Competitiveness Pricing Strategies of Low Cost Airlines
Lim Seng Poh.and Mohd Ghazali Bin Mohayidin
This study has documented the differences in price setting dynamics across low cost
airlines operating on one of the biggest regional market and six domestic routes. A
total
sample of 7883 fare quotes for nonstop travel from Kuala Lumpur to
Singapore and 6 domestic routes has been examined. This study revealed that low
cost airlines pricing strategies were influenced by various variables and the tendency
of price setting pattern was towards Barometric price leadership.
Field of Research – Marketing, Developing economies
1. Introduction
Price is the weapon of choice for many low cost airlines in the competition for market
shares, many marketers believe that the most powerful competition trend currently
used by shaping the marketing and business strategy is the pricing strategy because
it has a direct impact on a company‟s profitability. It is clear that low cost airlines
faced stiff competition among themselves. The competition has an important
implication for market share low cost airlines have to use effective pricing strategies
to increase profitability, boost brand power and fight off competitors.
Business is a game and every firm is vulnerable to attack by the competitors, for long
term sustainability airlines need to play the role effectively in this game. Pricing
Strategies have been assumed as a strategic financial control tool.
The phenomenal growth of low cost airlines has triggered the interest of people to
believe that they will become successful mainly due to their pricing strategy.
Nevertheless, in a turbulent business environment (rising investment risks, intense
competition among airlines and potential liability), there is a greater uncertainty and
challenges to the success of the airlines‟ existing pricing strategy in fulfilling
expectation of the customers. In the attempt to provide further insight into the link
between price setting behaviour of the low cost airlines , this research presents an
empirical research on the question of the competitive pricing strategies of low cost
airlines, the degrees of competitiveness and what are the factors that affect ticket
price setting.
Research Questions
1. Whether low cost airlines price setting strategies are influenced by
different variables?
2. Whether there is a significant relationship between ticket price of the low
cost airlines and their competitors?
2. Literature Review
Pricing is the only element of marketing mix that produces revenue for the firm
Lovelock (1996) Similarly, Shipley and Jobber (2001) had pointed that pricing can be
a powerful tool in every business. They also pointed that “price management is a
critical element in marketing and competitive strategy and a key determinant of
performance.” Besides the operation effectiveness and outstanding efficiency the
most important determinant of survival of a company is its pricing strategy. Bilotkach
(2007) examined price setting strategy among low cost airlines concluded that low
cost airlines had implemented dynamic pricing strategy; the price of the ticket was
fluctuated according to the demand curve. (Poala et al 2007) examined low cost
airline business model by concluding that low cost airline implemented the
optimisation strategies by using dynamic pricing strategy ticket fares tend to change
based on the demand curve. Mason (2001) has pointed out that low cost airline has
promoted the concept that the cheapest fares it offers are the further away from the
date of departure and prices rise as the day of departure nears as available capacity
is taken up. This finding is also supported by the Bilotkach et al (2007) survey which
identified that the rate of increase in offered fares accelerates as the departure date
nears. Airlines implemented price discrimination to customers. Button ( 2007) In the
airline oligopoly business model, price leadership strategy is implemented by airlines
in situation in which a market leader sets the price of the service and the
competitors feel compelled to match that price.
Every firm is vulnerable to attack by the competitors. This finding gave ideas to the
airline companies to plan and implement offensive strategy that constitutes the best
defence against attack by the challenger. The result of the survey definitely gave the
airline companies some guidelines to lower the probability of attack, divert attacks to
less threatening avenues or lessen their intensity. This finding has provided valuable
information for the airline companies to measure, manage and improve the cash flow
and profitability of their customer service.
3. Research Methodology and Research Design
Data have been recorded for 60 days from 13 November 2009 until 11 January 2010
and 23 April 2010 to 21 Jun 2010, two sets of interrupted time series primary data
fare quotes have been obtained daily, for one way travel between Kuala Lumpur to
Singapore and 6 other domestic routes. A total of 3913 ticket fare quotes have
been recorded for the first observation and the second set data yielded 3970 fare
quotes. Both sets of data have been submitted to Dickey Fuller Test for stationary,
results of test implied that the data were significant.
Conceptual Framework
Flights
Flight destinations
Weekend/day
Time frames
Fare Quotes
Advance purchase
Ticket Price
The multiple regression analyses based on the below equation.
