequent reoptimisation of the network obviates the problem, and the bid price/gradient approach is comparable in performance to the bid price curve. There will always be latency, however, between network reoptimisations from revenue management, which makes the bid price curve more appealing.

Continuous nesting provides a level of granularity beyond virtual nesting controls. Every service class availability request is considered unique, unlike virtual nesting, where availability was the same for all service classes mapped into a virtual bucket.

4) Overbooking

The travel industries in particular are plagued by the problem of “no-shows” – people who book inventory and then do not show up to use it (or pay for it). The attachments of cancellation penalties to airline discount fares are attempts to mitigate this problem, which have met with some success. To compensate for no-shows, travel firms “overbook” their capacity, trading off the possibility of empty units if they don’t 27 overbook enough against the ill will and out-of-pocket compensation to customers that occurs when customers are “bumped” (airlines).

This tradeoff should be considered in the following manner. First, the probabilities of incurring various No-Show rates must be forecasted in much the same manner that demand is forecasted. In the Travel industry, no-show rates often vary by rate class/market segment and time period. The “cost” of failing to honor a customer’s booking, including both out of pocket costs such as cash compensation to “bumped” airline passengers and a consideration of the potential loss of future revenue from the disgruntled customers, must also be calculated. (Airlines attempt to minimize customer ill will by compensating passengers who voluntarily relinquish their reservations with free tickets.) With this information the expected oversale cost (probable number of oversales times the total cost per oversale) can be calculated for any level of overbooking above the actual number of inventory units available for sale. The “correct” level of overbooking is where the expected cost of an oversale for the next unit to be sold is equal to the EMR value for the next unit to be sold. As long as the EMR value is higher than the marginal expected cost of an oversale, it will pay to allow another unit to be sold for at least the EMR value.

Traditional Vs Unrestricted Revenue Management

Traditional revenue management assumes market segmentation and fares which have rules and restrictions. Even without RM, there would be a mix of passengers owing to fare rules. Traditional RM performance measures are, typically, revenue opportunity, spoilage and dilution. On off-peak flights, all classes could be left open, as the fares rules ensured some mix of passengers. On busier flights, RM controlled classes according to historical fare mix.

RM of unrestricted fares is a different issue. With no rules and restrictions, different classes are no longer individual entities. The passenger will always buy the lowest fare in the cabin-there is no incentive to up sell, as there is no distinction between fare types. On lower load factor flights, demand will always shift to the lower class-therefore the emphasis is now on controlling all flights at the detailed level, whether traditionally peak 28 or off-peak. The dilution risk on off-peak flights must be assessed against the potential for volume growth on off-peak flights.

The role of RM has been re-defined; the question becomes when to close the current available class/fare-after x number of bookings or y days before departure? The traditional RM system becomes a vehicle to gather data, optimize overbooking levels and cabin split. User overrides are required to ensure that classes are closed on the approach to departure to assist with up sell. Overrides are also required to cap seat limits by class to protect against dilution from higher class. Achieving the balance of how many seats and how long to keep a class open is the key to a one-way RM approach. Calculating the number of seats to sell and when to close classes should reflect the level of price elasticity equation, however, are such that it is difficult to model-passenger choice depends on many factors, including schedule, competitive price, fare etc.

The main issue now present is that of booking frames for business and leisure travelers. When these time frames start to overlap, the question of which class will generate the highest revenue is crucial. For example, business passengers usually book within the last two weeks on short-haul routes. On many of these routes, however, leisure passengers are also looking to buy within a similar time frame. The trade –off between higher fare/lower volume vs lower fare/higher volume must be considered. To help ascertain the potential level of business dilution if lower classes are left open, PNR checks are useful. These can be manual or via a PNR database. By reviewing the volume of business itineraries booking in the lower leisure classes, it is straightforward to establish the likely level of dilution on a flight. (The number of business itineraries can be based on certain assumptions linked to length of stay, source of booking, advance booking period, etc) Until trials are made to close classes, however, it is difficult to establish what will happen following a change in strategy. 29

Chapter-4

Latest trends in RM

‘Rising fuel prices are leading to opportunity to explore intelligent RM Strategies’

With fuel prices skyrocketing and customers resisting higher fares, increasing revenue is a primary challenge for all airlines.

‘Network Optimization in a Mixed Environment of Restricted and Unrestricted Fare Structures’

The development of the optimization methodology, in response to the changed business environment with intensified competition from Low Cost Carriers with unrestricted fare structures. The approach extends the O&D network optimization principles for optimizing revenue in a restricted fare structure to an arbitrary mix of fare structures consisting of both restricted fares and unrestricted fares on a path basis.

