Airline competition analysis

Flight Transportation Laboratory September 1968

AIRLINE COMPETITION

FTL Report R-68-2

REPORT R68-2 AIRLINE COMPETITION

Conclusions 2

Introduction 3

The Available Data ₅

The Techniques of Analysis 7

Multi-Regression Models 11

Model Incorporating Schlach's Plan 21

Model Incorporating Quality of Service Index 28 Effect of Behavioral Elements 36 Possibilities of Further Work In This Area 39

Bibliography 40

Appendixes

A - Graphical Analysis 41

CONCLUSIONS

The object of the study was to predict market share that

an airline gets when operating in a given market and competing with

other airlines. Although market share depends on many factors, the

conclusion drawn from this study was that the dominant explanatory

variables are the frequency share and the number of competitors

operating in the market. To the first approximation the relationship

between the percentage market share and percentage frequency share

is almost a straight line. However on bringing in the third variable,

number of competitors, we obtain a family of S-shaped curves.

Distortions exist due to other variables, which appear

to have some affect on the market share. We have data on some of those

variables, but data on behavioral variables does not exist. Examples

of behavioral variables are loyalty of a passenger to travel by a certain

airline and breakdown of market by types of passengers, i.e. a passenger

INTRODUCTION

The purpose of this study is to investigate how airlines

share the passengers, attracted to air transportation. The percentage

of these passengers, that one airline will carry in any given market,

where it is in competition with other airlines, depends mostly on the

frequency it operates, number of competitors in the market, stage length

number of daily non-stop and one-stop flights, type of aircraft and the

connecting flights.

Market share furthermore depends on variables such as

passenger service, the image of the airline, the amount of money

spent on advertising in the given market. These variables are called

behavioral variables and are impossible to be included in the models

because of the inavailability of data on them. In this study investigation

was restricted to the variables on which data can be fairly easily

obtained.

Until recently it was assumed that market share depends mostly

on frequency share and furthermore that the relation between these is

linear. This implies that if for example one airline operates 70% of the

flights in any given market, it will carry 70% of the passengers in that

market. The models presented in this study investigate this hypothesis

as well as the effects that some of the other variables have on the

market share.

Model 1 investigates the effects of varying stage length and

frequency share. Model 2 brings in additional explanatory variables

including the second most important variable, number of competitors in the

market. At this stage of investigation it was recognized that market

share depends almost completely on frequency share. However the form

of the relationship was not yet clear. Models 3 to 6 are an attempt

Models 7 and 8 investigate the effects of times of flight

THE AVAILABLE DATA

The sources of data for this study are the Civil Aeronautics

Board's "Competition Among Domestic Air Carriers - Ten Percent Sample"

Volume VII-5, and the "Official Airline Guide", Quick Reference North

American Edition.

The data was taken for the top fifty city pairs for the

period January 1 through December 31, 1966. The actual raw data

and the notes concerning it are given in Appendix B.

PROBLEMS ENCOUNTERED WITH THE DATA

The very first problem encountered was that of service

by type of aircraft. Some airlines operated all jet fleet and some all propeller fleet. The problem came into existence when an airline

operated a mixed fleet. In this study, the service was taken as jet

if the airline operated jet more than 50% of the scheduled flights.

What do we do in the case when United Airlines, for example, operates

two flights daily from New York to Washington, D. C., one being jet

and the other turbo-prop? Besides being unable to define the type

of service, it also affects the flight time. The jet flight takes

45 minutes whereas the propeller flight takes 78 minutes. This problem

was overcome when in Model 8 each flight was individually considered

as to the type of aircraft and flight time.

The second problem was that in few markets some airlines

at city i destined for j in contrast to those passengers who were

merely passing through i. For example on flight from Chicago to

Milwaukee, the percentage of local passengers was very low in some

cases.

Airline % of Local Passengers

Eastern 25

North Central 19

North West 22

United 28

Another example of the same form of problem is illustrated

with TWA data. TWA has a service New York to Boston. Usually the

aircraft is a Boeing 707. Almost invariably this flight originates

from California for example Los Angeles, in which case most of the

passengers on board are not local. Only few passengers board this

flight at New York. So one of the other competitors, who may be

operating a propeller aircraft service is competing with the jet of

TWA, the Boeing 707 jet which TWA may not have scheduled had they

THE TECHNIQUES OF ANALYSIS

The Preliminary Graphical Analysis

The main object of the graphical investigation is to

identify the variables which are most directly related to the market

share. Some variables are seen to have definite relationship with the

market share, others merely show a trend and some offer no explanation

at all. Only the variables which are seen to bear a relationship with

the market share or show a definite trend, are finally used in the

regression analysis. No single line fits the points precisely, yet the

points display a visual tendency to lie along a certain path indicating

some underlying law of association, disturbed by idiosyncrasies in

individual cases.

The Multiple - Regression Analysis

The Multiple regression is used in data analysis to obtain

the best fit of a set of observations of independent and dependent

variables in an equation of the form,

Y = b0 + b ₁x₁ + b2x2 + . . . . . . . . + bnxn

where Y is the dependent vatiable and xi, x2 . . .* xn are the independent variables. Coefficients b0, b, . . . . bn are to be determined. The multiple regression technique manipulates the

the market share and its relevant variables. The program, described

bfiefly below, carries this out in a well defined step-by-step process

which introduces or deletes variables in accordance with the prescribed

criteria, in order to find the expression that fits best with the data.

