Evaluating FFP Programs

8/9/2019 Evaluating FFP Programs

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Evaluating Frequent Flyer Programs from the air passengersperspective: Prospects for market segmentation

Juan Carlos Martn

Department of Applied Economics Analysis. University of Las Palmas GCEdificio Departamental de C.C. E.E. y E.E., mdulo D, Campus de Tafira

35017 Las Palmas, Espaa; Tel: 34 928 458189; Fax: 34 928 [email protected]

Concepcin RomnDepartment of Applied Economics Analysis. University of Las Palmas GCEdificio Departamental de C.C. E.E. y E.E., mdulo D, Campus de Tafira


Raquel EspinoDepartment of Applied Economics Analysis. University of Las Palmas GCEdificio Departamental de C.C. E.E. y E.E., mdulo D, Campus de Tafira


Ab t t
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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Abstract:

1 Introduction

Air transport competition can be distorted by the existence of Frequent Flyer Programs(FFPs) (Drabbe, 1996). In this paper, the author claimed that FFPs have been used as acommon strategy employed by the former legacy carriers that cannot be easily matched bythe new entrants. He concluded that the competitive advantage and market power derivedfrom FFPs was something to be scrutinized by European regulators because some

passengers, especially those belonging to the business segment, could prefer airlines havingFFPs that allow them to redeem air miles. So, whether FFPs can constitute a valid strategyto differentiate the market is an open issue that has not been adequately analyzed in thepast.

FFPs have provoked that other industries, such as hotels, car rentals, credit card companiesand gasoline firms had started their own loyalty programs, alone or conjointly with airlines.Therefore, each year there are more air miles earned than redeemed, and the number of

miles awarded to those firms having little or no interest in keeping track of their worth isincreasing year by year. Furthermore, the airlines have reengineered the way in which theavailability of seats on the most popular redemption routes can be redeemed increasing thesearching costs of the awarded miles.

Gudmundsson et al. (2002) estimated that, given that the average frequent flyer is amember of approximately three programs (Webyer, 2002), there are about 69 millionindividuals engaged in the frequent flyer programs of the branded alliances. Gallacher(1997) asserts that the major alliance partners strengthened the ties between their FFPs,b hi b i fl b i l h h i


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FFPs have helped to develop own functional areas and service businesses with quiteindependent objectives and structures. Therefore, in these cases, these units are primecandidates to become separate profit centres or be outsourced. Gallacher (1999) argues thatbilateral FFP are less effective in attracting high-yield passengers than their multi lateralcounterparts. In fact, as Whitaker (1998) suggested airlines competition will be upon detailssuch as which alliance has the best connecting possibilities, most efficient check-in atairports, most appealing and fastest website, better airport lounges, and provides the most

personal recognition.Therefore, one of the areas on which airlines have focused their attention is how FFPsaffect its profitability. Hence, airline managers need to know how travellers make airlinechoices, as this information generally drives the pricing and yield management policy ofairlines (Proussaloglou and Koppelman, 1995). As the air industry becomes morecompetitve, all airlines are subject to find ways of attracting passengers, through differentstrategies. Of these competitive strategies, FFPs do really matter to air passengers in theselection of airlines.

Airlines can only invest millions of euros in the provision of FFPs if they anticipateadequate rates of returns. However, the managers confronted a number of questions inrelation to such levels of investment: How important are FFPs in passengers' choice ofairlines? Are passengers really willing to pay a premium for FFPs?, and, if so, how much?Do FFPs have an adequate rate of return? Are FFPs justified? It is evident that the benefitsthat can be accrued using airlines which have become affiliated to an FFP have madepassengers to consider more than the price and the standard of airlines when making a

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The objective of FFPs is how to maintain customer loyalty being this scheme profitable forthe airlines. They are based on the argument that passengers need to find changing airlinesaway from one offering loyalty bonuses difficult and costly (Klemperer, 1995). Therefore,this strategy plays an important role in the airline industry, and not many empirical studiesexist trying to measure the isolated effect of FFPs. Carlsson and Lofgren (2004) found thatEurobonus substantially increased the costs (12% of the average ticket price) for passengerswho change SAS airline for domestic Swedish routes. Proussaloglou and Koppelman

(1999) also indicate that FFPs are effective reward systems for repeated purchases thatmake demand less price elastic.