‘Fenceless Fare-Structures, Revenue Management Systems, and the New Role of the Analyst’

With the introduction of fenceless fare-structures, traditional RM systems have to be adapted in order to prevent spiral down. Many airlines actually use manual overrides to achieve this. As the optimization and forecasting capability of the systems evolves to take into account buy-down and even competitive scenarios, the role of the analysts will change again. Game theory, and simulations used to suggest that that role should not be reduced to adjusting the forecasts of the new-style systems while leaving the optimization to the computer. On the contrary, the responsibility of the analysts must grow as they need to define the strategy on how to deal with the competition.

‘Different Pricing and RM Strategies on Markets with Low Fare Competitors’

With recent trends toward fare simplification, airlines have been struggling to adapt their RM models to cope with reduced demand segmentation and "spiral down" caused by less restricted fares. 30

‘Coordinating Revenue Management in Airline Alliance’

Code sharing agreements in airline alliances allow alliance members to virtually extend the reach of their networks. Such agreements, however, create a difficult coordination problem: each member makes revenue management decisions to maximize its own revenue, and the resulting behavior may produce sub-optimal revenue for the alliance as a whole. A variety of mechanisms have been proposed to coordinate alliance sales and share revenues, including free sale and soft block arrangements. The questions to examine are (i) how revenue management decisions of the alliance partners are influenced by the revenue-sharing arrangement, (ii) the effect on alliance-wide profits and, (iii) how revenues are split among alliance partners.

‘How to Take Advantage of On-Line Sales Information in the RM Equation’

With the growth of on-line sales, Web Analytics used in tandem with revenue management can provide a powerful tool for revenue optimization. It is now possible to track exactly who uses website and precisely how they do it, what products are selling (and which aren't), activity trends, types of visitors, where they come from, number of visitors per page, the page they were on when they left your site, the percentage of customers who created a shopping cart and checked out, and many other factors. In short, on-line sales provide vital information that can guide airlines in improving their product offering, their customers’ experiences, and maximizing revenue.

‘Supply Side Revenue Management’

The ability to match demand (or yield) with capacity is central to the operational success of an airline. Variants of the Fleet Assignment Model (FAM) have proven invaluable during the schedule development and planning processes at many airlines. Likewise, much has been discussed about demand-driven dispatches. However, near-term fleeting adjustments are often met with operational hurdles.

‘Revenue Managing Consumers through Competitive Pricing’

‘The Evolution of Revenue Management- Integrating your CRM and Revenue Management Systems’ 31

Chaper-5

Revenue Management Strategies

RM strategies have been changing with time due to changing business environment. Various factors defining this new environment are discussed below.

A new business environment

. Continuous growth of the Low Cost Carriers (LCC) − One-way fare structures − Extremely low bottom prices − Non-transparent pricing structure . Full service network carriers reduce fare conditions . Capacity is continuously expanding . Yield and traffic volume under pressure − Resulting in declining revenues . Rapidly changing environment . Increasing costs − Fuel − Airport fees Reacting to Market Changes

Fig.5 Reacting to market changes strategy chart 32

While reacting to market changes using revenue management strategies, it is important to cover concepts like demand and supply, customer segmentation, pricing, booking class assignment and inventory nesting. With a good understanding of marketing, economic theory, understanding customer behavior and market dynamics is easier.

Market knowledge is important when analyzing and evaluating how demand will be impacted by changes in the market place. There can be many kinds of changes both externally and internally that can challenge a Revenue Analyst to maintain optimal seat allocations. We call these changes triggers.

The RM system has many levers and allows the revenue analyst to set up initial seat allocations based on current strategies. When a change in the market triggers a required adjustment, the analyst needs to understand what needs to be done, what system changes are required and what are the best levers to modify.

In order to set up an initial allocation strategy, and to be alerted to changes or trends, a good reporting system is needed. If you know what to look for, and you have reports that give good indications of performance, and also direct you to the appropriate levers to make adjustments, you will realize the benefits of the RM system faster.

Scheduled reports that look at KPI on a daily or weekly basis allow the Revenue Analyst to maintain the RM system and to optimize revenue potential. They also allow for the necessary feedback to management on the performance of their strategy.

Concepts and models such as this provide a reference base for how work flows and people interact, and what are best demonstrated practices. They ensure a consistent introduction and training is delivered to new employees as well as those following recurrent training. The entire model is documented for reference at any time. 33

Market Change Triggers Demand Management Schedule Management . Price . A New flight departure o Proactive pricing o Creating a new forecast o Reactive pricing o Copying booking class . Market growth /decline (share) history o Market demand is growing or o Setting overbooking declining . Evaluating schedule o Competitive schedule change change impact on demand . Group management . Capacity planning o Group acceptance evaluation . Event management o Identifying the event in the RM system o Managing events (moving dates)

Table 3 Factors of Market Changes 34

Value Positioning For Market Segmentation 0 3 + Good Value Value line 0 2 + y

t Premium Brands i l a 0 u 1 + Q

t c u 0 d

0 Mid-Market o r 1 P

d e v 0 i 1 e - c

r Economy Brands e P 0 2 - Poor Value 0 3 -

-30 -20 -10 100 +10 +20 +30 Relative Price

Fig.6 Perceived product quality and Relative price relationship

Pricing has to be designed to fit into a market positioning strategy. In the above Fig., Prices that position a brand below the diagonal will be perceived as poor value by consumers because their price is too high for the quality they are seen as offering. Pricing above the diagonal, offers outstanding value with customers perceiving high quality brands at relatively low prices.