Deviations from the regression line will be expected since we are only

using a limited number of variables to describe the market share.

In the step-wise procedure, intermediate results give valuable

statistical information at each step in the calculation. Basically we

obtain a number of intermediate regression equations as well as the

complete multiple regression equation. These equations are obtained

by adding one variable at a time. The variable added is the one which makes the greatest improvement in "goodness of fit". The coefficients

represent the best values when the equation fitted to the specified

variables.

The beauty of the step-wise procedure is

1) a variable may be indicated to be significant in any early stage and thus enter, and

2) after several other variables are added to the regression equation, the initial variable may be indicated to be insignificant, in which case it will be removed from the regression equation before

adding an additional variable.

The BMD02R Step-wise Regression Program

This program (Bio - Medical Computer Program BMD02R)

developed by the University of California and modified to a suitable

linear regression equations in a step-wise manner. At each step

the variable added is the one which has the highest partial correlation

with the market share or the dependent variables partialled on the

independent variables which have already been added, and equivalently

it is the variable which, if it were added would have the highest

F value.

There are two F values. "F - to - enter" indicates the improvement that the addition of a relevant variable to the regression

equation would make to reduce the residual error. The "F - to - remove"

is an indication that the variable under consideration is not contributing

very much to increasing the goodness of the fit. The variable with the

highest F - to - enter value is chosen first.

One other feature of the program is that variables, if desired,

can be forced into the regression equation. They will automatically be

removed when their F values become too low. Another feature of the

program is that it has a flexible method of generating new variables

from functional representations of basic input variables. Thus,

besides alinear equation relating market share to explanatory variables,

one can use a logarithmic linear form, or can create squares, sums,

combinations of the independent variables, etc., to be used in a form

of market share estimating relationship.

In the output, besides the multiple regression equation, the

program provides us with other statistics which are useful in judging

the "goodness of fit". One gives the standard deviation which measures

regression line. The other called Multiple R, is the multiple

correlation coefficient and has value ranging from zero to unity.

This statistic, associated with a particular variable, is a measure

of the percentage of the market share variation explained by the

MULTI-REGRESS ION MODELS

Model 1. Market Share- Variation with Stage Length and Frequency. The stage length is best represented by the variable T . .

If for example three airline operate in a given market, then the airline which takes the least time to fly from i to

j,

represents

T . min.

Define K .

-Ti

tO

where = total system

=actual flight

= average wait distribution aircraft.

(trav). - t. -- f(

(travel) time for airline k from city i to

j

time for airline k from city i to

j

time for a passenger arriving with poission with varing ; , before he can board the

From the previous studies performed in the Flight Transportation

Labortary, it has been found that W

T/2-N

where T is the total time

the airline flies the aircraft daily and N is the total number of daily flights, namely the frequency. Flights are assumed to be scheduled

from 6 A.M. to midnight daily, giving T the value 18. K

_N -(.)

So with t .. and N. . known, we can determine T .. for airline k. Let P.. = # of passengers from i to

j

on airline k.

k

P..

=

P.. = total # of passengers from i to

j.

1] k

1]

and so = market share of passengers for airline k in the city pair ij.

Our model assumes that

X

where K.. = constant

1]

Let I woK

We have

/(C)

(F7

,') P

_{- . - - - - . . (4 )}

Equation (4) states that market share for airline k in city pair ij is equal to some constant time minimum flight time to some poweroe multiplied by relative time for the airline to some power

The product form of equation (4) has to be changed into linear form before regression analysis can be performed on the computer. Taking logs of equation (4), we obtain

log (market share) = a + a log(Tm) + a log(T k

0 1 min 2 r

where 6, = w

From regression analysis we obtain

-0.219

-2.56 M.S.= 895 T . T min r Multiple R = 0.6572 Std. Err. of Est. = 0.8498 F-Ratio = 55.471

Example. Eastern Airlines. Boston - New York Market.

T = 34 minutes.

min k

t.. = 48 minutes. IJ

N = 38 non-stop flights daily.

T = t.. + 18 _ 2.25

-0.219 _-2.56

Model 2.

In this model we wanted to see how market share relates to the independent variables.

Number of non-stop flights. (NSTOP)

Number of one-stop flights. (lSTOP)

Minimum flight time. (T _min. )

Frequency Share (%) (FREQ) Number of Competitors. (Nc

M.S. = K (NSTOP) (lSTOP) (T _min.i)

or log(M.S) = C + a log(NSTOP) + log (lSTOP) + log (T _min. ) + 6 log (FREQ) + _log(Nc Regression Equation. (FREQ) N - -c 0.162 -0.1536 M.S. = 0.156 T . NSTOP min 1.46 0.288 FREQ N C -. - . (7 Multiple R = C.9173 Std. Err. of Est. = 0.4519 F-Ratio = 191.170.

Example. Eastern Airlines Boston- New York Market.