Many FFPs are currently in operation and all of them have an important influence in traveldecisions. FFPs were initially devised by airlines to encourage customer loyalty from thosepassengers who travelled frequently - mainly as a result of increasing switching costs to thecompetitors. Such strategy is based on rewarding schemes by offering their membersupgrades from tourist class to business or first class, free flight tickets or other advantagesin check-ins, priority on the waiting lists of overbooked flights, extra baggage allowancesor business lounges. These benefits are obtained by converting a certain number of 'airmiles' which are accrued each time the member takes a flight with the same airline intobasic points or credits. The FFPs retain details of all the flights and record all the points orbonuses that have been accumulated to date. Therefore, FFPs create artificially importantswitching costs to the passengers because FFPs members are awarded with a number ofmiles or points in return for every flight taken with a particular airline. The amount of milesor points is usually determined by the distance flown, with a bonus ranging from 50% to

200% f fli ht t k i B i Fi t Cl S i li d il b t1


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2.1 The questionnaire

SP experiments offer the decision maker hypothetical scenarios that need to be realistic3

inthe context of the experiment, and the preferences expressed by the individuals indicate therelative importance of the attributes that characterize the scenarios. A fundamental reasonwhy SP methods have become quite popular is their ability to mimic consumers decisionsmade in real markets that otherwise would be difficult to be observed. With a greaterdiversity of levels of service and fares, the ability to identify preferences and to infer the

WTP for specific service levels like the accession to FFP makes SP very appealing (Rose etal., 2005).

Most air passengers are sensitive to airline travel cost. However, service quality also affectspassengers choices, but is in many ways subjective. A comprehensive literature review hasbeen made to extract a prior list of important attributes which were used in previous studies(Alamdari, 1999; Mason, 2000; 2001; Evangelho et al., 2005). In summary, these priorstudies found that the most influential factors affecting business passengers were reliability,

punctuality, seating comfort and schedules, and price was the most important factor forleisure passengers. However, FFPs were also an important feature which cannot beneglected in the analysis of airlines service quality.

Based on the literature review, passengers opinions extracted from personal interviewswith members of our department, who are frequent passengers of the route, and our ownpersonal experience toward different airlines services, a list of 24 attributes with differentlevels of service was obtained. As suggested by theoretical considerations, not all thecontext-dependent service quality attributes can be studied using SP techniques. Thus, we

l d f f d b 7 l h i li


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experiment was adapted for each traveler, and offered three different levels that werepresented by verbal descriptions.

A similar approach was used for the attribute named penalty for changes, but in this casethe proportionate change method was employed, adapting the scale of the controlledchanges by the type of ticket that passengers had purchased. The changes were restricted tothose which travellers are expected to be confronted with. The 100% penalty was onlyoffered to passengers that have paid a coach fare, the level of attribute change to a range of

penalty between 0 and 30 percent was limited to the passengers who fly in business or firstclass4.

Comfort was described to respondents as having more legroom in their seats (using the factthat their knees will not touch the front seat in any case). We were first tempted to use twoor three different seat-pitch. Seat pitch is referred to the horizontal distance between thesame part (i.e., front) of two seats in consecutive rows of an aircraft, and thus, greater seatpitch should be weakly preferred by passengers (all other things equal) to less seat pitch

5.

However, during the focus group we realized that it was better to verbalize therepresentation and to specify it at two different levels.

Where a given difference between the two alternatives could be composed in different waysmixing all the possible attribute levels in some order6, in this analysis a fractional factorialdesign to reduce the number of choice scenarios to 27 has been considered. This designallows us to estimating two-order interaction effects among some attributes, namely price, penalty for ticket changes and frequency. Thus, the experimental design involved 27possible comparisons of virtual airlines A and B.


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3 The data

Our SP experiment was implemented through questionnaires ministered at Gran CanariaAirport, near the boarding gate of the flight, after travelers have gone trough securitypremises. A total of 310 questionnaires were completed during the last two weeks ofFebruary 2005. The interviews were done by four college students that are familiar withdiscrete choice analysis and were properly instructed to carry out the task. They worked on

6 hour shifts for two weeks, and their shifts were adequately scheduled so that the studentswere at the boarding gates of each of the flights scheduled in the route under analysis. Thus,all the flights of this route were properly sampled in order to obtain enough representationof the passengers.