In a strategic approach to pricing, Management first defines its desired market position relative to its target customers and competing brands; this positioning defines the acceptable price, which in turn determines the acceptable level of costs. The product has then to be designed to meet cost constraints.

While markets have traditionally been divided into three or four segments, the evidence is that the number is increasing. At the limit every customer can be an individual segment for which a separate product is designed and price set. This is referred to as one-to-one marketing.

Several factors shape this trend. One is rising customer expectations for individually tailored solutions. These expectations are fed by greater affluence and more competition 35 among suppliers. The second factor is information technology, which facilitates one-to- one communication between buyers and sellers. Finally, new production methods, from flexible manufacturing to new networking relationships among supply chain participants, increasingly facilitate customized solutions.

Competitive Flight Optimisation

Fig.7 Source: Lufthansa Systems ProfitLine Yield Rembrandt.

Competition Match Rule- Based Control

. Scan the internet for the lowest available price on selected routes . Define the correlation between each flight departure and the competition . Establish business rules for matching lowest available price on a flight departure basis Competitive Pricing Challenges

We consistently find examples where an airline could improve bookings and revenue by making dynamic pricing changes or better communicating their value proposition. It is difficult for airlines to price perfectly because: They are forced into a match/don’t match game There is no single right price for a population 36

They don’t have enough real-time customer information They don’t know what else is displayed on the screen at the time of sale

Reduce Internal Processing Cost Strategies and Policies

Fig.8 Source: W.H.T. Blom, VP Pricing & Revenue Management Europe, KLM, Barcelona 7 & 8 March 2005

Reducing internal processing costs involves examining strategies and policies to improve business processes and to set guidelines and policies about how and when to adjust strategies in response to the competitive market place. Flight performance categories are very useful when applying a systematic approach to seat allocation strategies. We can see nine possibilities in a two dimensional perspective. Another dimension might include flight treatment in a competitive or non-competitive environment. 37

Flight Management Strategies

Fig.9 Source: Jerry Foran, British Airways, IATA RM & Pricing, Oct. 2004

This table illustrates how flight categories can be used to manage flights on a route by day-of-week and time-of-day. An on-going adjustment to the categories is taking place to account for special events as well as patterns and trends over time.

New revenue management challenges . Increased competition . Passenger Traffic Growth . Average airline yields decline . Restriction free pricing . A new pricing model . Price simplification . Value positioning for market segmentation 38

Chapter-6

Future of Revenue Management

What will drive the future of RM?

. Pricing transparency

. Computing power and database manipulation

. Understanding of consumer behavior (especially web analytics)

. Consumer tolerance for being ‘RM’d’.

Pricing transparency has grown quickly over the past several years. From the days when pricing was transparent only to the travel agent, to the advent of online agencies like Expedia, Travelocity and Orbitz, to the creation of information conduits, like Kayak and Sidekick. Such transparency has made the other airline’s selling price much more relevant to the determination of my optimal selling price, such that RM systems will need to get much better at knowing the competition’s price and incorporating it into the calculation of future demand and the elasticity of demand. I expect that airlines will figure this out, gradually, over the next ten years.

Computing power will make the incorporation of competitive prices viable. It will also enable more tailored offerings (such as genuine one-to-one pricing), as airlines learn how to use what they know about a customer to alter the offering.

But in order to customise the offering, they will need to know a lot more about their customers than they do today. They will need to learn how to use some of the powerful data that can be gleaned from the click streams of their website visitors. And to combine other data sources with their vast databases of frequent flier travel. Finally, all of this ‘science’ will be for naught, unless it provides a buying environment that customers like (or at least will tolerate), and good value. Airlines can justify changing prices constantly, charging five times more for the last seat than the first one, or charging more on the Friday before Christmas than on Christmas Day all we want, but the travelling public will 39 decide if they buy it. It remains to be seen if there is sufficient value in foregoing the extremes of RM in order to provide a more palatable pricing structure, or — on the contrary — if the public demands greater access to last seat availability and is willing to pay for the privilege. So how will it play out? To borrow a trite phrase, ‘The Customers will decide’. They will determine such critical factors as:

• The importance of loyalty and loyalty programs

• Tolerance for non-refundability — paying a hefty price for holding inventory versus the hotel/rental car model

• Transferability — should/will a customer that bought a non-refundable be able to transfer usage

• Predictability — how important will it be to customers to ‘know’ that a fare is fair, that it won’t go down later, that it isn’t materially higher than from a different channel or a competitive service?

