Massachusetts Institute of Technology
Flight Transportation Laboratory
September 1968
AIRLINE COMPETITION
FTL Report R-68-2
N.K. Taneja
FLIGHT TRANSPORTATION LABORATORY
REPORT R68-2
AIRLINE COMPETITION
BY: N.K. TANEJA
TABLE OF CONTENTS
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
B - Airline Competition Data
41
51
-2-
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
on business compared with one on vacation.
-3-
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.
of the relationship was not yet clear.
to determine this relationship.
However the form
Models 3 to 6 are an attempt
-4-
Models 7 and 8 investigate the effects of times of flight
through the day and the quality of service offered.
-5THE 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
had a low percentage of local passengers, i.e. passengers boarding
-6-
at city i destined for
j
merely passing through i.
in contrast to those passengers who were
For example on flight from Chicago to
Milwaukee, the percentage of local passengers was very low in some
cases.
% of Local Passengers
Airline
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.
aircraft is a Boeing 707.
Usually the
Almost invariably this flight originates
from California for example Los Angeles, in which case most of the
passengers on board are not local.
flight at New York.
Only few passengers board this
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
operated New York - Boston only.
-7-
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 + b1 x 1 + b 2x2 + . . . . . . . . + bnxn
where Y is the dependent vatiable and xi, x 2
independent variables.
determined.
Coefficients b 0 , b,
.
.
.
. .
.
* xn are the
. bn are to be
The multiple regression technique manipulates the
statistical data and produces the appropriate relationship between
-8-
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.
by adding one variable at a time.
These equations are obtained
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
form to be used on the M.I.T. IBM 360 computes a sequence of multiple
-9-
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.
highest F -
to -
The variable with the
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.
besides
Thus,
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
the amount of residual error between the observed points and the computed
-10-
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
regression.
-11MULTI-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
Ti
where
.
K
-
(trav).
-
tO
=
t.--
f(
total system (travel) time for airline k from city i to
=actual flight time for airline k from city i to
=
j
j
average wait time for a passenger arriving with poission
distribution with varing ;
, before he can board the
aircraft.
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.
_N K
-(.)
So with t .. and N. . known, we can determine T .. for airline k.
# of passengers from i to j on airline k.
k
P.. =
P.. = total # of passengers from i to j.
k 1]
1]
Let P.. =
and so
=
market share of passengers for airline k in the
city pair ij.
Our model assumes that
X
where K.. = constant
1]
Let
IwoK
/.3T
and
K
=k..
1]
another constant
-12We have
(F7
/(C)
,') 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
where
+ a log(Tm)
0
min
1
+ a log(Tk
2
6, = w
From regression analysis we obtain
-0.219
M.S.= 895 T .
min
T
-2.56
r
Multiple R = 0.6572
Std. Err. of Est. = 0.8498
F-Ratio = 55.471
Eastern Airlines.
Example.
T
min
k
t..
IJ
Boston
-
New York Market.
= 34 minutes.
= 48 minutes.
N
= 38 non-stop flights daily.
T
= t..
+ 18
M.S. = 895x34
_
2.25
-0.219
x2.25
-2.56
#E
58 %
r
-13Model 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.
(Tmin
. )
(FREQ)
Frequency Share
(%)
(Nc
Number of Competitors.
M.S.
= K
(NSTOP)
log(M.S) = C + a
or
+
(lSTOP)
(Tmin
.i)
(FREQ)
-
N
-
c
log(NSTOP)
log (lSTOP)
+
log (T . )
min
+ 6 log (FREQ)
+
log(Nc
Regression Equation.
-0.1536
0.162
1.46 0.288
NSTOP
.
FREQ
NC
min
M.S. = 0.156 T
- .
-
.
(7
Multiple R = C.9173
Std. Err. of Est. = 0.4519
F-Ratio = 191.170.
Example. Eastern Airlines
Boston- New York Market.
T . = 34 minutes
min
NSTOP = 38 minutes
FREQ = 53
N
=5
c
On substitution of these values into the regression equation we
obtain,
M.S. = 81 %.
From the step-wise regression analysis , it was discovered
-14-
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.