T . min = 34 minutes NSTOP = 38 minutes FREQ = 53 N =5 c

On substitution of these values into the regression equation we obtain,

M.S. = 81 %.

that the dominant explanatory independent variable was percentage frequency share. This was the first variable to come into the regression equation. The regression equation at point appeared as, 1.30 M.S. = 0.308 FREQ Multiple R = C.9112 Std. Err. of Est. = 0.4629 F-Ratio = 718.810

Using the Eastern Airline example, we obtain Market Share to be 54%. In the next part of the analysis of market share constration was placed on the explanatory frequency share. Two models were tried and their description is given below.

Previously it had been suggested that to the first

approximation the form of the relationship between market share and frequency share is linear. This implies that if for example one airline operates 70 % of the flights in any given market, it will carry 70 % of the passengers in that market.The hypothesis made in this study is that the actual form of the curve is S-shaped.

Model 3. Market Share vs Frequency Share. The Cubic Model. Let X = Percentage of Frequency Share

Y = " Market " then

Y = A X3 + B X2 + C X + D-- - -(-The regression equation obtained was,

Y = - 3.1 + 1.06 X - -Multiple R = 0.9187

Std. Err. of Est. = 8.9438

F-Ratio = 794.908

Using the above Eastern Airline example, we obtain Market Share to be 53 %.

Once again the analysis shows that frequency share exlpains most of the variation of market share. The reduced form of Model 2,

equation (8) and Model 3, equation (10) produce identical results. Furthermore equation (10) of Momdel 3 does varify the widely held assumption that to the first approximation, the relationship between market share and frequency share is linear.

Model 4. Market Share vs Frequency Share S-shaped Curve.

With this and Models 5 and 6, we test the hypothsis that market share is mainly a function of frequency share and the number of

competitors in the market. These two independent variables showed some correlation with the market share in the graphical analysis.

In Model 4, we again assume that

Y = A X3+ B X2+ C X + D - -

-Now we attempt to bring the variable, number of competitors into equation (11).

r~/1 51/

ASSUMPTIONS.

1. The curves are S-shaped and pass through the points (0,0) and (1,1).

2. Each curve crosses the 45 degree line at X=- , where n is the n

number of competitors in the market.

Conditioning equation (11) to the above two assumptions,we obtain

Regression equation obtained from the first step,

M.S. = 0.99240 x Frequency Share. - - -Multiple R = 0.9740

Std. Err. of Est. = 8.946

F-Ratio = 901.198

Comparing equation (12) and (13), we obtain

M.S.~~ 4. ( -0 - +- - .. (F-0

On substitution of the values for our Eastern Airline

example, we obtain from equation (14) the market share to be 53 %. This is once again comparable to the previously determined values.

Model 5. Market Share vs Frequency Share and # of Competitors. This model is the same as Model 2,except that the data has been screened to conform more to the basic assumptions. The data used. in this model incorporates the following assumptions.

1. All flights are non-stops. 2. All flights are jet flights.

3. In every case the percentage of local passengers is greater or equal to 70.

A local passenger is defined as a passenger who originates at city i and travels to city

j.

Any passenger who is on flight at city i and who may have come from city such as h destined for say city

j

or k, is not counted as a local passenger.

Using this data and with model as in 2, we we obtain the following regression equation.

M.S. = ( F.S.) 1.04 No02 3 c

Using the Eastern Airline example , equation (15) predicts the

market share to be 45.5 %.

One must be extremely cautious when screening the data.

Certain markets can not be left out because of peculiar characteristics. For example Boston-New York, Eastern Airlines offer a shuttle

service. In the market Denver-Chicago, Continental Airlines offer a special fare.If original data was to be screened continously,

then the remaining data will produce results to any desired accuracy. In such cases intelligent judgements have to be made as when to stop

Model 6. Market Share vs Frequency Share and # of Competitors. Let

Y= Market Share X= Frequency Share

Nc = # of Competitors

In this model we investigate the relationship given by,

where is constant.

Applying the condition that the curve crosses the 45 degree line at

X

4 _{- where N is the # of competitors, we obtain}

L

c

A family of curves was plotted for various values of N and c given intervals of X. The details are shown on the graph on the previous page.

The Regression Equation - Model 6.

Multiple R = 0.9654

Std. Err. of Est. = 0.0043

F-Ratio = 668.002

Using our Eastern Airline example equation (18) predicts the

~~m-r---t--- -n--- - -~ I

.1

El +

4-r

i

_ 1 _ __ 1 4 4 ~1 I 1 41 ~

I

I-- -~ C--- ~--i;,---~ I 74 -k

---

--

-7

At this point of investigation it is clearly indicated that number od daily flights that a particular carrier offers in a given market influences the percentage market share that can be obtained. The question which came

to mind at once was: Given a carrier schedules a certain number of flights on any given day then how is the market

share affected by the times of the day at which he schedules his flights?

A businessman would tend to take flights which suit his time better, rather than showing preference for an airline because of loyalty or better passenger service on flight. He would be more interested in taking flights which originate between the hours of 8 and 10 in the morning

and 4 and 6 in the evening. This suggests that we should perhaps investigate frequency distribution throughout the day rather than total number of daily flights. If in any given short-haul market, there exists a very high percentage of travelers on business, then it is quite possible that a carrier could schedule most of the flights during the critical hours and thereby obtain a high percentage of market share.