1. Loyalty programmes will get better and even more important than they are today. Airlines will make it even more worth your while to keep using them, to the exclusion of competitive options, through the use of escalating benefits. And their use of personal data about customers will enhance these offerings.

2. Airlines will have to find a better way to balance the inventory holding costs and the customer’s need for some flexibility. They will use inventory controls and bid prices to govern which changes are free or inexpensive and which ones are not.

3. Security concerns will continue to limit the buyer’s rights to transfer tickets, but airlines will figure out how to allow buyers to transfer the value of the tickets and the rights to a specific seat on a specific flight to a new customer.

4. Airlines will never agree on the need for fare predictability. Some will market themselves as ‘every day low fares’, and some will market their last seat availability for 40 their best customers, who pay the ‘best’ (ie the highest) fares. Just like in other retail business. Source: By Scott D. Nason, Vice President —Revenue Management at American Airlines, Journal of Revenue and Pricing Management

The continuing evolution: Customer-centric revenue management

In an effort to get closer to the consumer, airlines are investing in data mining, business intelligence and advanced data analytics to understand customer traits, behaviors and preferences in order to improve customer retention, acquire new customers and maximize the revenue-generation potential from the customer base. The renewed focus on customer loyalty and the customer experience are key areas of investment for airlines to differentiate themselves.

Customer relationship management (CRM) applications automate the customer-facing interactions between an enterprise and a customer based on an acknowledged fact that it costs three times as much to acquire a new customer as it takes to retain an existing customer. Traditional business processes that are the focus of CRM within an enterprise are marketing, sales and service.

Key Enablers of Customer centric Revenue Management

Customer-centric revenue management is a CRM enabler to increase an airline’s profitability based on customer insight. Traditionally, it has been the role of marketing in an airline to acquire new customers in the most cost-effective manner. In today’s environment, however, it requires a combination of marketing, revenue management and real-time inventory control to facilitate one-to-one targeted responses to manage the customer lifecycle across all customer touch points. Figure 10 illustrates the key enablers of customer-centric revenue management. While these initiatives can be sometimes viewed as independent initiatives, they need to come together in a cohesive framework for the practice of effective revenue management in a changing landscape. 41

Fig. 10 Key Enablers of CCRM

Forecasting based on consumer preferences

. A forecasting approach that follows the consumer demand process

. A top down approach that provides insight into market demand and consumer preferences

• Displacement time, elapsed time, competitor schedules/fares, aircraft type, restrictions, etc.

• First choice demand

• Improved estimates of recapture, up sell and price elasticity 42

Forecasting conditional demand based on the selling fare

. Given “Price-Demand Relationship, calibrate the “Price Curve”

. Estimation of Price-Demand Relationship

. A causal model for forecasting conditional demand

• Historic price points, observed bookings & inventory controls

• Estimation of buy-up, buy-down behavior using proximity of fares by days prior to departure

Fig.11 Price sensitivity graph by relating it with demand and days of departure

Significant improvements can be achieved in following the demand process – augmented with a consumer choice model 43

Fare Simplification

The pricing revolution and impacts on revenue management

Traditional Fare Structure

. Each product is independent and governed by a set of restrictions

. Segments passenger demand by creating fences. Independent unique products by fare class

. Traditional revenue management forecasts demand by fare class and determines optimal allocations against available capacity

Fare Simplification: Original LCC Model

. Products (fare classes) are not independent.

. Lower fare differential

. Multiple fares are filed, but with the same identical restrictions

. Promotes 100% sell down to the open fare class due to the absence of fences

. Revenue management should forecast dependent demand (based on current fare class that is open)

. Active monitoring and closure of selling fare at the right time is required to promote sell-up to a higher fare

The pricing revolution and impacts on revenue management

. Hybrid fare structure

. Products (fare classes) with identical restrictions are not independent.

. Multiple fares are filed with identical restrictions 44

. Promotes less than 100% sell down since multiple classes with different restrictions may be open

. Restriction free tariffs can be on local or connecting markets

. Revenue management should forecast dependent demand

. Active monitoring and closure of selling fare at the right time is required to

promote sell-up to a higher fare 45

Alternate segmentation

Getting closer to the customer requires an understanding of the data and an investment in a data warehouse. With an investment in the storage and analysis of passenger name record and ticket data, airlines are interested in segmentation of customers beyond the traditional booking class that is used for inventory control and distribution of availability through the Global Distribution Systems. Figure 12 illustrates a typical enterprise data management infrastructure. Creation of a data management infrastructure supports a deeper understanding of the customer base and prevents customers from leaving through the revolving door.