-15-
Model 3.
Market Share vs Frequency Share.
The Cubic Model.
Let X = Percentage of Frequency Share
Y
"
=
"
Market
then
Y = A X3
+ B X2
D--
+ C X +
-
-(-
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.
-16Model 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 X
2
+ 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
X 4-
C.
.f
_
-17Regression 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.
-18-
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
further screening.
-19-
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 X4 - 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
market share to be 80 %.
k~
A
-20-
~~m-r---t---
-n---
-~
-
I
.1
+
El
4i
r
_
1
1
4
~1
I
_
4
41
1
__
~
I
I
I-
-
74
-k
---7
---
-~
C---
~--i;,---~
-21-
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.
to mind at once was:
The question which came
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
He would be more interested in taking flights
on flight.
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
imperatives.
-22-
WEIGHTING OF TIIES OF THE DAY
AS FACTOR IN TRANSPORT DEMAND FOR DAY RZTURN TRIPS
(from Monday to Friday)
24
hours
6 7 8 9 10
0
17 18
20
Non-abtive
zone
Working time
zone
Non-active
zone
22 23
(B)
(A)
(B)
0.4
-44
H4
0
H
Unit
Junit
00
44
0--0
b N
NO
*
N
N
FI
r-
0
N
0
0
N
N
c
c
N
ci
(Zcci
6
0
0
c
z
7
M
2 11~
ouo
Number of
Weighted
31
3 N value of
-23-
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.
-24-
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.
(4)
is assigned to the unfavorable time
The highest coefficient
(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
trip and its
weighted value.
-25-
Model 7
Determination of Market Share Incorporating Scharlach's
Plan
K
Hd~5
K
where=
Market Share for airline
K
in the city pair
k t
Time value coefficient for airline kin the city
pair
J
TVC
Z
K A
summed overn the number of flights
,
,
duration/scheduled flight time
=weighted
VC),= Sum of total time value coefficient for all
"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'
Calculated
Carrier
# of Flights
49.
'
&Z,)
Actual
Q{{9
_Q~b
TWA
32.04
12
23.6
United
76.70
26
56.2
56.C
Western
27.67
12
20.4
33.0
.
i3<4
9.0
-26-
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
w
Carrier
American
K
Number of
Flights
Calculated
Actua
LNI4 6
CM -S)is;4
3.0
9.6
12.C
Continental
11.75
37.6
20.C
TWA
10.44
33.6
22.0
19.25
4C. C
Western
6.00
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
-27-
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.
-28-
Market Share as a Function of Type of Service, Equipment
Frequency and Flight According To Time of the Day
Model 8
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.
coefficient
(TVC)
The time value
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.
-29-
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
11J (At C) kW 14
F
'K)
(_SI
-
C)
-o)
=
Quality service index for airline k for the city
pair ij
=
(defined previously)
Time value coefficient
for the nth flight of the day for airline k
in the city pair ij
I. A
SFw L-
=
Kt
3j
IJ
Service weighting factor for the nth flight of the
day for airline k in the city pair ij
Type of aircraft weighting factor for the nth
flight of the day for airline k in the city
pair ij
21_S
KK
i
- -
(Cp 1)
-30-
Taking Example 2
of Model 7, the market share was computed
again using the quality of service index.
Model 7
Calculated
Carrier
American
L
M
. I
Actu 1
b)-
/
Calculated
(/
45
5.87 x 56
14.3
12.0
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
9.6
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.
El
-
-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.
Carrier.
LAX.- PHX. Market.
# of nonstop
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.
-32DETROIT - NEW YORK
Market.
Actual M.S. (%)
Carrier
AA
F.S. (%)
48
NW
29
UA
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.
d
where
=
S=
(A
=
Market Share
Frequency Share
Terminal Activity
i and
j
being the terminals
Using this we get
Actual %
Estimated
"-S
%M
AA
65
64.5
NW
29
25.4
UA
5
10.6
Carrier
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
"
-33-
Bonanza Airlines far greater market share than what 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
-34-
Multistop
Weighting Factor
0
1
1
2
1/4
3
1/9
4
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.