Equation:
P ats = αo + α1 I ats + α2I² ats + α3 F1 ats + α4 F3 ats + α 5 F4 ats
+ α7 FD1 ats + α 8 FD2 ats + α 9 FD3 ats + α 10 FD4ats + α 11FD5ats
+ α 11 FD 6 ats + α12 TP1 ats + α 13 TP2 ats + α 14TP3 ats
+ α 15weekend ats + α 16 FQ + α 17 AB 1+ α 18 AB2 + α 19 AB3
+ α 20 AB4 + α 21 AB5 +
α 22 AB6 + α 23 AB7 +α 24 AB8 + α 25 AB9
+ α 26 AB10 + α 26 AB11 + α 27 AB12 +
ats
where P indicates ticket price, a indicate airlines; t is the date on which the fare was
collected; s is the date of flight; I stands for interval (i.e., days to departure I = s - t);
F1 is the dummy variable for airline ( equal to one if airline is AirAsia and zero
otherwise), F3 is the dummy variable for airline ( equal to one if airline is Jetstar
Asia and zero otherwise) F4 is the dummy variable for airline ( equal to one if airline
is Tiger Airway and zero otherwise) F2 indicates Firefly as reference airline.
FD1 is the dummy variable for flight destination (equal to one if the flight destination
is KL- Singapore and zero otherwise) FD 2 is the dummy variable for flight destination
(equal to one if the flight destination is KL- Penang and zero otherwise) FD3 is the
dummy variable for flight destination (equal to one if the flight destination is KLLangkawi and zero otherwise) FD4 is the dummy variable for flight destination (equal
to one if the flight destination is KL- Kota Bahru and zero otherwise)FD5 is the
dummy variable for flight destination (equal to one if the flight destination is KL- Alor
Setar and zero otherwise) FD6 is the dummy variable for flight destination (equal to
one if the flight destination is KL- Kuala Terengganu and zero otherwise). FD7
indicates KL- Johor Baharu and set as reference flight destination.
TF refers to time frame of departing, TF1 is the dummy variable for time frame( equal
to one if the time frame of departure is 6.00a.m - 12.00p.m) TF2 is the dummy
variable for time frame( equal to one if the time frame of departure is 12.01p.m 6.00p.m),TF3 is the dummy variable for time frame( equal to one if the time frame of
departure is 6.01p.m - 12.00a.m).
AB refers to advance purchase of the ticket, AB1 is the dummy variable for advance
purchase( equal to one if the ticket purchase within 59-55 before the actual date of
departure) AB2 is the dummy variable for advance purchase ( equal to one if the
ticket purchase within 54-50 before the actual date of departure) AB 3 is the dummy
variable for advance purchase( equal to one if the ticket purchase within 49-45
before the actual date of departure) AB4 is the dummy variable for advance purchase
( equal to one if the ticket purchase within 44-40 before the actual date of departure)
AB5 is the dummy variable for advance purchase( equal to one if the ticket purchase
within 39-35 before the actual date of departure) AB 6 is the dummy variable for
advance purchase( equal to one if the ticket purchase within 34-30 before the actual
date of departure) AB7 is the dummy variable for advance purchase( equal to one if
the ticket booking within 29-25 before the actual date of departure) AB 8 is the
dummy variable for advance purchase( equal to one if the ticket purchase within 2420 before the actual date of departure) AB 9 is the dummy variable for advance
purchase( equal to one if the ticket booking within 19-15 before the actual date of
departure) AB10 is the dummy variable for advance booking( equal to one if the
ticket purchase within 14-10 before the actual date of departure) AB 11 is the dummy
variable for advance purchase( equal to one if the ticket purchase within 9- 5 before
the actual date of departure) AB 12 is the dummy variable for advance purchase(
equal to one if the ticket purchase within 4-0 before the actual date of departure)
=
error term, α and are constant in this study. The regression indicates that how a
unit change in the independent variables (flight, flight destinations, time frame,
weekends, fare quotes and advance booking days) affects the dependent variable
(Ticket Price) The error is incorporated in the equation to cater for other factors that
may influence Ticket Price.
A. Granger causality test
The lags term for these monthly data have been fixed 1 to 19 and if the probability
value greater than significance level P < 0.05 then reject the hypothesis otherwise
accept the hypothesis.