Key metrics and continuous feedback

Propensities, attitudes,

behaviors…

Age, income, location…

Name, address, email,…

PNR, Ticket data, …

Fig.12 An Airline’s Enterprise Data Management Infrastructure

Branded products are another example of alternate segmentation beyond the booking class. In an effort to overcome the perception of an airline seat as a commodity, a key initiative in the airline community is to focus on the brand, describe the uniqueness of the products offered for sale and communicate the product offering to the customer. This is based on the fundamental premise that the airline seat is not a pure commodity as it is currently perceived. Table 4 illustrates examples of branded products adopted by some airlines. 46

Airline Branded Products

Air Canada Tango, Tango Plus, Latitude, Executive

Tiny, Economy, Premium Economy, bmi Business

Promo (Promotional), Econo (Tourist), Avianca Flexi (Flexible), Plena (Full Rate), Ejecutiva (Business)

Table 4: Examples of branded products

Each branded product has unique traits that are essentially soft qualifiers bundled into the product definition such as access to pre-reserved seats, frequent flyer miles credit percentage, lounge access, baggage count allowed at no charge, etc. The standard segmentation of customers for revenue management is based on the booking class.

Product Unbundling: ‘Distribution with Differentiated Content’

Impacts of customer centric revenue management on distribution

With the growing emphasis on ancillary products as a revenue stream that can augment the bottom line, airlines require the capability to sell, distribute and settle ancillary services across all channels of distribution. This implies that a capability is required to set the prices for ancillary services, distribute products with differentiated content and conduct financial settlement across all channels. This has significant impacts on the capabilities of current airline reservations systems, Global Distribution Systems and revenue accounting.

Promoting ancillary services also has an impact on revenue management. If certain customer segments are more likely than others to consume ancillary services, this should be factored into the decision-making process when discount allocation controls are 47 established on an airline’s reservations inventory system. Hence, the average passenger revenue for a booking class can be augmented with the ancillary revenue forecast before allocations are determined to ensure that seats protected for booking classes with an ancillary revenue upside receive additional seats. This requires a forecast of expected ancillary revenues by customer segment based on historic consumption, which can then be added to the average fare value of the booking class to get a true representation of contribution when the network is optimised. Current revenue accounting systems do not aggregate ancillary services consumed by a flight segment. Hence, the challenge is to enhance the existing revenue accounting systems to track the usage of ancillary revenues by flight segment.

Ancillary Services (Air and Travel Extras)

. In 2004, ATPCO estimated the opportunity value for a global solution for

. ancillary services to exceed $9 billion in revenue

. Unbundling of airline products is becoming a reality

− Some airlines are experimenting in the distribution of a variety of in-flight products and services (i.e. pre-paid seats, baggage check, meals, entertainment, etc) based on customer insight

− Other airlines focus on selling optional flexibility with the use of their fares (upsell / rule buster)

. An independent survey conducted by Leflein Assoc. in January 2006 showed that many travelers would pay for extra perks such as more overhead bin space, in- flight internet access, etc.

. Ryanair reported that its ancillary revenue rose 31% in the quarter ended June 30th 2006, outpacing its 20% increase in traffic

Pricing of Attributes

. Pricing of attributes based on customer willingness to pay 48

. Varies by market 49

Differentiated Content

. Capability to explain to a customer the products offered by an airline.

GDS / Reservations Initiatives

. Provide the capability to sell, distribute and settle air and travel ancillary

services through the consumer direct and indirect channels of

distribution.

. A key element of merchandising is to differentiate airline content,

provide capability for upsell on inbound, outbound and round trips.

Intelligent Proactive Fare Management: ‘Customer willingness to pay’

Pricing, long forgotten, is a key enabler of incremental revenues. Traditional airline pricing is limited to competitor fare monitoring and matching based on rules.

Tactical and strategic price leadership in a market is increasingly being viewed as a competitive weapon. Tactical pricing is the traditional fare management process of responding to a fare action taken on a specific fare in a market by a competitor. Strategic pricing has a longer term view and is the process of promoting an entirely new tariff structure for a market. The time dimension and fare management intelligence by Tactical Vs Strategic Pricing 50

Table 5

Tactical Strategic

. Evaluate and recommend a . Evaluate and recommend a new tariff smart response based on a structure (all fares) for the market competitor’s specific action . Time window is 3 months– 12 months (fare specific) . Price leadership and implementation . Time window is 0 days – 3 of a sound pricing strategy months . Long term objectives and greater . Response to competitor actions uncertainty based on quality of service and prevailing fares . Pro-active pricing based on customer willingness to pay and prevailing . Fulfils immediate objectives competitive market conditions with limited uncertainty

. Active monitoring of competitor fares to execute “smart- matching”

Traditional airline pricing has relied on reactive fare matching to respond to competitor actions. Sometimes, the reaction ripples through other markets or differs from the original change, inducing a sequence of changes. The objective of reactive fare changes is often to match a competitor’s fare to preserve market share. Traditional tactical fare matching can be replaced by determining the right response based on the quality of service offered by the competitor that initiated the fare action. Hence, the tactical fare response to a specific fare action by a competitor can be an intelligent response as a function of the quality of service. If the quality of service offered by a competitor that initiated the action is inferior, a fare match response may not be the desired alternative. 51