Estimated
Market
LAX -
Carrier
PHX
AA
TN
Co
WA
BL
Actual
%
14.8
26.0
12.85
39.5
6.85
M 1.5
12.0
22.0
20.0
40.0
6.0
-35-
Estimated
Carrier
Market
DET
NY
-
-
% M , -4)
Actual
% Af..)
NY
AA
NW
UA
64.5
25.4
10.6
65.0
29.0
5.0
W.DC
AA
AL
BN
EA
NA
TW
UA
19.5
0.62
0.64
65.0
8.5
2.8
3.0
17.0
1.0
2.0
69.0
7.0
1.0
2.0
-36THE 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
passengers by classification.
There is no way to find out
-37how 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 "Upup 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.
how it is spent:
Even more important may be
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
note and exploit.
The consequence of leaving out the creative factors is that
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 un-
quantifiable and their effect on market share is unpredictable.
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
for the shuttle service.
-39-
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
those vacationing and those traveling for personal reasons.
-40BIBLIOGRAPHY
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 MarketingSurvey of Buying Power" June 10, 1966
-41APPENDIX 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.
to A7 show the result of this.
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-51-
APPENDIX - B.
Column.
AIRLINE
COMPETITION
DATA.
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-
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
Column A-
The number in parenthesis represents the inter-city distance
in miles.
A
B
F
E
D
C
J
I
H
G
NO. OF
NO. OF
MARKET NO. OF
FLIGHT TYPE OF LOCAL PAX
ONE-STOP
NON-STOP
COMPETITORS
SHARE
AS % OF
A/C
TIME
FLIGHTS
FLIGHTS
PAX
TOTAL
(min)
-----------------------------------------------------------------------------------------------CITY PAIR
1. Boston
Mass.
NewYork
N.Y.
(188)
CARRIER
J
85
96
65
4
68
3
P
89
21
AA
EA
NA
70
48
34
P
J
NE
63
5
-----------------------------------------P
67
AA
2. New York
N.Y.
J
57
BN
nY.
Washingt on
D.C.
(205)
_84------------17
82
2
76
2
5
2
7
53
7
19
1
26
---- -----16
6
1---------- ------2
23
7
5
68
P
92
69
34
8
48
NA
70
P
71
7
8
7
11
TW
45
J
83
1
5
J
2
72 ------ -----
9
80
J
61
3. Los Angeles TW
California
UA
61
J
88
56
n San Ffannisco
33
84L
J
58
WA
California
--------------~--(347)~---------------------------- ------47
81
J
105
AA
4. Chicago
2
72
J
112
NW
Illinois
New York
19
80
J
103
TW
N.Y.
(711)
UA
105
J
85
31
Florida
New York
N.Y.
5
38
5
EA
-------- 1.147
----------------------------------
5. Miami
5
FREQUENCY
SHARE %
(N. STOP
FLIGHTS)
EA
205
J
96
44
NA
200
J
97
33
NE
205
J
98
22
-------------------------------------------------
-----3
2
-------------
-----
7
3
25
51
24
12
----------- ~----------------------------13
261
4
18
2
4
6
35
4
14
17
6
12
37
53
16
3
9
---------
33
1
5
2
------------
30
17
-----------------
B
A
6.
Los Angeles
Cal.
New York, N
(2446)
7. Los Vegas
Nev.
Los Angeles
Cal.
(228)
295
295
90
45
89
285
92
32
22
38
33
39
BL
TW
44
91
24
26
49
86
7
UA
WA
48
4.6
91
13
22
J
92
59
83
J
91
65
83
J
29
79
J
86
8o
AA
TW
UA
8. Detroit
AA
Mich.
NW
New York, N.Y.
UA
-- - - - - - --
9.
i
n
Chicago
Ill.
Los Angeles
Cal.
(1742)
10. Chicago
Ill.
Minneapolis
Minn.
C
- - - - - - - - - - - - - -- - - - - - -
AA
209
CO
TW
UA
205
EA
60
NW
65
69
UA
205
215
71
72
J
9
38
5
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
29
24
26
20
8
28
-80
39
52
61
66
71
21
4
--------------------------------------------
32
---
11. Chicago
Ill.