THE VALUE OF TRAVEL TIME DEPENDING ON TIMES OF THE DAY

In a comparative study on air transport and surface media published in September 1966, Mr. Scharlach of Deutsche Lufthansa (Reference 3) examines the weighting of times of the day as a factor in transport demand for day return trips. In the case of the German domestic network he weights the times of the day according to professional and psychological

WEIGHTING OF TIIES OF THE DAY

AS FACTOR IN TRANSPORT DEMAND FOR DAY RZTURN TRIPS

(from Monday to Friday)

24 hours

0 6 7 8 9 10 17 18 20 22 23 Non-active Working time Non-abtive

zone zone zone

(B) (A) (B) 0.4 -44 H 4 Unit H Junit 0 4 4 ouo 00 0--0 c 0 0 r- 0 0 b N NO N N 0 N N * N N (Zcci ci c c FI z M 6

7

2 11~ Number of Weighted 31 3 N value of

He hypothesizes that the twenty-four hours of the day do in fact have distinct values for man as a private

individual and as someone working for a living. For the vast majority of business travelers, the times between

10 a.m. and 5 p.m. are the busiest in their working day. In general, times between 8 and 10 a.m. and between

5 and 7 p.m. have, comparatively speaking, a lesser value in the professional field. Therefore it is these periods which will account for the bulk of business travel - the periods which precede the very busy professional time zone

in the case of outbound trips and those following it in the case of return trips.

An equivalent period for private travel, from 11 p.m. to 6 a.m., during which the sacrifice of time is unwillingly accepted and only if no other solution is available, corresponds to the seven-hour active work period. Depending on how close they are to the two seven-hour periods, the value of the times between these two "units" is - in the

morting - greater first of all in private travel and subsequently in business travel. The pssosite is true in the evening when the value of the times is firstly greater in business travel and then in private travel.

This train of thought indicates, that there is a particularly favorable period - in the morning for departures and in the evening for return trips - which is the most popular for both business and private travel. It is the period from about eight to nine in the morning (with a marginal zone up to ten o'clock) and the favorable period is longer in the evening than in the morning.

The diagram illustrates the preceding data more clearly. The 24-hour day is divided into two "units" - the night unit,

with its hours of rest, and the day unit, with its hours of hard work. Division is further made according to the hour categories defined above, into optional and marginal zones, with the

units themselves including marginal times. The highest coefficient (4) is assigned to the unfavorable time (units) coefficient (3) to the marginal zones for 6 to 7 a.m. and 10 to 11 p.m., coefficient

(2) to other marginal categories, while only optimal zones, namely 8 to 9 in the morning and 6 to 8 in the evening, are not

"penalized" since coefficient (1) is applied to them. Thus with the actual duration of the trip and its duration weighted

according to the time zones covered, the trip's weighting factor is calculated. In other words, a value coefficient for the trip itself is worked out by comparing the actual duration of the

Model 7 Determination of Market Share Incorporating Scharlach's Plan K

Hd~5

K where= k t

Market Share for airline in the city pair

K

Time value coefficient for airline kin the city

J pair , , summed overn the number of flights

TVC

=weighted duration/scheduled flight time

Z

VC),=

Sum of total time value coefficient for all

K

A

"J

competitors in the city pair

Example 1

Market ij is Los Angeles, California to San Francisco, California. Competitors are TWA, United, Western

Distance is 347 miles

Service is Jet and non-stop

KO' Carrier TWA United Western 32.04 76.70 Calculated 49. ' &Z,) Actual Q{{9 _Q~b # of Flights 12 26 27.67 12 .

i3<4

9.0 23.6 56.2 20.4 56.C 33.0

Example 2

Market ij is Los Angeles, California, to Phoenix, Arizona Competitors are American, Continental, TWA and Western Distance is 356 miles

Service - Jet and non-stop

Non-jet and multistop service is considered in the next model

Number of Flights Carrier Calculated CM -S)is;4 Actua

LNI4 6

American 3.0 Continental 11.75 TWA 10.44 Western 6.00 9.6 37.6 12.C 20.C 22.0 4C. C 33.6 19.25

One calculated percentage market share deviates videly from the actual market share especially for Western Airlines. The reason for this is that although this carrier only operates two non-stop jet flights, it also operates four single-stop turbo-prop and propeller type aircraft flights. Model 8 will take this into aconutht.

The only major critiaism on this method is that its use is limited to short-haul market where the stage lengths

are short and the passengers are generally day-trip passengers. Even in the short-haul market the weighing factors do not apply in every case. On Sacramento - San Francisco route for example, the type of business a traveler might be connected with is

legislation. If this is the case then these passengers would w

K

want to reach San Francisco between 10 and 10:30 a.m. Another case, where the above weighting factors may not apply is the case of passengers who check out of hotels and take a flight. Normally the checking out time from a hotel is noon. In this case these passengers would consider a flight in the early afternoon more important than say one at 5 p.m.

It is extremely doubtful whether the same weighting would apply for example on the transcontinental flights. Just

to name one reason, would be to point out the effects of time zone on transcontinental flights.

Model 8 Market Share as a Function of Type of Service, Equipment Frequency and Flight According To Time of the Day

The previous model is fairly restrictive since it does not take account of the type of service for example non-stop or multistop and type of equipment, jet, turbo-jet or propeller. Model 8 is an attempt to take this into consideration.