While tactical pricing is focused on a response to a specific fare action by a competitor, strategic pricing is a pro-active approach to filing a new tariff structure for a market based on the desired pricing objectives and business constraints. The first step in the strategic pricing process is to define the pricing strategy by market or market entity (group of markets). The objectives and goals almost always vary by market or market entity, which represents a group of markets where the fares are related. A strategy for one market or a group of markets may be very different from an alternate market or market entity. For example, a specific market may have an overriding goal of maximising market share because there are dominant competitors and to be a player requires revenue volume. Likewise, for a different market, the airline may have the dominant lift and hence the objective of maximising margins instead of volume may be more appropriate. In some situations, a market may be experiencing very low bookings and hence improving the booked load factor on the pertinent segments may be a primary objective. The market strategy objective should satisfy the business constraints that need to be imposed before a tariff structure is generated for the market. Typical examples of business constraints are the price relationships that need to be maintained between the different tariffs and competitor response to a fare action. Besides the potential increase in revenues with a fare action, a secondary advantage of using a strategic fare optimisation model is the introduction of price consistency in a market over time.

The decision variable used in the strategic fare optimisation process is the fare category, which represent the broader customer segments, where each product is defined based on a combination of one or more of the associated restrictions. Typical attributes that make up a fare category are

• Fare Type (Adult Economy — ‘ECO’, Military — ‘MIL’, Government — ‘GOV’, Child — ‘CHD’, Seniors — ‘AGE’, Student — ‘STU’, Bereavement — ‘BRV’)

• Advance purchase restriction (3AP — ‘03’, 7AP — ‘07’, 14AP — ‘14’, 21AP — ‘21’) 52

• Minimum stay restriction (Saturday Night Stay — ‘S’, MIN 3 — ‘03’, MIN 7 — ‘07’)

• One-way versus round-trip fare (‘X’/‘R’)

Associated with each fare category are multiple fare basis codes that can afford subtle variations in restrictions and prices. One or more fare categories are associated with a fare class, which is the level at which inventory is controlled. The status of a fare class (available for sale or not available for sale) is then determined by revenue management, updates are established in the host CRS and then distributed to all the sales channels.

For pricing analysts to initiate an intelligent fare action is a multidimensional problem. For example, setting prices too high may create an undesirable price image with respect to the competition, and setting them too low may result in lost margins, price wars and eventual brand erosion. The objective is to set and manage prices based on the strategic goals for the market or market entity where fares are related between markets, while simultaneously maintaining price consistency and price-image targets. An important consideration is also the current market share for the specific market or market entity and the assumptions made on competitor response, which is the percentage of other airline (OA) market share that will match the pricing initiative. Figure 13 illustrates the fare action dilemma faced by a pricing analyst. Given the historical data on average fares and traffic distribution by fare category for a specific period (eg Summer 2007), the pricing analyst has to determine price levels for the target (Summer 2008) period. Specifically,

(i) What is the initial price for full Y (economy fare with no restrictions) for the target period?

(ii) What are the discounts off full Y fare category that should be established to achieve a desired traffic distribution across all fare categories? 53

Fig.13 The pro-active fare action dilemma

Once the price discounts at the fare category level and the value of full Y are known, the pricing analyst can introduce multiple variations at the fare basis code level and determine the value of the fare based on the historic traffic flow from the calibration period. The simple rule of thumb is to ensure that the selling fare basis codes associated with a fare category average out to the recommended discount at the fare category level.

Accurate Availability: ‘Sense and respond inventory controls’

The exponential growth in online bookings over the past decade has provided customers with instant access and visibility into competing schedules and fares through the web supermarkets like Travelocity and Expedia. This unparalleled transparency of schedules and fares over the internet has propagated a bargain hunting mentality among leisure online travelers, resulting in a disproportionate growth in availability processing due to 54 the increased shopping activity. As a result, the need for greater revenue and inventory control has not been greater.

Revenue management is going real time, to respond to market conditions and shopping alerts is an integral part of the decision making process to respond to real time alerts.

Traditional RM does not consider competitor availability in the decision making process. To be aware of competitor, there is a great emphasis on real time revenue management. Real time revenue management is the ability to sense and respond to prevailing market conditions (competitor schedules and availability) in real time and provides a mechanism to override traditional revenue management controls.

In a nutshell Customer centric revenue management is all about…

. Forecasting demand and no-show behavior based on consumer traits and preferences.

. Simplified pricing is here to stay with the emergence of low cost carriers as a major force worldwide.

. In an attempt to get closer to the customer, airlines are contemplating alternative segmentation strategies to manage seat inventory.

. Accurate availability based on the competitive landscape and stated marketing objectives is on the radar of most leading airlines.

