ryanair malighetti, Ryanair

[ Pobierz całość w formacie PDF ]
Journal of Air Transport Management 15 (2009) 195–203
Contents lists available at
Journal of Air Transport Management
journal homepage:
Pricing strategies of low-cost airlines: The Ryanair case study
Paolo Malighetti
a
,
*
, Stefano Paleari
a
, Renato Redondi
b
a
Department of Economics and Technology Management, University of Bergamo– Universoft, Viale Marconi 5, Dalmine 24044, Italy
b
Department of Mechanical Engineering, University of Brescia – Universoft, Via Branze, 38 – 25123 Brescia, Italy
abstract
Keywords:
Dynamic pricing
Low-cost
Ryanair
Fares
We analyse the pricing policy adopted by Ryanair, the main low-cost carrier in Europe. Based on a year’s
fare data for all of Ryanair’s European flights, using a family of hyperbolic price functions, the optimal
pricing curve for each route is estimated. The analysis shows a positive correlation between the average
fare for each route and its length, the frequency of flights operating on that route, and the percentage of
fully booked flights. As the share of seats offered by the carrier at the departure and destination airports
increases, fares tend to decrease. The correlation of dynamic pricing to route length and the frequency of
flights is negative. Conversely, as competition increases discounts on advance fares rise.
2008 Elsevier Ltd. All rights reserved.
1. Introduction
In the airline business, the maximisation of the profits obtained
from each flight is strictly related to the maximisation of revenues,
because many of the costs incurred are essentially fixed, at least in
the short term. Pricing has always represented an important factor
in the carriers’ choices, driving the adoption of different strategies
by low-cost and full-cost carriers. Full-cost carriers choose price
discrimination techniques based on different fare classes, complex
systems of discounts with limited access, customer loyalty
schemes, and overbooking techniques. Low-cost carriers instead
use ‘‘dynamic pricing’’. Because of dynamic pricing, it is now
common for people to buy air tickets to European destinations for
less than V10.00 (airport taxes excluded).
This paper deals with the pricing policies of low-cost carriers,
offering a detailed analysis of Ryanair, the main developer of the
low-cost model in Europe. Generally speaking, fares tend to
increase until the very last moment before the closing of bookings.
If it is assumed that Ryanair aims to maximise its profits, it is to be
expected that travellers are prepared to bear higher costs more
easily as the date of flight approaches. We aim to identify the
competitive and contextual factors that drive the choice of the
average fares, and their relative dynamics. In details, our analysis
will focus on Ryanair’s pricing policies in correlation with the
features of its airport network. The results show that the fare policy
is clearly innovative relative to traditional pricing strategies, and
that the fares are influenced by the competitive economic context
in which the route is offered.
In recent years, the entry of low-cost carriers has totally revo-
lutionised the air passenger transport industry. The low-cost
business model was introduced by Southwest in the US at the
beginning of the 1970s. However, it was only in the 1990s that the
phenomenon spread worldwide. Ryanair was one of the first
airlines in Europe to adopt the low-cost model in 1992. Easyjet,
Ryanair’s main low-cost competitor, was founded in 1995.
Although the phenomenon is relatively recent, the stunning results
obtained by low-cost carriers urge academics to study the reasons
for their success.
The reduction of costs lies at the core of the low-cost business
model, which aims to offer lower fares, eliminating some comfort
and services that were traditionally guaranteed (hence the defini-
tion of ‘‘no frills’’, often employed to refer to low-cost flights). The
use of an on-line booking system, the suppression of free in-flight
catering, the use of secondary airports connected through a point-
to-point network, and the use of homogeneous fleets are only a part
of the innovative choices made by low-cost airlines.
Many studies have analysed low-cost businesses, highlighting
the keys to lower costs (
Alamdari and Fagan, 2005; Doganis, 2006;
Franke, 2004
), and the role played by entreprership (
Cassia et al.,
2006
). The containment of costs is only one of the reasons for the
success of a low-cost carrier. Alertness to ‘‘latent demand,’’ char-
acterised by the passenger’s willingness to pay elastic prices, which
is not the attitude of the so-called ‘‘traditional’’ passenger, is among
the key factors.