The results of one of the Civil Aeronautic Board's study (Reference 4) indicated that there is a distinctive

and definite relationship between the quality of service offered by a local service carrier and its traffic participation in a market

competitive with trunkline carriers.

Model 8 is an extension of Model 7. The time value coefficient (TVC) is further adjusted to take account of the major factors which most profoundly affect the share of the

traffic that a particular carrier would be likely to attract. We will call the adjusted value of the time value coefficient,

the quality of service index (QSI). The quality of service index was constructed by multiplying time value coefficients by values assigned to each of the major factors affecting

market share. The factors considered here are frequency, stops and equipment type. After experimentation, the following values were decided upon by the CAB report, because they produced results consistent with observed public response to service quality

changes. For example: non-stop service attracts more traffic than multistop, and jet flight more than piston type aircraft flight.

The following values were decided upon for use in computing the quality of service index.

Service Weighting Factor

Non-stop One-stop Two-stop Three-stop

Four, or more stops

Frequency

All one-way flights

Operating 5, or more days per week Type of Aircraft Prop Turbo-prop Jet

;QY

1i

'K) -o) J

11

(_SI

- = Quality service index for airline k for the city pair ij

C)

= Time value coefficient (defined previously) for the nth flight of the day for airline k I. A in the city pair ij

SFw -

_{L = Service weighting factor for the nth flight of the}

Kt day for airline k in the city pair ij

Type of aircraft weighting factor for the nth

3j

flight of the day for airline k in the city pair ij

21_S

i

KK

IJ

- -

(Cp 1)

Taking Example 2 of Model 7, the market share was computed again using the quality of service index.

Model 7

Calculated Actu 1 Calculated

Carrier _{L M . I b)- /} 45 (/ American 5.87 x 56 14.3 12.0 9.6 Continental 11.75 x 56 28.6 20.0 37.6 TWA 10.44 x 56 25.5 22.0 33.6 Western 12.96 x 56 31.6 40.0 19.25

It can be seen from the table of results that quality of service index of Model 8 predicts results to greater accuracy than time value coefficient of Model 7.

It should be pointed out that both of these models

incorporate weighting factors. Although it is true that weighting factors would produce results which are more accurate than can otherwise be obtained, the value of the weighting factors decided upon in the models is arbitrary.

-31-Model 9. Determination of Market Share using Frequency Share and the Airline Image.

The models developed so for have explained the variance

in market share for most of the markets. However certain markets have peculiarities which cannot be explained by the models developed

so far. An example of this is Los Angeles, Phoenix market. LAX.- PHX. Market. # of nonstop Carrier. flights. M.S.(%). AA 2 12 TWA 4 22 CO 4 20 WA 2 40 BL 0 6

Using the equations from our previous models , the estimated percentage market share comes out to be very much different from the actual market share. Western Airlines are getting a very high percentage of the market share. Airline Image or Terminal Activity was the variable investigated.

Which airline a passenger will choose to travel by, will to an extend depend on the image of that airline in the passenger',s mind. One approximate way of determing the airline image is to

determine the percent scheduled aircraft departures that a particular airline performs of the total departures at the given terminal.If for example a total of one hundred scheduled aircraft departures were performed at terminal X and United Airlines accounted for ten

of these departures, then the value given to the variable Airline Image or Terminal Activity for United Airlines at X is 10 %.

The following example will illustrate the importance of of Terminal Activity as an explanatory variable.

DETROIT - NEW YORK Market. Actual M.S. (%) 29 F.S.

_(%)

48 38 14

There is very little difference in the quality of service offered by the three carriers. All three operate jet aircraft, nonstop flights with very nearly the same flight time. One variable that accounts for the variance in the market share is the variance in the frequency share. However as seen in the table frequency share' alonecdoes not explain the percentage market share. The missing variable was found to be terminal activity.

where d = Market Share

S=

Frequency Share

(A

= Terminal Activity

i and

j

being the terminals Using this we get

Actual %

"-S

65 29 5 Estimated

% M

64.5 25.4 10.6

Having identified terminal activity as a variable, it was still difficult to explain the distribution of market share in the Los Angeles - Phoenix market. On close inspection of the flight schedules, it was found that our estimating equation was giving

Carrier AA NW UA Carrier AA NW UA

-33-Bonanza Airlines far greater market share than what -33-Bonanza was actually getting. The reason Bonanza was getting a low

percentage of market share was due to the fact, that in this' market all their flights were nonait and multistop. Fifty percent of their flights were as many as four stop flights. So they were in fact competing with jet non-stop flights of their competitors. Western on the other hand, although had higher percentage of frequency share, was operating two thirds of their flights with one-stop.

It is a known fact that multistop flights are less attractive to passengers than non-stop flights. The greater the number of stops, the less attractive the flight-becomes

compared to a non-stop flight. An attempt was made to investigate the relationship between a non-stop flight and multistop

flight from the point of view of attractiveness to a passenger who has a choice of taking a non-stop flight offered by one

competitor and a multistop flight offered by another competitor. After testing many forms of relationships, such as

1) a multistop flight is equivalent to of a non-stop flight

2) _{the factor being /L*ti- )}

3) the factor being I

where n is the number of stops performed by a carrier in the market ij, -p.2 was found to be the best factor.