. Price leadership is increasingly being viewed as a competitive weapon.

Other Important points in future of RM

New Customer Options

The major US airlines have been surveying customers with the intention of unbundling fare products. They are all looking at this as a way to offer lower fares at a lower cost, in order to compete with the low-cost carriers, and at the same time allowing customers to buy-up for better service. 55

Customers purchasing airline tickets are often interested in products such as flight insurance, car rental, or accommodations, and may appreciate the convenience of purchasing them at the same time. One option is to display icons that the customer can choose to click through to access these supplemental services after they have finished booking and paying for their flight. Another option is to integrate the decision to purchase another product or service into the initial airline ticket booking process. A travel consumer is more likely to purchase an ancillary product such as flight insurance when its purchase is integrated into the airline ticket “booking stream”. This means that the customer is asked to make this decision before he or she has finished booking the flight. The cost of the extra product is added to the total bill before final payment. When Ryanair integrated an insurance product (previously offered on their site by clicking through a discrete icon after purchasing the ticket) into the airline ticket booking stream, the percentage of customers purchasing the product rose from 2% to 10%. To do this type of dynamic product packaging airlines need computer software that allows them to retain control over the booking at a reasonable price. This also means that you can only do this type of marketing on your company website and not through a GDS. Some airlines prefer the click-through option for all ancillary products claiming that customers are smart enough to create their own packages. Either option represents an opportunity to make extra revenue with very little incremental cost to the airline. The dreaded middle seat could be in high demand. That's because it may soon cost more for a coveted aisle seat or even a seat by the window. Airline revenue management is going a la carte. Some experts believe it is only a matter of time before passengers will pay according to seat location within the coach cabin. United fired the first salvo with "Economy Plus Access," in august of 2007. For a $299 annual fee, any passenger may request an upgrade to the Economy Plus section of the plane where the seat pitch can range from 3" to 5" more than a standard coach seat. The Economy Plus section was formerly the province of United's elite Premier fliers or others traveling on full fare tickets. With Access, United has assigned a premium price to the most sought-after seats. 56

Airline services are going a la carte, however, a la carte options are not a new concept in other industries, and it's fast becoming the norm. The good thing about it is that customers can decide if they want it or if they don't want it. But this is only the beginning, because the marketplace is there and the tools are there on everybody's Web sites to assign a price to almost any option. Airlines might soon give customers the option to use the special check-in lines, as well as the express security lines at many airports, for a fee. Many travelers will probably gladly pay to bypass the queue. Customers could accept anything that seems like a reasonable proposition. “Individual pricing” is yet another step in the evolution. Priceline.com allows travelers to name their own price, though travel vendors can reject any bid. Orbitz offers a product called "Deal Detector" which also allows users to establish a threshold price. Orbitz sends customers an email if the price drops below their set threshold. With individual pricing, Orbitz would pass the Deal Detector price to the airlines and they would decide if they want to meet the threshold price on a case-by-case basis.

Non-Ticket Revenues With ancillaries accounting for 18 percent of total revenues at Ryanair, and this number still growing, it’s no wonder airlines around the world are taking notice. The profit margins on ancillaries are much higher than on the commodity based sale of seats, and gross profit margins could be as high as 40 percent with very low selling costs, particularly if sold online. Ryanair says they are only scratching the surface today, and in fact two years ago, they didn’t have a manager who had sole responsibility for this area. Air Canada has developed a set of fare families that show “the trade-off between fare levels and the services offered. Available products are displayed with a clear mention of the services associated with them. The customer can be influenced by such displays. If the customer asks for the economy cabin, the airline can display a special business fare and the price difference is justified by the value delivered to the customer. To compete with the LCCs, Air Canada has adopted a display that shows one-way fares in a combinable calendar format for competitive routes. 57

Air Canada is an example of an airline that has adopted a value-based pricing model in which customers choose fare products based on the attributes associated with each product. In some markets, all advance purchase requirements have been eliminated. They have created four branded “fare families”, Tango, Tango plus, Latitude and Executive Class. Each fare family has specific attributes that target a particular market segment. For example, Executive Class passengers have priority check-in, baggage handling and boarding, advance seat selection, and access to special airport lounges. Since we know that flexibility is particularly important to the business segment that this brand targets, these tickets are also fully refundable. Fare products in the least expensive fare family, Tango, do not have any of these attributes. Tango tickets cannot be cancelled or exchanged without financial penalty and do not even earn frequent flyer points. Measuring performance of ancillary product sales is still an area that needs to be worked on, as well as where to assign revenues.

Hybrid Inventory Nesting In response to competition and low-fare airlines, some airlines are redesigning their business models. This has created a need for other inventory control techniques. For example, since the Internet has created an alternative distribution channel for airline tickets, a hybrid nesting structure has emerged to help manage requests for seats via the Internet somewhat independently of the seats sold through more traditional means.