2. State of the art
This study refers to two main fields of literature, namely the
analysis of the low-cost business model and the study of dynamic
pricing techniques. The main point of interest is the extraordinary
* Corresponding author.
E-mail address:
(P. Malighetti).
0969-6997/$ – see front matter 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jairtraman.2008.09.017
196
P. Malighetti et al. / Journal of Air Transport Management 15 (2009) 195–203
performance of the major low-cost carriers, especially when
compared with the trend, and the average profitability, of the air
transport industry in general. Researchers have extensively exam-
ined the cost-effective policy, which so clearly permeates the low-
cost business model.
Franke (2004)
and
Doganis (2006)
have
focused in particular on the cost benefits that low-cost carriers can
derive from their operational choices. Their studies show that there
is no single driving element responsible for the competitive
advantage. Rather, all the choices made contribute to the produc-
tion of cost benefits.
Gudmundsson (2004)
, using a longitudinal
survey approach, studies factors explaining the success probability
of the ‘‘new’’ airlines and finds that productivity and brand image
focus are significantly related to financial non-distress, whilst
market power (market-share) focus is significantly related to
financial distress.
A first mover competitive advantage could explainwhy the most
successful airlines seem to be able to maintain their market lead-
ership in the short and medium term, are the ones that gave rise to
the phenomenon, as witnessed by the likes of Southwest in the USA
and by Ryanair and Easyjet in Europe. It is clear that, a good low-
cost strategy can never be replicated in all its detailsdand this
could account for the carriers that succeeded as well as for those
that did not.
Alamdari and Fagan’s (2005)
study quantified the
impact of the deviation from the original low-cost business model.
The importance of the different strategic choices made by
carriers suggests investigating other elements of the low-cost
business model. Revenue analysis is an important element that has
been less studied. Indeed, the generation of revenues is one
distinctive aspect differentiating low-cost from full-cost airlines
policies.
Piga and Filippi (2002)
have analysed the pricing policies
of the low-cost business model in comparison with the pricing
strategies of the full-cost airlines. Coherent choices seem to be
essential in pricing policies as well. For instance, the widespread
use of the Internet for the sale of tickets tends to decrease price
dispersion. This phenomenon may in part be attributed to the
‘‘efficiency of electronic markets,’’ as defined by Smith (
Smith
et al., 2000
).
The success of the low-cost model is based on a fragile balance
between fare levels, load factors and operating costs. The structure
of revenues and the determination of prices are nearly as important
as the minimisation of costs in the equation of profits. Indeed, an
excellent pricing strategy for perishable assets results in a turnover
increase, ceteris paribus, which can be quantified between 2% and
5%, according to
Zhao and Zheng’s (2000)
study.
The analysis of fare levels and policies aims to understand the
key factors in the achievements of low-cost carriers, including the
effects of the competitive interaction between carriers (
Pels and
Rietveld, 2004
). The price choices and the ability of the airlines to
understand the characteristics of the demand, in either a condition
of monopoly or a competitive context, are decisive in the balance of
the business model itself. Fare dynamics must be taken into
account in a thorough evaluation of market competitiveness, and of
the benefits travellers have achieved through deregulation.
This paper analyses the pricing strategies adopted by Ryanair
against the characteristics of the context in which it operates,
including the degree of competitiveness.
First, the study deals with the demand curve derived from
Ryanair’s prices. The analysis starts from the microeconomic prin-
ciples of dynamic pricing. Generally speaking, airlines deal with
perishable goods sold in different time steps, with the aim to
maximise profits. The offer of seats on a flight can be compared to
the sale of ‘‘perishable assets’’ with pre-determined capacity in
conditions of negligible marginal costs. The themes investigated by
the relevant literature are dynamic pricing and yield management.