When we give a weighting of unity to a non-stop flight, then the weighting factors to be applied to multistop flights are

Multistop 0 1 2 3 4 Weighting Factor 1 1/4 1/9 1/16

So here is the explanatory variable for Bonanza Airlines

in the market Los Angeles - Phoenix. Each of its four stop flights is only worth 1/16 of a competitor's non-stop flights.

Using equation (23), the following estimates were obtained for the percentage market share in few of the markets, where our previous estimating equations failed to predict to the expected accunacy.

Market Carrier LAX - PHX AA TN Co WA BL Estimated 14.8 26.0 12.85 39.5 6.85 Actual % M 1.5 12.0 22.0 20.0 40.0 6.0

Market DET - NY NY - W.DC Carrier AA NW UA AA AL BN EA NA TW UA Estimated

%

M , -4)

64.5 25.4 10.6 19.5 0.62 0.64 65.0 8.5 2.8 3.0 Actual

%

Af..)

65.0 29.0 5.0 17.0 1.0 2.0 69.0 7.0 1.0 2.0

THE EFFECT OF BEHAVIORAL ELEMENTS

The percentage of market share that a carrier obtains

in a highly competitive market depends on many behavioral

elements of the system. It is very difficult if not

impossible to incorporate these behavioral elements into

the models for two reasons. First, it is impossible to

place satisfactorily numerical values to these variables.

Secondly, in some cases where means are available to quantify

the variables, we are faced with the problem of availability

of data.

A little thought would, for example, suggest that the

market share would depend on marketing effectiveness of the

carrier. Management has the responsibility of deciding on

the marketing mix for each customer type (businessman or

vacationer) for each product (first class seat or economy

class seat) in each territory (various routes). Suppose

the management sets a price of P (not completely under

management control in the case of airlines) an advertising

budget of A, and a distribution budget of D for product

i selling to customer type j in area k at time t. This can

be represented as: (P,A,D)ij,k,t Market sales refer to the

behavior of sales in response to alternative levels,

allocations, and mixes of the marketing effort. In order to

quantify marketing effectiveness, we would need data on

advertising and distribution budget for each route and

how many dollars were spent on advertising on each route.

Airlines tend to spend a fixed amount annually as a

system advertising expense and then small amounts in

various markets. Did a passenger go from i to j on TWA

because of his response to TWA's nation wide theme

"Up-up and away with TWA" or because of the publicity he

noticed at i.

Looking into the problem a little deeper suggests

that the effect of advertising on sales is not simply a

function of how much is spent. Even more important may be

how it is spent: Specifically, what is said, how it is

said, where it is said, and how often it is said. Two

carriers in the same market may budget the same amount for

advertising, offer essentially the same aircraft seat (non-stop

jet) and charge the same price (especially true in the

airline industry); yet they may enjoy quite different market

shares, owing in no small way to important differences in

their creative advertising strategies. Creative strategies

are thought of as unique, unquantifiable entities. Some

marketing analysts rationalize the omission of creative

factors in their study of advertising's effect with the

argument that all large advertising agencies are equally

creative and therefore differences in individual campaigns

tend to "wash out', but with airlines offering almost

exactly the same product at virtually the same price and

passenger service, it is precisely the differences in

individual campaigns which marketing management want to

a substantial part of the original movements of the market

shares remain "unexplained".

Passenger service is another area which is difficult

to incorporate in a model. Passenger service consists of

two parts, on flight and on ground. The on flight passenger

service consists of such facilities as meals, movies etc.

How is the market share effected by changing the service?

It is very difficult to measure this. NE went all steak.

Preferring steak dinner instead of fancy foreign-flavoured

meals is largely a psychological phenomenon unrelated to

quantative measurable variables. Also changes such as this

are usually throughgoing; airlines discontinue the old

service in introducing the new one.

The on ground passenger service such as the booking

of car-rentals on hotels, arranging for connecting flights

can also be important. Again these variables are

quantifiable and their effect on market share is

un-predictable. Looking at another case, airlines in general

spend a large amount on reservations in order to produce

better customer service. However Eastern Airlines, in the

market Boston-New York do not have a reservation system.

They are offering a shuttle service. So they may have

zero reservation expense in this market, but may suffer

additional expenses on standby aircrafts which is essential

POSSIBILITIES OF FURTHER WORK IN THIS AREA

In this study the main concentration has been on

explanatory variables which are quantifiable. The variable

which received little attention is connecting flights. It

might be of interest to investigate what effects "connecting

flights" would have on the determination of percentage

market share. Obtaining data on connecting flights would

be a very difficult and extremely time consuming task.

It is also suggested that an attempt should be made

of taking into account the effects of some behavior elements

such as passenger patriotism and effects of advertising and

passenger service.

One way of taking account of some of these behavioral

variables might be to segment each market into types of

passengers. As a start, the market might be segmented into

two groups, the businessman and others, which may include

BIBLIOGRAPHY

Reference 1. Civil Aeronautic Board. Competition Among

Domestic Air Carriers. Volume VII-5

Reference 2. Official Airline Guide. Quick Reference North American Edition, May 1, 1966.