Detroit
Mich.
AA
NW
UA
6o
54
p
J
57
9
62
21
52
J
67
12
17
12. Cleveland
AA
68
J
89
4o
TW
68
J
85
9
UA
74
J
93
49
Ohio
New York
N.Y.
69
-
77
7
3
32
3
14
12
55
3
'--------------------------------------------------------------------------------------------
55
-
-
-
-
-
0
D
AA
TW
110
69
P
J
88
91
1
56
UA
74
J
92
34
14. New York
AA
*
TW
San n
Francisco
Cal.
UA
335
340
340
J
J
J
89
90
92
34
35
29
15. Boston,
Maas.
Washington
D.C.
,
(393)
AA
EA
98
L69
P
J
83
85
30
24
NA
NE
65
120
J
P
76
2
88
43
16. Chicago
AA
240
J
62
36
TW
UA
247
250
J
60
70
24
AA
85
P
70
2
66
50
44
10
10
13. New York
N.Y.
Pittsburgh
P4
2
47
8
2
47
3
5
5
6
4
2
1
31
31
37
4
4
5
1
1
19
24
-------------------(A--------------------------------------------------------------
4
Ill'
Cal.
,t San Francisco
in
17. Chicago
Ill.
St. Louis
Mo. (261)
DL
6
1
8
3
5
3
10
48
7
32
6
9
39
1
3
27
41
50
50
18. San Francisco UA
Cal.
WA
Seattle
Wash.
98
96
J
J
87
84
60
38
2
7
4
4
2
64
36
DL
EA
148
151
J
J
84
82
50
20
3
3
3
2
30
30
NW
151
J
77
28
4
3
4o
19. Chicago
Ill.
Miami
Fla.
(1190)
A
E
.hicago
I11.
Washington
D.C.
AA
TW
99
91
j
J
63
40
66
1
UA
92
J
73
30
0
H
3
8
3
I
3
1
4o
15
45
9
-------------------------------------------------------------------------------------------47
8
2
39
59
J
59
NW
21. Chicago
Ill
1.
UA
59
J
69
57
9
3
53
Cleveland
mhio
----------------------------------------------------------------------~----------------------
22. Buffalo
N.Y.
New York
AA
65
UA
54
J
J
96
79
93
11
2
11
1
92
1
3
8
N.Y.
~------------------------------------- ------------------------------------------------------23. Chicago
1
Ill.
I
Kansas
Mo.
4
...
City
36
10
J
56
53
77
65
J
TW
71
3
42
11
47
8
2
9
--------------------------------------------------------------------------------------------
24. Los Angeles
Cal.
San Diego
Cal.
(111)
Los Angeles
Cale
Seattle
3
13
5
1
52
1
1
12
4
4
52
22
5
22
34
J
J
50
41
13
4
NA
PC
UA
30
37
30
J
P
J
36
17
58
WA
32
57
AA
DL
37
J
UA
WA
126
130
6
----------------------------------------------------------------
---
-------------
25.
J
50
73
BN
CO
J
J
82
82
58
42
2
4
3
4
1
57
43
Wash.
- (960) -- - - - ~- - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- -
- -
40
4
3
54
69
J
133
AA
26. Boston
Mass.
TW
137
J
65
15
2
3
20
Chicago
4
2
31
70
J
141
UA
Ill.
----------------------------------------------------------------------------- -------------------------50
1
6
2
-48
69
J
95
TW
27. Chicago
Ill
UA
99
J
78
48
6
2
50
Philadelphia
Pa.
--64
28. Chicago
I.
Denver
Col.
--------------------------------------------------------------------------------------Co
TW
127
130
J
J
48
32
49
8
UA
134
J
48
42
2h--------------
3
5
2
1
8
2
33
13
53
~------------------------------------------------------------- -------------------
35
2
6
3
52
75
J
104
DL
29. Atlanta
47
1
8
33
66
J
100
EA
a.
1 N
LnNew York
18
1
3
14
66
3
103
UA
N.Y.
-----------------------------------------------------------------------------------------------------50
9
2
53
94
3
48
AA
30. New York
N.Y.