The approach to test for Granger causality is to regress the current time series Y
against the time series X to observe if jointly the coefficient associated with the x is
statistically significant. Essentially, a Granger causality test looks at the pattern of
variables over time to see if there is a pattern whereby one set of variable
consistently precedes another for example Firefly consistently changes its fare 2
days in advance of AirAsia on the given route then this suggests Granger causality.
E views 7 Microsoft package has been applied for Granger Causality test. The whole
scenario was based on the following equation.
PAAt = Price of Air Asia, PFFt = Price of Firefly, PJSt = Price of Jetstar Asia,
PTAt = Price of Tiger Airway,
“ PAAt “causes” PFFt or PFFt “ causes” PAAt”
4.
Discussion of Findings
A. Multiple regression analysis
Data of the low cost airlines have been subjected to regression analysis. The quoted
fare is the dependent variable in all the regressions, based on the results the
influencing variables have been identified.
Results
P ats = αo -1.627 I +3.489F1 ats - 45.940 F3 ats - 59.130 F 4 ats + 55.019FD1 ats
+33.694 FD2 ats + 69.898 FD3 ats +42.766FD4ats + 23.755 FD5ats + 21.302
FD 6 ats - 6.933 TP1 ats + 3.698 TP2 ats + 13.016 weekend ats - 3.423 FQ 16.176AB 1 ats +13.533AB2 ats +23.902AB3ats +21.656AB4ats – 10.239 AB6ats15.227 AB7ats -23.926 AB8ats -18.342 AB9ats +3.385 AB 10ats -12.367 AB11ats
+3.490 AB12ats+ ats
There were five sets of dummies variables, noted flights, flight destinations, time
frames weekday and advance purchase.
F2 (Firefly low cost airline) has been chosen as the reference flight, FD 7 ( Kuala
Lumpur – Johor Bahru) as the reference for flight destination, advance purchase 3935 days as reference for advance purchase and TF3 (Time frame 6.00pm – 12.00
a.m) as the reference for time frame. TF 4 ( Time frame 12.00 a.m – 6,00 a.m) has
been omitted as no flights offer the trip during this particular time frame.
Adjusted R square for this model is nearly 43%, P- value is 0.00 and significant at
95% confident level (p < 0.05%) the result implied that nearly all the variables are
significant.
Based on the multiple regression result, it has concluded that when increase 1 unit,
F1 ( ticket price of Air Asia) will increase by RM3.489, F3 (ticket price of Jetstar Asia.
Asia) will decrease by RM45.960, F4 (ticket price of Tiger airway) will decrease by
RM59.130.
Flight destinations, whenever increase 1 unit, ticket price of FD1 (KL – Singapore)
destination will increase by RM55.019, ticket price of FD 2 (KL – Penang) will
increase by RM33.694, ticket price of FD 3 (KL – Langkawi) will increase by
RM69.898, ticket price of FD 4 (KL – Kota Baru ) will increase by RM42.766, ticket
price of FD 5 (KL – Alor Setar) will increase by RM 23.755, ticket price of FD 6 (KL –
Kuala Terengganu) result revealed that when increase 1 unit ticket price of weekend
will increase by RM21.302.For time frame variable, when increase 1 unit ticket price
ticket price of time frame 1( 6.00am – 12.00pm) will deceases by RM6.933 and ticket
price of time frame 2 (12.01pm – 6.00pm) will increase by RM3.698. The equation
also implied that whenever increase by 1 unit. ticket price of weekend has expected
to increase by RM13.016. The relationship between fare quotes and date of
departure was reversed as the number of flights near to the date of departure has
been reduced or fully purchased.
Based on the multiple regression result, it has concluded that whenever increase 1
unit, F1 ( ticket price of Air Asia) will increase by RM3.489, F3 (ticket price of Jetstar
Asia) will decrease by RM45.940, F4 (ticket price of Tiger airway) will decrease by
RM59.130.
Flight destinations, whenever increase 1 unit, ticket price of FD1 (KL – Singapore)
destination will increase by RM55.019, ticket price of FD 2 (KL – Penang) will
increase by RM33.694, ticket price of FD 3 (KL – Langkawi) will increase by
RM69.898,, ticket price of FD 4 (KL – Kota Baru ) will increase by RM42.766, ticket
price of FD 5 (KL – Alor Setar) will increase by RM 23.755, ticket price of FD 6 (KL –
Kuala Terengganu) ) will increase by RM 21.302.