Only people who were about to take a flight from Las Palmas de Gran Canaria to Madridwere interviewed, to make sure that they were familiar with the experiment. Moreover,people who were traveling with tourist packages, namely, packages with a combination ofhotel, air travel, rent a car, and so on, were not considered as these people do not have aclear idea of the exact cost of the air travel portion of their travel package.

The questionnaires had 3 sections. In the first section, a number of questions were askedabout the trip, such as: airline; connection at destination; connecting airline; fare class(business, coach); departure time; trip cost; trip motive; trip frequency; who pays for thetrip; number of people flying with the interviewee; advance of purchasing the ticket; modeof purchasing the ticket; and frequent flyer program. In the second section, the individualswere confronted with the SP experiment. And finally, in the third and last section, the

i di id l k d b t th i i i h t i ti h id


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(70%), and again travellers in this route may be characterised by having a better educationthan the average population of Spain.

4 Model and WTP measures

In this paper, the random utility theory as the theoretical basis of discrete choice models isused. It represents the econometric tool for the empirical estimation of the demand function

in a context of discrete choices. Although there are several interpretations of the randomutility theory, the one stated in McFadden (1974) and Domencich and McFadden (1975)9,

is the formulation commonly used by economists. It states that the utility of alternative j forindividual q is given by:

jq jq jqU V = +

where jqV is the representative or systematic utility (observed by the modeler) and jq is a

random term that includes unobserved effects. jqV depends on the observable attributes ofalternativej as well as on the socio-economic characteristics of individual q.

The dependent variable represents the individual behaviour and it is a discrete variable.This is therefore a probabilistic model and, from its estimation, we can obtain theprobability distribution of the dependent variable for every individual observation. Hencethe probability that individual q select the alternative j is given by the expression:

( ) ( ( ) )jq jq jq iq iq iq jq jq iqP P V V i j P V V i j


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We estimated two alternative specifications of the conditional indirect utility, describedbelow. In Model 1 (MNL1), we only consider interactions between FFP and cost, and FFPand frequency. Therefore, the conditional indirect utility reads as:

1 2 2

1 2

* 1 * 2 *

1 2

( ) ( ) ( )

1, 2

j C C FFP P F F F FFP LR Fr Fr FFP

R R

V FFP C P F FFP F LR FFP Fr

R R j

= + + + + + + + + +

+ + =

Table 3 summarizes the variable names and its acronyms used in our models as well astheir expected sign. In our case, the effect produced by the joint variation of FFP and otherattributes is analyzed, showing that the total effect differs from the sum of the individualeffects produced by variations of each attribute separately. When this happens, the variablesare said to interact. The presence of interactions conditions the functional form of the utilityfunction imposing restrictions on the marginal utilities. The linear specification is notappropriate then to model these situations because marginal utilities are constant. In this

case, second-order terms of the Taylor expansion that include the products among thevariables that interact with each other are added to the linear part of the conditional indirectutility function.

The second model (MNL2) considered also the interactions of FFP and the passengers whopay for their tickets. The conditional indirect utility is expressed as follows:

1 2 1 2

1 2 3

1 2 1 2

* 1 * 2 * 3 1, 2

j C P F F LR Fr R R

C D C D C D

V C P F F LR Fr R R

C D C D C D j

= + + + + + + + +

+ + + =,


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the level of food and the frequency of the airlines. Here, the positive value of the interactionbetween FFP and the price of the ticket can be interpreted as if part of the cost of the ticketis not seen as a disutility for FFP passengers because it can be recovered from theredemption points.

The model MNL2 is statistically better than MNL1 (see the likelihood value), and itrepresents a much more informative model because it accounts for some interactionanalysis which can help us to pinpoint whether FFP passengers form a different markets

segmentation per se, or its interaction with the payer of the ticket should be furtherinvestigated. In fact, it can be seen that the market segmentation is better addressed for theattribute which accounts for whether the payment is made by passengers or companies thanfor the association to some FFP. We also highlight that when passengers pay for theirflights, the disutility associated with the cost is higher than the one experienced for thegroup of passengers whose flights are paid by their firms. These results are in the line ofthose obtained by Mason (2001). In this paper, the author even reported that businesstravellers who worked for companies that had a corporate travel policy conform a different

market segment to those who work for small and medium companies. This result indicatesthat the socioeconomic characteristic of who pays for the ticket has a very importantinfluence in the WTP figures of the next section. In this study, it can be seen that thesegmentation of the FFP when passengers pay their tickets is not straightforward. In fact, itcan be seen that the disutility associated to the cost could be lower for the passengers whoare adhered to some FFP. This result can be, in principle, counterintuitive, however someincome effect could exist and help us to explain this estimation.