Reference 3. "The Scharlach Plan". Aeroplane.The International Air Transport Journal.

February 14,

1968

Reference 4. Civil Aeronautic Board. Report Docket 12285

Reference 5. Sales Management-"The Magazine of Marketing-Survey of Buying Power" June 10, 1966

APPENDIX A - GRAPHICAL ANALYSIS

At first graphical analysis was performed to see

the relationship between market share and market frequency, first holding the number of competitors constant at 3

and varying the stage length. Graphs Al to A4 show this *

data plotted.

Next the number of competitors in the market was varied for all stage lengths. Graphs A8 and A9 show the data plotted.

*The procedure was repeated using all competitors. A5

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-51-APPENDIX - B. AIRLINE COMPETITION DATA.

Column. E.- A local passenger travels on a single carrier and his entire domestic air journey is between two cities of the city pair. Other passengers are designated as

"connecting" passengers. This data is included so that market share can be calculated on the basis of local passengers only if desired.

Column H-I.-Regularly scheduled weekday flights from first city to second city in pair as May 15,1966. Wednesday was arbitarV chosen as the key day for flights which operated on fewer than all five days. "Shuttle" flights were counted only once. It would be better to weight these flights according to the average number of sections flown. May is a good average month, but it would be better to average data from each month's flight schedules. Flight frequencies in opposite direction

for city pairs are usually quite comparable, but not identical; It would be better to average data from each direction.

Column

J-Column

A-Non-direct flights are sometimes also important. Their influence seems to greatly diminish as the number of stops increases. It is suggested that frequency shares be

recalculated weighing non-stop flights by a factor of

The number in parenthesis represents the inter-city distance in miles.

CITY PAIR CARRIER FLIGHT TYPE OF LOCAL PAX MARKET NO. OF NO. OF NO. OF FREQUENCY

TIME A/C AS % OF SHARE COMPETITORS NON-STOP ONE-STOP SHARE

%

(min) TOTAL PAX FLIGHTS FLIGHTS (N. STOP

--- FLIGHTS) 1. Boston AA 70 P 85 4 5 5 2 7 Mass. EA 48 J 96 68 38 5 53 NewYork NA 34

J

65 3 5 2 7 N.Y. NE 63 P 89 21 19 1 26 (188) --- 5 --- _84--- ---- --- 1--- ---2. New York AA 67 P 82 17 6 16 2 23 N.Y. nY. BN 57 J 76 2 5 7 Washingt on D.C. EA 68 P 92 69 34 8 48 (205) NA 70 P 71 7 8 7 11 TW 45 J 83 1 5 7 1.147 J 72 2 2 3

---

---

--- ---

---

---

---3. Los Angeles TW 61 J 80 9 3 13 25 California UA 61 J 88 56 261 51 n San Ffannisco California WA 58 J 84L 33 12 24 ~--(347)~--- --- --- --- ~---4. Chicago AA 105 J 81 47 4 18 4 35 Illinois NW 112 J 72 2 2 6 4 New York N.Y. TW 103 J 80 19 14 6 37 (711) UA 105 J 85 31 17 12 33 5. Miami EA 205 J 96 44 3 16 53 Florida NA 200 J 97 33 9 1 New York 30 N.Y. NE 205 J 98 22 5 2 17 --- --- ---

---6. Los Angeles Cal. New York, N (2446) 7. Los Vegas Nev. Los Angeles Cal. (228) AA 295 TW 295 UA 285 BL 44 TW 49 UA 48 WA 4.6 J 92

59

9 39 8. Detroit Mich.

New York, N.Y.

AA NW UA 83 J

83

J

79 J 9. Chicago Ill. i Los Angeles n Cal. (1742) 10. Chicago Ill. Minneapolis Minn. 11. Chicago Ill. Detroit Mich. AA 209 CO 205 TW ₂₀₅ UA 215 J EA

NW

UA 60

65

69

-80 28 8 4 32

---

---AA NW UA

6o

p

54

J

52 J 12. Cleveland AA 68 J 89 4o 3 7 32 Ohio TW 68 J 85 9 3 14 New York

69

3

55 N.Y. UA 74 J 93 49 12

55

'---90

89

92 91

86

91 45 32 22 24 7

38

33

39

26 13 22 91

86

8o

65

29

5

29 26 71 72

38

52 61 71 24 20

66

21 57

67

77

9

12 62 21 17 - - - -- - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

-13. New York AA 110 P 88 1 3 1 6 N.Y. _{TW 69 J 91 56 8} 2 47 Pittsburgh P4 UA

74

J

92

34

8

2

47

---

(A---14. New York AA 335 J 89 34 3 5 4 31 n * TW 340 J 90 35 5 2 31 San Francisco Cal. UA 340 J 92 29 6 1 37 15. Boston, AA

98

P

83

30

4

4

1 19 Maas. _EA

_L69

_J

₈₅

₂₄

₅

_{1 24} Washington D.C. NA 65 J

76

2

5

, (393) NE 120 _P 88 43 10 48 16. Chicago AA 240 J 62 36 3 7 32

4

Ill' TW 247 J 60 24 6 1 27 ,t San Francisco in Cal. UA 250 70

39

9 3 41 17. Chicago AA 85 P 70 50 2 10 50 Ill. _{44 10} 50 St. Louis DL

66

Mo.