Zhao and Zheng (2000)
have determined the minimum
conditions required for optimal dynamic pricing. Because the price
trend is influenced by demand, one part of the literature focuses on
optimal pricing policies by using specific functional forms to
represent demand and customer benefits. For example, it is quite
typical to use an exponential demand curve (
Gallego and Van
Ryzin, 1994
) and a mechanism ‘‘of customer arrival’’ into the
market with a probability similar to a Poisson process. The studies
mentioned above presuppose a continuous optimal price function.
Other studies are more likely to hypothesise the existence of
a limited range of prices (
Wilson, 1988
). The present study adopts
a continuous function, because Ryanair offers a wide range of
prices.
The study of price dynamics raises interesting questions. Many
travellers have probably noticed that prices often tend to increase
as the flight date approaches. According to
McAfee and te Velde
(2006)
, in the period preceding the flight date, the price trend
mainly depends on the trade off between the option of waiting for
a potential lower price, and the risk of seats becoming unavailable.
In this case, the functional form of the demand curve, together with
its adjustment over time, also help to determine a series of
minimum prices.
This study analyses the range of actual prices on all of Ryanair’s
routes. It aims to validate some of the assumptions made in the
literature through a thorough study of this wide empirical sample.
The estimated demand curve makes it possible to make inferences
about the trend of bookings and the curve relating to the fully
booked aircraft.
Stokey’s (1979)
studies determined an optimal
constant filling curve in a context of monopoly. Similar results can
be obtained by using a demand with functional forms belonging to
the family of continuous functions presented by
Anjos et al. (2005)
.
For such functions, when dealing with goods that are to be sold by
a given deadline, it is possible to define and implement the optimal
pricing strategy. The reference curves adopted in this study belong
to the Anjos family of curves.
The structure of demand, which guides the optimisation choices
of the carrier, is influenced by the presence of competitors, and the
passengers’ opportunities to opt for a substitute service. Classical
studies, starting from
Borenstein’s (1989)
analysis, have mainly
focused on the airlines’ average fare level, showing the undeniable
influence exercised by the competitive structure on the fares of full-
cost airlines. Such competitive structures are exemplified by a fare
premium correlated to the dominance of the hub of reference.
Alderighi et al. (2004)
have pointed out that full-cost airlines tend
to decrease fares on routes also operated by low-cost carriers. The
influence of the competitive structure on the pricing strategies of
low-cost carriers has been less studied, as far as we know.
Pels and
Rietveld’s (2004)
studies have examined the evolution of fares on
the London–Paris route; traditional behavioural models do not
seem to apply here, given the mixture of direct and indirect
competition.
It is not clear whether the presence of other airlines can criti-
cally affect the pricing strategies of low-cost carriers.
Pitfield (2005)
has analysed the routes originating fromNottingham East Midlands
airport in 2003, when it was possible to observe low-cost airlines in
direct competition. The results showed a weak influence of the
competitive structure on prices. The historical pattern of fares
offered by each airline seems to play a more important role, as
would be expected in a situation of price leadership. In a study
examining the London–Berlin and London–Amsterdam routes,
Barbot (2005)
found that the low-cost and full-cost markets coexist
on totally separate levels, so that low-cost carriers compete ‘‘only’’
among themselves, as do full-cost carriers.
The approach we have adopted here focuses on the different
behaviours assumed by carriers according to the distinctive char-
acteristics of the routes they operate. We aim to identify the
competitive and contextual factors that drive the choice of the
average fares, and their relative dynamics.
P. Malighetti et al. / Journal of Air Transport Management 15 (2009) 195–203
197
3. Methodological aspects
This study considers the functional form of demand as proposed
by
Anjos et al. (2005)
, where the demand for air tickets depends on
price levels, and on the time interval between the purchase date
and the flight date, according to
The literature on low-cost carriers highlights the important role
played by dynamic pricing. It is assumed that once the flights have
been scheduled, the marginal costs incurred in relation to the
number of passengers are practically null. It follows that the
maximisation of profits is strictly dependent on the maximisation
of the revenue function. Let the reference unit of time be the single
day.