4 1 WTP


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1 0

kj

j jj

qj

V VWTP

V

I

=

(1)

Where 1jV is the utility when the level of the attribute qkj is 1, and

0jV the utility when the

level of the attribute qkj is 0.

4.2 MNL1 results.Willingness-to-pay measures for all models are shown in Table 5. It can be seen that themore valued attributes are reliability, comfort(more leg room) andpenalty for changes andthe less valued are those associated with food and frequency. However, we obtained adifference among the willingness-to-pay measures depending on the model specificationwhich shows that the willingness-to-pay are higher for the FFP passengers.

In MNL1, individuals are more willing to pay for compensation with a free ticket than for

the reimbursement of the cost of the ticket in case of delay, but this comment is reversed forthe non-FFP passengers. For having more leg room in the seat, FFP passengers have awillingness-to-pay of almost 40 euros. Thus our paper finds evidence that FFP passengersare willing to pay a premium for the extra legroom which is higher than the premiumperceived by non FFP passengers. So, it is likely that in the near future some of the airlinesthat are servicing this route will include programs similar to the ones that have appeared inthe past in the US, e.g., Americans More Room Throughout Coach or Uniteds PremiumEconomy programs. However, the success of these programs depend basically on how

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result is consistent with the quality associated to these levels. The results for non-FFPpassengers are also plausible, because they are willing to pay almost five euros forchanging from level 0 to level 1 and 6.27 Euros for changing from level 0 to level 2. Thesefigures are similar to the prices that airlines actually charge for food in Gran Canaria-Madrid route in economy class. Looking at Iberia menu brochure, we see that freshgourmet and cured ham ciapatta sandwiches cost 7 and 7.50 euros, respectively. However afresh menu that includes a fresh sandwich, still mineral water and a snack costs only 6

euros. However, we would like to remark that these menus have a fierce competition madeby some cafeterias that are located in the passengers terminal building, where it is possibleto find a menu for 5 euros including a similar basket of articles.

Nowadays, coach passengers do not have an option to buy a menu including some hotdishes in this route. It was long time ago when passengers could enjoy a hot meal in each ofthe companies servicing the route. The days when catering companies provided hot menusto airlines and all the passengers were entitled to enjoy the food have passed. Thus, it isapparent that service-quality competition, regarding in-flight meals, has evolved along the

timeline going from a well known hot-meals service to the simple no frills in-flightservice and lower average fares, in which all the services are sold as value-added totravelers with higher willingness-to-pay. So according to Morrison(2001) and Dresner et al.(1996), it seems that in this route the business model that was named low-cost-carrier(LCC) has emerged as the equilibrium point of the models of spatial competition regardingservice quality (Hotelling, 1929), and full service model (FS) is history.

4.3 MNL2 results.

V diff l b i d i h l f h d l MNL2 I b h


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comprise hot meals freshly prepared each day and will complement the existing standardmeals on offer, which will continue to be offered inclusive of the price of the flight11.

Focusing our attention on the important comfort variable, we can see the highest WTP forFFP passengers that do not pay their flights who are willing to pay around 100 for havingmore space between the rows of seats. However, we also see, that in the case of FFPpassengers who pay their tickets, the WTP shrinks to 14 . This is an interesting result thatdeserves a further analysis, and airlines should have this in mind before starting such

initiatives. FFP passengers who do not pay their flights are also more willing to pay tobenefit from more frequent flights per day. In this case, we see that the WTP is 20 Euros foreach additional flight in comparison to 3 Euros of passengers who pay their tickets.

Travellers have been experiencing during the last decade increases in delays and flightcancellations, and customer dissatisfaction with air carrier service has grown parallel tothis12. There is no single solution to the growing problem of delays and the resultingconsumer concern over air travel has usually been mitigated by some airline with some

aggressive compensation policy handling delays with equivalent free tickets or costreimbursement. Spanair was the first airline in Spain introducing its punctuality guaranteepolity in the year 2001 in some of its routes, but after three years of implementation thecompany extended the policy to all scheduled domestic routes with effect from 15th ofJanuary of 2004. The guarantee provides passengers with a free ticket if the aircraft doorsclose more than 15 minutes after schedule due to airline-related delays. With this policy,Spanair seek to differentiate its product from the rivals along a dimension, that we haveshown, is valued by passengers. We suppose that the company has been rewarded for the

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compensating passengers with free tickets in the same route or other complementaryscheme giving passengers a free bonus with an accumulation of miles equivalent to thedistance of the delayed flight.