MO
41
J
83
47
9
3
50
Syracuse
N.Y.
------------------ ---------------------------------- -------------------------------------------42
6
5
2
57
55
3
70
TW
31. Chicago
Ill.
75
62
41
7
58
Pittsburgh
Pa.
---------------------------------------------------------------------------------------------------84
2
16
2
93
75
J
50
BN
32. Dallas
Texas
Houston
TT
68
P
76
7
3
1
Texas
---------------------------------------------------------------
~---------------------
16
H
I
J
1
22
2
4
4
17
33
33
83
4o
2
4
17
96
68
B
C
D
E
AA
Co
60
60
J
J
84
79
12
20
Aona
Arizona
TW
61
J
82
(356)
WA
60
J
AA
66
P
A
33. Los Angeles
Cal.
4
-----------------------------------------------------------------------------------------------------3
1
4
2
25
P
28
EA
34. Chicago.
71
22
81
33
P
19
Ill.
NO
Milwaukee
13
4
11
22
J
28
NW
Wis.
(81)
UA
33
P
28
6
4
13
35. New York
2
67
6
N.Y.
N.Y.
UA
57
J
93
14
3
33
Rochester
N.Y.
-2----------------------------------------------------------------------------------------------21
1
3
3
29
81
J
144
AA
36. New York
N.Y.
EA
125
J
86
9
3
21
St. Louis
2
57
8
62
84
TW
137
J
Mo.
(7}------------------------------------------------------2
-------------------------4
36
7
64
60
J
91
32
NE
87
J
95
64
38. Dallas
Texas
New York
N.Y.
AA
BN
234
283
J
J
72
74
59
35
2
4
3
4
2
57
43
39. Chicago
AA
57
J
59
12
3
1
4
8
DL
EA
76
P
80
55
3
52
50
37. Boston
Mass.
EA
Philadelphia
Pa.
'0----------------------------------------------------------------------------------------------
Cincinnati
Ohio
(253)
7
10
2
77
15
.
0
B
A
UA
40. Portland
Ore'
WA
San Francisco
1
66
31
2
J
80
77
8
5
J
49
63
2
5
4
83
80
J
120
I
62
38
Cal
(535)
41. Chicago
AA
3
1
56
44
36
56
J
127
BN
Dall
Tex.
------------------------------------------------------------------- -------------------------6
1
2
3
56
P
60
AA
42. Albany
N.Y.
MO
60
P
54
97
16
94
New York
N.Y.
------------------------------------------ --------------------------- -----------------------
43. Chicago
co
c11.
Indianapolis
Ind.
AA
55
P
64
48
DL
EA
52
P
J
60
54
14
30
Pa.
Washington
D.C.
(122)
45. Philadelphia
Pa.
Pittsburgh
5
45
2
18
4
36
-----------------------------------------------------
----------------------
-l- ----44. Philadelphia
43
3
AA
33
J
46
5
DL
32
J
69
2
NA
30
P
53
11
AL
80
P
86
59
TW
6o
J
89
41
3
2
9
4
551
5
1
11
11
10
2
56
8
44
Pa.
----------~--------~-------- ~----------------------~------------------------------- ------------------47
8
4
57
77
P
45
AA
46. New York
N.Y.
29
5
71
19
e
AL
62
P
N.Y.
52
Providence
R.I.
EA
44
P
65
17
3
18
6
1-----------------------6
67
P
40 ------------------------------------------NA
(153)
-----------------------
A
B
47. Denver
Col.
Los Angeles
Cal.
(830)
CO
UA
120
122
48. Los Angeles
Cal.
Spraatito
Cal.
(361)
49. Dallas
Texas
Los Angeles
Cal.
UA
WA
52
75
AA
DL
226
225
50. Hartford
C onn.
New York
)
-N.Y.
1
(106)
AA
AL
EA
NE
TW
UA
60
62
82
J
P
33
91
50
50
86
66
J
59
42
P
55
P
66
57
63
51
P
31
48
J
66
35
J
64
38
5
26
23
1
23
2
b
8
5
1
4
3
1
44
56
22
30
19
4
15
11