1
Considering T days, the revenue R can be expressed as
q
i
¼
Ae
a
$p
i
F
ð
i
Þ
where i˛
½
1
;
K
;
T
(6)
where A and
a
are two constants, and F(i) is a function positively
correlated to the time period between the purchase date and the
flight date. In this case, the function of demand is subject to an
exponential decrease as the advance purchasing time increases.
An advance booking is less useful because people are less sure of
their plans far in advance. Given the functional form of the demand
in expression
(6)
, it is possible to identify the optimal pricing
strategy by substituting the following form for p
i
in expression
(5)
.
R
¼
X
T
p
i
q
i
(1)
i
¼
1
where p
i
is the flight price on the day i of the year, and q
i
is the
number of seats booked on the same day. The optimal pricing
strategy results from the maximisation of the previous expression,
under the binding limit of the aircraft’s capacity, which can be
expressed as
1
a
$F
ð
i
Þ
p
i
¼
m
þ
(7)
The multiplier
m
can be viewed as the extra charge assigned to
the fully booked flights.
2
In the next section, some F(i) forms will be
tested on Ryanair’s actual prices. The parameters of the price
function will be estimated by minimising the quadratic error
compared to the actual prices. The underlying assumption is that
Ryanair operates by maximising its revenues, and using a demand
function similar to function
(6)
. Therefore, the accuracy that may be
obtained using the model for the estimation of prices enables
assessment of the validity of the forms of the demand curves.
Through the substitution of the optimal price expression
(7)
in
the expression
(6)
,wehave
X
T
q
i
Q
(2)
i
¼
1
where Q is the capacity, that is, the total number of seats available
on the aircraft.
For the purposes of this study it is assumed that, for the specific
route and type of customers availing themselves of low-cost flights,
the operator is not a price-taker. We hypothesise that the
competitive structure and the level of market and product differ-
entiation enable the operators to modify the price variable. The
maximisation problem can be solved through a ‘‘lagrangian’’.
q
i
¼
Ae
1
(8)
p
i
q
i
þ
m
Q
X
!
L
¼
X
T
T
Expression
(8)
implies that, following the application of the
optimal price, the expected demand is steady over time. If the
quantity sold over a certain time span is greater than the steady
expected quantity, the operator may decide to raise the price.
Similarly, the operator may decide to reduce the price in order to
gain demand when demand is scarce.
In the empirical calculations, two functions are used for the
estimation of prices. The first expression is
q
i
(3)
i
¼
1
i
¼
1
where
m
represents the Kuhn–Tucker’s multiplier, which takes into
account the aircraft limit of capacity. It follows that
m
Q
X
!
¼
0
T
q
i
i
¼
1
If the limit of capacity is reached,
m
>
0; if not,
m
¼
0. In order to
determine the optimal price p
i
at the specific time i, the derivative
of the expression
(3)
with respect to p
i
must equal zero, thus
obtaining
1
a
$
ð
1
þ
b
$i
Þ
p
i
¼
m
þ
(9)
where i is the number of days between the advance reservation and
the flight date. The form of the optimal price is a hyperbola with the
price going up as the flight date approaches. This functional form
makes it impossible to obtain price reductions as the flight date
approaches.
A more complete functional form is
p
j
m
v
q
j
v
p
i
¼
q
i
þ
X
T
v
L
v
p
i
¼
0 where i˛
½
1
;
K
;
T
(4)
j
¼
1
This expression can be held valid even if the markets on the
different days are not ‘‘separated.’’ In this case, for example, the fare
during one period can modify the quantity of available seats in
a successive period, that is,
v
q
j
/
v
p
i
s0withi s j.Inlinewithmanyof
the studies analysed in the literature, for the purpose of this study, it is
assumed that the markets for the purchase of air tickets are separated
in time, that is
v
q
j
/
v
p
i
¼
0withi s j. A later development of this study
will eliminate this hypothesis in order to verify the possible interaction
between the demands of the different periods.