Thus, we have shed some light of the willingness-to-pay for different systematicheterogeneity: FFP vs. whether passengers pay their flights or not. We see that WTP areimportant in all the cases, and that there is a better explanation for the observedheterogeneity due to whether firms do pay the tickets. This heterogeneity can be further

analyzed using the new generation of mixed logit models.

In summary, the airlines need to find a balance between cost leadership and productdifferentiation strategies. It is clear that value added services increase airline costs. Thus,airlines need to find out whether the fare premium could cover the extra costs associated tothe policies of product differentiation. In this paper, some fare premiums (WTP) have beenobtained and these could help airline managers with deciding whether some productdifferentiation or market segmentation strategy is worth-while. Alamdari (1999) argued that

although passengers increasingly appreciate the provision of in-flight entertainmentservices during long haul flights, they do not appear to be prepared to pay for it to theextent to cover the full costs of the systems. More over, since many airlines do not chargetheir passengers for this added value service, it is increasingly hard for others to introducecharges. In a context of low cost airlines, Alamdari and Fagan (2005) found little evidenceto prove that passengers were prepared to pay for added value services and argued howairlines like Ryanair, that follows a model more closely to the original low-cost modelinitiated by Southwest, can achieve lower costs to the benefit of its profit margins. Thus,

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A novel feature of this study is how different attributes have been presented to travelers byan adapted SP experiment and how these attributes represent global airline service quality.The aim of this paper was to present how certain attributes were valued by passengers.Thus, airline managers can assess the real possibilities to depart from their actual strategyin pursuit of other strategies (product differentiation and market segmentation) which helpairlines being more profitable. Airline managers need to assess whether these strategies,such as increasing new frequencies, marketing new products directly to firms, introducing

new programs of more legroom, are or not payoff. On the other hand, it could also happenthat our results find strong evidence suggesting that higher yields do not compensate theextra costs associated with the provision of added value services, as in the previous studiesof Alamdari (1999) and Alamdari and Fagan (2005).

Comparisons were made with other previous references and with results observed in thereal world. Taking our results as a whole, our conclusion is that WTP measures are notinsignificant in any case, and that different stakeholders of air industry (passengers, airlinesmanagers and regulators) should pay attention to these figures in order to apply sensible

policies regarding this type of issues.

The fact that Iberia started an ambitious program of in-flight catering service and Spanairinitiated a punctuality guarantee program aimed squarely at the results obtained in ourpaper. WTP are usually higher for FFP passengers when the ticket is paid by the firm, andmany leisure travelers are likely to choose the lowest-priced carrier, regardless of servicequality.

We also claimed that SP methods may be fruitful assessing the full impact of serviceli i i b i li A d f i li b f l i h k


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References

Alamdari, F. (1999) Airline in-flight entertainment: the passengers' perspective, Journal ofAir Transport Management 5, pp. 203-209

Alamdari, F. and Fagan, S. (2005) Impact of the adherence to the original low-cost modelon the profitability of low-cost airlines, Transport Reviews 25(3), pp. 377-392

Carlsson, F. and Lofgren, A. (2004) Airline choice, switching costs and frequent flyerprograms. Department of Economics, Gothenburg University.

Domencich, T. and McFadden, D. (1975) Urban Travel Demand: A Behavioural Analysis,North Holland, Amsterdam.

Drabbe, H. (1996) Frequent flyer programs competition aspects. European Air LawAssociation Conference Paper, pp. 1-8.

Dresner, M., Lin, J.C. and Windle, R., (1996). The impact of low-cost carriers on airportand route competition. Journal of Transport Economics and Policy 30, pp. 309-328.

Espino, R., Ortzar, J. de D., and Romn, C. (2006) Analysing demand for suburban trips: amixed RP/SP model with latent variables and interaction effects, Transportation 33,pp. 241-261.