For time frame variable, whenever there is an increase 1 unit, ticket price of time
frame 1( 6.00am – 12.00pm) is expected will decrease by RM6.933 and ticket price
of time frame 2 (12.01pm – 6.00pm) will increase by RM3.698. The equation also
implied that whenever increase by 1 unit, ticket price of weekend has expected to
increase by RM13.016. The relationship between fare quotes and date of departure
is reversed as the number of flights near to the date of departure has been reduced
or fully purchased.
For advance days of purchasing ticket variables, whenever there is 1 unit increase
advance booking AB 1 (59-55 days) will increase by 6.764, AB2 (54-50 days) will
increase by 12.533, AB 3 (49 -45 days) will increase by 23.902, AB 4 (44 -40 days)
will increase by 21.656, AB6 (34 -30 days) will decrease by 10.239, AB 7 (29 -25
days) will decrease by 15.227. AB8 (24 -20 days) will decrease by 23.926, AB 9 (19 15 days) will decrease by 18.342, AB 10 (14 -10 days) will increase by 3.385, AB 11 (9
-5 days) will decrease by 12.367 and finally AB12 (4 -0 days) will increase by 3.490.
Generally, Air Asia low cost airline‟s ticket price was the most expensive, with the
coefficient 58.417 followed by Firefly low cost airline, Jetstar Asia and Tiger airway‟s
ticket price was the cheapest with the coefficient -3.847.As for domestic routes, flight
destinations Kuala Lumpur - Langkawi (FD3) was the most expensive with the
coefficient 70.471 as Langkawi was the place of interest, Air Asia offers 8 trips
perday to this particular destination moreover the distance between Kuala Lumpur
and Langkawi was the furthest.
This was followed by Kuala Lumpur – Singapore route (FD1) with the coefficient
55.019. The cheapest destination was Kuala Lumpur – Johor Baharu (FD7) the most
possibility reason is the distance between Kuala Lumpur and Johor Baharu is very
near less than 400 km. It was obvious that the weekend ticket prices were more
expensive with the coefficient 9.689 if compared with the weekday ticket prices.
Therefore, ticket price for travel on Saturday, Sunday and Monday appear higher as
compared to other days ( Tuesday, Wednesday, Thursday and Friday).
The fare quotas for all airlines are not consistent this happens due to the absence
of fare quotes for the airlines as the date gets closer to departure date. Therefore,
the relationship between fare quotes and interval was negative (co efficient – 3.423)
as near to the departure date, most of the tickets have been sold out.
B. Granger Causality
The granger causality test has concluded that in this oligopoly market structure, the
tendency of low cost airlines‟ ticket prices was more towards Barometric price
leadership the leader may not have a large market share but acted as a barometer
and there was a tendency of frequent switches in the leadership position. Ticket
price of Firefly low cost airline has given a significant impact to AirAsia low cost
airline in domestic routes, out of these six domestic routes four are significantly
granger cause AirAsia low cost airline‟s ticket prices. The ticket price trend for all
low cost airlines was downward at the 60 days far from the date of the departure was
a general graph picture, nevertheless some airlines did offer cheaper fare when near
to the date of departure. The result of multiple regression analysis has provided
evidence that ticket prices of airlines was indeed statistically influenced by various
variables. The pricing pattern was quite similar across all the airlines.
Table 1 The results of Granger Causality Test (13 November to 11 January
2010) and (23 April to 21 Jun 2010)
Null Hypothesis
Destinations
13 Nov to Jan 2010
23 Apr – 21 Jun
2010
There
is
no K.L - Penang
Accept
Reject
significant
K. L - Langkawi
Accept
Reject
relationship ticket
K. L – Kota Bahru
Reject
Reject
price of low cost
K.L–K.Terengganu
Reject
Accept
airlines in domestic
KL- AlorSetar
Reject
Reject
routes
K. L – Johor Bahru
Reject
Reject
There
is
no K.Lumpur
Reject
Reject
significant
Singapore
relationship ticket
price of low cost
airlines in regional
route
5. Conclusion
Airline Deregulation Act was a landmark event in the history of Malaysia airlines
industry. The immediate consequence of this deregulation was that the established
carrier faced competition with the low cost airlines on many fronts. First, they
compared vigorously with low cost airlines motivated in part by the belief that market
share would determine the ultimate survivors in a restructured industry. The
competition yielded new low cost airline which is fully owned by the incumbent full
service carriers and as a result of this competition, discount fares proliferated, fare
war began and total traffic increased dramatically as passengers took advantage of
previously unheard coast to coast fares.