Here, expression
(4)
is simplified in the following optimal
conditions:
1
p
i
¼
m
þ
1
þ
b
$i
þ
g
$i
2
þ
q
p
(10)
a
$
In this case, the price may decrease as the departure date
approaches. The degree of accuracy of both functional forms will be
discussed in the next section.
The hypothesis is that Ryanair has tailored a pricing strategy for
specific routes. In other words, it is assumed that Ryanair holds
specific values for the parameters in
(9)
and
(10)
for each individual
route. An estimation of the parameters of the price functions is
made for each route using data from the 90-day period before the
flight date.
q
i
þð
p
i
m
Þ
v
q
i
v
p
i
¼
0 where i˛
½
1
;
K
;
T
(5)
1
Demand and prices are assumed to be fixed over the single day.
2
Fully booked flights have no available seats on the day before departure.
 198
P. Malighetti et al. / Journal of Air Transport Management 15 (2009) 195–203
4. Sample and descriptive analysis
Table 1
Variation of the network operated by Ryanair between July 2005, and June 2006.
4.1. Reference data
D
%
Variable
7/1/2005
6/30/2006
Number of served airports
95
111
16.8
Our database includes the daily fare for each route
3
operated by
Ryanair over the 4 months prior to the flight. The study examined
all the flights scheduled by Ryanair from 1st July, 2005, until 30th
June, 2006. The database enables (1) a comparison between the
fares for the different routes, and (2) tracing the fare variation for
each individual route as the flight date approaches.
Number of daily flights (average)
650.2
820.7
26.2
Number of routes
442
594
34.4
Percentage of routes
with daily flight frequency
70.1%
70.8%
Percentage of routes with
more than a daily flight frequency
23.6%
3.4%
Percentage of routes
with no daily flight frequency
6.3%
25.8%
4.2. Characteristics and evolution of the network operated
by Ryanair
instance, the figures demonstrate that in 75% of cases (75th
percentile in
Fig. 3
) the price
5
does not exceed V50 for bookings
made at least 20 days earlier than the actual date of flight. On the
contrary, during the last week prior to the flight all prices increased
sharply, with ticket prices exceeding V75 within 3 days of the date
of flight in 50% of the cases, and topping V200 in 5% of the cases.
The impression of a steady increase in prices as the date of flight
approaches is verified only on average. As a matter of fact, Ryanair
makes sure to provide ‘‘special offer’’ periods in which fares reach
their lowest. Such periods do not seem to have any particular
recurrence in terms of length and time. When restricting the
analysis to flights operated on the same route only, it is not possible
to mark a specific period for promotions. Indeed, most routes show
a slight increase in prices, or at least a steady upward trend similar
to most of the percentiles shown in
Fig. 3
.
Fig. 4
shows the average
price trend on the Rome Ciampino–London Stansted route (one
with high-frequency service), while
Fig. 5
shows the exact price on
specific dates.
Fig. 4
compares two price trends pertaining to two
dates, neither of which falls on a holiday (such as a bank holiday or
a religious festival). No steady price trend can be observed in either
case: over the 90 days leading to the flight date, lower fares are
offered as the departure day approaches, but this occurs in the two
cases during different periods of time, with different lengths and
intensities. If it is assumed that this phenomenon may occur often
in Ryanair’s pricing policy, it may be inferred that the expectations
of the passengers should admit a probability (p) for the price to fall
in the following days.
Thanks to the database at our disposal, we were able to inves-
tigate the recurrence of special offers in Ryanair’s pricing policy. For
each individual flight we calculated the percentage of days on
which the price offered was lower than any other previous price.
Data were gathered per route, and analysed according to the
pattern of distribution of the percentages.