(261)

18. San Francisco UA 98 J 87 60 2 7 4 64 Cal. _{WA 96 J 84 38 4 2 36} Seattle Wash. 19. Chicago DL 148 J 84 50 3 3 2 30 Ill. EA 151 _{J 82 20 3 30}

Miami

Fla. NW 151 J 77 28 4 3 4o (1190)

.hicago AA 99 j 63 66 3 8 4o I11. TW 91 J 40 1 3 1 15 Washington D.C. UA 92 J 73 30 9 45

---21. Chicago NW 59

J

59 39 2 8 47

Ill

1.

UA

59

J

69

57

₉

₃

₅₃

Cleveland mhio --- ~---22. Buffalo AA 65

J

96 79 2 11 1 92

N.Y.

UA

54

J

93

11

1

3

8

New York

N.Y.

---

~---

---23. Chicago BN 77 J 50 36 3 8 42 1 Ill. CO 65

J

73 10 _{2 11} Kansas City I Mo. TW 71 J 56 53 9 47

... 4

---

---24. Los Angeles AA 37 J 50 13 6 3 13 Cal. _DL

₃₄

_{J 41 4} San Diego Cal. NA 30 J 36 5 1 4 (111) PC 37 P 17 1 1 4 UA 30 J 58 52 12 52 WA 32 J 57 22 5 22 --- --- ---25. Los Angeles UA 126 J 82 58 2 4 4 57 Cale _WA 130 _{J 82 42 3 1 43}

Seattle

Wash. -

(960) -- - -

- ~- - -

-- - - -- -

-Mass. TW 137 J 65 15 2 3 20 Chicago

₄

2 Ill. UA 141 J 70 31 --- ---27. Chicago TW 95 J 69 -48 2 6 1 50 Ill UA 99 J 78 48 _{6 2 50} Philadelphia Pa.

---

64

---28. Chicago Co 127 J 48 49 3 5 1 33 I. TW 130 J 32 8 2 13

Denver

Col. UA 134 J 48 42 8 2 53

2h---

~----

---29. Atlanta DL 104 J 75 52 3 6 2 35 1 N a. EA 100 J 66 33 8 1 47 LnNew York N.Y. UA 103 3 66 14 3 1 18 ---30. New York AA 48

3

94 53 2 9 50 N.Y. MO 41 _{J 83} 47 _{9 3 50} Syracuse N.Y. --- --- ---31. Chicago TW 70 3 55 57 2 5 6 42 Ill. 75 62 41 7 58 Pittsburgh Pa. ---32. Dallas BN 50 J 75 93 2 16 2 84

Texas

TT

68

P

76

7

3

1

16

Houston

Texas ---

~---33. Los Angeles AA 60 J 84 12 4 2 1 17 Cal. Co 60 J ₇₉ 20 _{4 33} Aona TW 61 J 82 22 4 33 Arizona

(356)

WA

60

J

83

4o

2

4

17

---34. Chicago. EA 28 P 25 2 4 1 3 Ill. NO 33 P 19 81 22 71 Milwaukee Wis. NW 28 J 22 11 4 13 (81) _{UA 33 P 28 6 4 13}

35. New York

AA

66

P

96

68

2

6

67

N.Y. N.Y. UA 57 J 93 14 3 33 Rochester N.Y. -2---36. New York AA 144 J 81 29 3 3 1 21 N.Y. _EA 125 J 86 9 3 21 St. Louis Mo. TW 137 J

84

62

8

2

57

(7}---

---37. Boston EA 60 J 91 32 2 4 36

Mass.

NE

87

J

95

64

7

64

Philadelphia Pa. '0---38. Dallas AA 234 J 72 59 2 4 4 57 Texas BN 283 J 74 35 3 2 43 New York N.Y. 39. Chicago AA 57 J 59 12 3 1 4 8 Cincinnati DL 76 P 52 80 10 77

Ohio

EA

55

3

50

7

2 15 (253)

40. Portland UA 83 J 80 66 2 8 62 Ore' WA 80 J 77 31 ₅ 38 San Francisco

Cal

(535) 41. Chicago AA 120 J 49 63 2 5 3 56 Dall BN 127 J 56 36 4 1 44 Tex. --- ---42. Albany AA 60 P 56 3 2 1

6

N.Y.

MO

60

P

54

97

16

94

New York N.Y. --- ----

---43.

Chicago

AA

55

P

64

48

3

5

45

c 11. DL 52 P 60 14 2 18

co

Indianapolis

Ind.

EA

43

J 54 30

4

36

-l- ---

---

---44. Philadelphia AA

33

J

46

5

3

4

9

Pa. DL 32 J 69 2

5

11

Washington

51

D.C. NA 30 P 53 11 5 1 11 (122) 45. Philadelphia AL 80 P 86 59 2 10 2 56

Pa.

TW

6o

J

89

41

8

44

Pittsburgh

Pa. ---~---~--- ~---~--- ---46. New York AA 45 P 77 57 4 8 47 N.Y. N.Y. e AL 62 P 71 19

5

29

Providence

52

R.I.

EA

44

P

65

17

3

18

(153) NA 40 P 67 6 1 6 --- ---