Flight Category Treatment

The flight category treatment is to set price points according to the demand and passenger mix associated with each flight day of week and time of day pattern. Not only do customers have buy up opportunities, but now they also have buy across with the introduction of fare families. 58

Fig.14 Source: www.aircanada.com

Once customers have chosen their flight and fare family, they can begin the online booking process. During this process they can add or delete certain “a la carte” attributes which will increase or decrease their ticket price.

Figure 14 shows two options being offered to a passenger booking a Tango Plus seat. If you don’t want to check any baggage, $5 will be deducted from the fare price. In Canada, 10 percent of Air Canada’s customers choose to opt-out of checking baggage.

If you would like to buy an onboard Café voucher for meal service, $5 will be added. 25 percent of Air Canada’s domestic passengers opt out of frequent flyer mileage points for a reduction in price. 59

“A-la-carte” Access to Lounge

Fig, 15 “a-la-carte” option allows access to the airport lounge for an additional $25.

Change in the Air

. Paper tickets soon to be a thing of the past

. Travel agency commissions have all but disappeared in North America and Europe

. Online sales through airline websites continue to boom

. GDS and airlines have signed peace agreements

. GDS “a-la-carte” desktop agency solution

Many GDS operators are reinventing themselves in order to stay relevant as a distribution partner. In addition to expanding and diversifying their products and services, their focus 60 is on reducing internal costs and finding ways that costs are borne by users not the airlines. They are now offering more flexible subscription terms.

Galileo, a leading global distribution system (GDS) and subsidiary of Travelport, and Air Canada today announced in August 2007, a multi-year agreement for a revolutionary graphical agency desktop solution that will provide Galileo-connected Canadian travel agents access to the full range and attributes of Air Canada’s innovative à-la-carte fare products and Flight Passes. Air Canada will become the launch customer of the desktop solution, developed by Galileo and powered by Air Canada’s direct-connect application programming interface (API) platform called “AC2U.” Galileo is the first and only GDS to offer a full merchandising solution capable of supporting the full range of Air Canada's fare and Flight Pass products. The new desktop solution offers Galileo agencies full product descriptions, the ease of a graphical display and prompts the user when product options are available. All information is seamlessly integrated into the travel agent accounting and back-office systems.

Global Distribution

As long as airlines sell outside of their home countries, where they are not known presences, they will need an intermediary such as the GDS

With new smaller jets the low-cost carriers need to get higher yielding passengers to make money. They needed the reach the GDSs give into corporate markets and clients where they could get higher yielding traffic. The low-cost carriers are being approached by the GDSs with offers of a lot more flexibility both on what they give and on the economics. 61

Credit Cards

$3 billion certainly has the attention of airlines, because that is what they cumulatively spend worldwide each year on credit card merchant fees. Credit card fees are trending higher. Some card companies had recently hiked rates to 3%, well above the historic averages of 2.2% to 2.5% for carriers. Airline officials report there is no negotiating with credit card companies.

Having already cut labor, commissions and GDS costs, the carriers now see credit card fees as the next opportunity for meaningful savings. That leaves two options: finding lower-cost alternatives to credit card payments or foisting off credit card fees on someone else -- ultimately, the customer, which could violate contracts with credit card companies and might even be illegal. Southwest and Northwest have introduced the PayPal service on their Web sites. This is especially interesting to customers who do not want to use their credit cards 62

Chapter-7

Conclusion

Within the airline industry, market dynamics and RM are evolving. However, the basic theory of RM remains unchanged. The primary objective of RM is to maximise the profitability of the company by applying knowledge about the market and the competition, and by using RM systems and tools effectively.

As the evolving trend suggests, revenue management, CRM and how products are distributed are converging with strong inter-dependencies that require a holistic view to understand business impacts and how the various customer touch points need to be managed. The continuing evolution of pricing and revenue management is a winning proposition for both the airline and the customer.

Airlines who succeed in hiring people with the right skill set, establishing robust RM processes, and applying RM principles through use of optimal systems and tools are bound to increase incremental revenues 63

References

Gary Parkar, Revenue Management:An Evolutionary Revelation, Singapore, Aug 21-22, 2007 Ben Vinod, Advances in inventory control, Journal of Revenue and Pricing Management, Volume 4 Number 4, 7th October, 2005 Ben Vinod, Continuing Evolution of Revenue Management: Customer Centric Revenue Management, November 29, 2006 E. Andrew Boyd, Airline Distribution and the Practice of Revenue Management Dr. Peter P. Belobaba, Revenue and Competitive Impacts of O-D Control: Summary of PODS Results, INFORMS Revenue Management Section Meeting ,New York, June 7-8, 2001 E. Andrew Boyd,, Revenue Management and Dynamic Pricing: Part I Stefan Pölt, Revenue Management Tutorial, AGIFORS Reservations & Yield Management Study Group Berlin, 16-19 April 2002