Fig. 6
shows the distri-
bution by percentiles on the Rome Ciampino–London Stansted
route. On this route, 50%
6
of the flights monitored (‘50th percentile’
in
Fig. 6
), do not show any downward price trend within 30 days of
the date of flight. In the case of 30-day advance bookings, 25% of
cases (75th percentile curve) were recorded with at least 6
following days on which prices were lower than the one recorded
on the day, whilst 2.5% of cases recorded at least 18 days in a row
with lower prices.
The data gathered do not provide information about the actual
number of seats booked for each single flight. Conclusions on
volumes are drawn in the empirical analysis (
Fig. 10
). Nevertheless,
the data show whether the flights were fully booked in the 24-h
period before the scheduled departure date. As a matter of fact,
Ryanair makes use of a non-refundable ticket policy and no
Ryanair’s network is characterised by a very dynamic and steady
expansion. A comparison between the data gathered as of 1st July,
2005, and later on 30th June, 2006, gives a clear picture of the
dimensions of the phenomenon: in July, 2005, Ryanair served 95
airports, increasing to 111 one year later; over the same period,
routes expanded by 34.4%, reaching the total number of 594 (see
Table 1
).
Nonetheless, 25 routes that were operated in July 2005 were
then cancelled; 6 routes saw their flight frequency halved; and the
frequencies of 16 other routes were each decreased by more than
10%. Ryanair operates on many low-frequency routes, 70.8% of the
overall network being made up of routes with only one single flight
per day. By and large, it may be said that Ryanair serves its routes
daily. However, in 2005–2006 this trend changed, as the number of
routes with no guaranteed daily flight increased from 14 to 77.
An estimation of Ryanair’s ASK
4
(Available Seat Kilometres)
distribution is made possible by the information available about the
scheduled flights, and the distance between the departure and
arrival airports. From a geographical point of view (see
Fig. 1
),
Ryanair’s main business focuses on the connection between
England, Ireland, and the rest of Europe (44.2% of the routes, and
49% of the flights start at British or Irish airports). The major flow
(measured in ASK) is between Italy and England (14.6%) (see
Table 2
).
Apart from the British Isles, the main flow is between Italy and
Spain (3.1% of the whole business). The domestic routes play
a relatively small role, accounting for less than 5% of the scheduled
air traffic, in terms of numbers of both flights and routes. Italy is the
only place besides the British Isles in which Ryanair operates
domestic routes.
The significant increase in new routes and markets occurred in
a surprisingly balanced way. The geographic distribution in 2006 is
approximately the same as in 2005, with only a slight decrease in
service to the UK (
Table 3
).
Ryanair’s network comprises mainly short-length journeys,
with all its routes ranging between 200 km and 2000 km and with
a median value of 1040 km (as shown in
Fig. 2
). The distribution
proves symmetrical with respect to the median value, forming
a bell-shape histogramwith the exception of two peak levels at 450
and 1800 km.
Fig. 3
shows the percentile distribution of ticket prices with
respect to advance booking in days. It is understood that prices may
vary according to other parameters as well, for example, route
specificity. Yet the role played by advance reservation in Ryanair’s
pricing policy is so significant that average figures also provide
important
information about Ryanair’s pricing strategies. For
3
At the beginning of July 2005, the total number of routes was 442, while by the
beginning of July, 2006, it had risen to 594. The definition of route is to be intended
here as directional. Outbound and inbound routes between two airports are thus
considered as two different routes.
4
ASK (Available Seat kilometres) accounts for the number of seats available on
a flight multiplied by the route’s length (in kilometres).
5
Unless otherwise stated, the price mentioned throughout this work refers to
the ‘‘net’’ fare indicated on Ryanair’s website, which excludes other cost categories
such as airport taxes, security fees and credit/debit card handling fees.
6
Referred to 691 out of 1382 flights monitored on the route analysed.
P. Malighetti et al. / Journal of Air Transport Management 15 (2009) 195–203
199
Table 3
Distribution of Ryanair’s flights considering their country of origin and their
variation.