Evangelho, F., Huse, C. and Linhares, A. (2005) Market entry of a low cost airline andimpacts on the Brazilian business travellers. Journal of Air Transport Management

11 99 105


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Whitaker, R. (1998) Whos ready for a cosy threesome? What are the implications of threealliance groups dominating the airline industry? Airline Business, 7

Wittink, D.R. and Cattin, P. (1989) Commercial use of conjoint analysis: an update, Journalof Marketing 53, pp. 91-96.


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Tables and figures

Table 1. Attributes and levels. Airline SP experiment.

Attribute Level Definition

0 P+20%

1 PPrice

2 P-20%Business/1

stClass fare Coach

0 30% 100%1 10% 50%

Penalty for changes inthe ticket

2 0% 30%

Business/1st

Class fare Coach

0 Cold sandwiches + drink Not available

1 Hot food + drink Cold sandwiches +

drinkFood

2A la carta (when the passenger

buys the ticket)Hot food + drink

0 Small space between seatsComfort

1 Wide space between seats

0 2 flights/day

1 4 flights/dayFrequency

2 6 fli h /d


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Figure 1. Screen shown to travelers in the SP experiment


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Table 2. Descriptive statistics.

Tri

pCost()

Net

household

month

lyincome()

Weeklyworking

hours

Net

individual

month

lyincome()

Age

Observations 310 310 235 310 310

Min. - 300.00 5.00 - 19.00

Max. 320.00 15,000.00 126.00 9,500.00 74.00

Mean 93.25 3,088.00 46.88 1,677.49 35.47

S. Deviation 64.12 1,716.00 17.03 1,296.66 12.31

Variation C. 0.68 0.55 0.36 0.77 0.34

Median 75.00 3,000.00 40.00 1,500.00 32.00

Table 3. Modelling variables. Airline SP experiment.

Level of service attributes

A i i


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Table 4. MNL Estimation results. Airline SP experiment.

MNL1 MNL2

-0.0184 -0.00854

(-7.7) (-3.1)-0.0031 -0.00454

(-2.2) (-3.1)

0.0902 0.1370

(1.3) (1,9)

0.244 0.2524

(2.7) (3,3)

0.5323 0.6048

(8.7) (9,6)0.0781 0.1211

(3.3) (6,2)

0.7755 0.8202

(10.5) (11)

0.7029 0.7686

(9.0) (9,7)

0.00504 -

Reliability level 2 (R2) F2

Travel cost*Frequent Flyer Program (C*FFP) C*FFP

Frequency (Fr) Fr

Reliability level 1 (R1) R1

Food level 2 (F2) F2

More leg room (LR) LR

Penalty for changes in the ticket (P) P

Food level 1 (F1) F1

Parameter

Estimates

(t-test)

Travel cost (C ) C


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Table 5. Willingness to pay measures. Airline SP experiment

Attribute MNL1 MNL2 Attribute MNL1 MNL2Penalty for changes in the ticket ( / 1%)) Frequency (/service)

Average 0.2 0.37 Average 6.95 9.75

- Frequent Flyer Program 0.24 0.47 - Frequent Flyer Program 11.07 12.58- Traveler pays the ticket - 0.1 - Traveler pays the ticket - 2.68

- Other pays the ticket - 0.76 - Other pays the ticket - 20.26

- Non Frequent Flyer Program 0.17 0.3 - Non Frequent Flyer Program 4.24 7.88

- Traveler pays the ticket - 0.12 - Traveler pays the ticket - 3.26


Food: level 0 to level 1 () Reliability: level 0 to level 1 () = Free ticket

Average 5.63 8.92 Average 48.36 66.02

- Frequent Flyer Program 6.74 14.23 - Frequent Flyer Program 57.9 85.18






Food: level 0 to level 2 () Reliability: level 0 to level 2 () = Reimbursement

Average 10.47 20.32 Average 43.84 61.86

- Frequent Flyer Program 13.24 26.21 - Frequent Flyer Program 38.14 79.82

- Traveler pays the ticket - 5.58 - Traveler pays the ticket - 17,00





More leg room ()

Average 33.2 48.68

- Frequent Flyer Program 39.74 62.81

- Traveler pays the ticket - 13.38

- Other pays the ticket - 101.17

- Non Frequent Flyer Program 28.88 39.37

- Traveler pays the ticket - 16.29

- Other pays the ticket - 70.84

Evaluating FFP Programs

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