Business is a game, for long term viability airlines need to play the role effectively in
this game. Game theory is the study of cooperative and non cooperative
approaches to games and social situations in which participants must choose
between individual benefits and collective benefits. The games involved scenarios
where participants must make decisions that affected not only the individual
participants but also all the other participants as well. Game theory is used in
economics to analyze competitive situations where the players of the game
(companies) attempt to maximize their performance in strategic situations. Their
success depends on their choices and how their competitors react to their choices
and make choices in response. Basically, airlines are facing “prisoner dilemma”, two
rival airlines operate from the same origin to a number of identical destinations.
Generally, the service package that they offered to customers is very similar, so their
rivalry reflected in their fare offerings. The trend of the fare pattern demonstrates that
a firm responds to the aggressive pricing of the competitors by pricing more
aggressively itself.
An increase in a competitor‟s price, all other things being equal will normally prompt
some passengers to switch to other airline. The reverse is also true if AirAsia raises
its prices and Firefly does not and all other things being equal, Air Asia will lose
some business An increase in the Firefly‟s price will normally shift the Firefly demand
curve to the right and assuming AirAsia ticket price hold and Firefly drop the demand
curve of Firefly will shift to the left. The substitute goods take the stand.
This study suggested that in long term profitability low cost airlines need to play
different network strategies they need to sustain cooperative pricing behaviour as a
stable equilibrium. They may compete aggressively for certain route but may form
alliance – cooperate for other routes. In the prisoner‟s dilemma it is clear that without
effective communication and understanding of mutual benefits the potential gains of
cooperation will not materialise. In the context of airline industry in Malaysia, pric e
mechanism is always being influenced by political and legal environment that
comprise laws, government, agencies, pressure groups, various organization and
individuals. Some decisions are made by government such as landing rights; service
to certain routes, equity ownership might affect the revenue of airline.
Low cost airline Air Asia for example the application to use Subang airport as its hub
had been turned down by government but now the government has allowed Fire Fly
( a subsidiary of MAS) to operate from Subang airport.
Hence, Sun Art of war chapter 3 :
So it is said that if you know your enemies and know yourself, you can win a
hundred battles without a single loss
If you only know yourself but not your opponent you may win or lose
If you know neither yourself nor your enemy, you will always endanger
yourself.
Sun Tze Art of War suggested the importance of positioning in strategy and that
position is affected both by objective conditions in the physical environment and the
subjective opinion of competitive actors in that environment. Sun Tze art of war
stressed that strategy was not planning in the sense of working through an
established list but rather that it requires quick and appropriate responses to
changing environment. In the changing world, future projections cannot be based on
past orientation low cost airlines need to understand the competitive landscape, to
analyze the customers, competitors and the company itself with special emphasis on
strategic positioning. Therefore, low cost airlines need to equip themselves with
competitor intelligence in the way to understand what the competitors are doing, their
moves and countermoves. The role of competitors in impacting consumer behaviour
and market trend must not be overlooked.For long term sustenance, the commitment
to fulfil the utility of the customers is very crucial. Laura (2007) highlighted that
JetBlue low cost airline has changed the low cost airline business model which was
purely price driven. The consumers have embraced JetBlue‟s service not only
because it is cheap but also it is better. Low cost airlines will become ineffective if
they compete only on price, they need to follow JetBlue‟s way by competing on
value.
W.Chan Kim and Renee Mauborgne (2005) argued that „the only way to beat the
competition is to stop trying to beat the competition‟. They emphasized that success
comes not from battling competitors but from making the competition irrelevant by
creating „Blue Ocean‟ of the uncontested market space. They further emphasized
that company should go for strategic move by creating value innovation. Southwest
Airlines created a blue ocean by offering high speed transport with frequent and
flexible departures at prices affordable to mass of buyers. Besides this, Southwest
Airline was able to offer unprecedented utility for travellers and achieve a leap in
value with a low cost business model.
The increasingly stiff airline competition coupled with an ever expanding of
customers‟ expectation, every firm which intent to have a firm grasp on their market
share must understand consumer buying behaviour. It is obvious that for long term
sustainability, competing in price is not a wise strategy. The patterns of fare setting
where a low cost carrier has a monopoly, suggest that it can enjoy such an
advantage but where there is competition on a route it is not clear that this
advantage can be sustained. This study suggested that low cost airlines therefore
should have the motivation to price cooperatively.
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