Situation as of 1/7/2005
Situation as of 6/30/2006
Routes
Daily flights
(average)
Flight
share
Routes
Flights
(average)
Flight
share
AT
5
6.0
0.9%
5
6.0
0.7%
BE
11
16.6
2.6%
16
21.7
2.6%
CZ
1
1.0
0.2%
1
1.0
0.1%
DE
41
50.6
7.8%
48
61.5
7.5%
DK
2
2.7
0.4%
2
2.9
0.3%
ES
52
65.3
10.1%
61
75.1
9.2%
FI
3
3.0
0.5%
3
3.4
0.4%
FR
30
40.1
6.2%
46
55.3
6.7%
HU
–
–
–
1
1.0
0.1%
IE
50
91.8
14.1%
80
123.7
15.0%
IE (domestic)
(2)
(6.0)
(0.7%)
IT
69
94.4
14.5%
78
108.0
13.2%
IT (domestic).
(6)
(10.0)
(1.5%)
(6)
(12.0)
(1.5%)
LT
–
–
–
3
3.0
0.4%
LV
4
4.0
0.6%
6
6.6
0.8%
NL
5
5.7
0.9%
6
6.7
0.8%
NO
5
6.3
1.0%
7
8.3
1.0%
PL
1
1.0
0.2%
18
19.4
2.4%
PT
3
4.0
0.6%
7
8.0
1.0%
SE
17
23.0
3.5%
20
26.0
3.2%
SK
–
–
–
4
5.0
0.6%
UK
143
234.8
36.1%
183
278.1
33.9%
UK (domestic)
(10)
(21.5)
(3.3%)
(14)
(25.4)
(3.1%)
Total
442
650.2
100%
594
820.7
100%
Fig. 1. Distribution of Ryanair’s flights and served airports (as of 30th June, 2006).
overbooking procedures, which means that when a flight is fully
booked the website shows the unavailability of seats. For each
individual flight, we calculated the fully booked flight ratio (where
fully booked flights are defined for our purposes as those with no
available seats in the last 24 h before departure).
Fig. 7
shows the
distribution of routes according to the fully booked flights ratio.
The same period was monitored for all routes and covered all of the
months considered in the analysis. The figures highlight the
unquestionable ability to fill all seats available on the different
routes, with most of Ryanair’s routes being declared fully booked
10–20 times out of 100.
before departure. A low
b
will show a steady price trend as the
number of advance booking days increases. On the contrary, a high
b
indicates a significantly discounted fare, with respect to the
highest fare ever offered, on advance purchases. Finally, parameter
m
shows the average surcharge in the cases of flights characteris-
tically fully booked on the day before the scheduled departure.
These parameters were calculated for all routes for which fares
dating back to at least three months before the actual date of flight
were available. 550 out of the 594 monitored routes have been
taken into consideration. The remaining routes had been only
recently introduced, were monitored for less than three months,
and consequently were not taken into account. The parameter
estimates were carried out by minimising the standard error of the
predicted fares for each individual route.
5. Empirical analysis
In the empirical analysis, we applied Eq.
(9)
to estimate the price
trends for each individual route. The equationwas obtained from an
exponential demand function subject to Ryanair’s profit max-
imisation, and showed that, as the date of flight approaches, the
price trend tends to resemble a hyperbola driven by parameters
a
and
b
, where
a
indicates the highest price level that may be
reached during the last days before the scheduled departure date.
The lower
a
is, the higher the fare will be the day before departure.
Parameter
b
indicates instead a decrease in the fares that is directly
proportional to the increase in the number of advance booking days
Table 2
Main flows between nations operated by Ryanair (as of 30th June, 2006).
Rank
Country pairs
ASK (daily average)
%
1
England - Italy
22,301,303
14.6
2
England - Spain
19,538,299
12.8
3
England - Ireland
11,175,029
7.3
4
England - France
10,620,722
6.9
5
England - Sweden
6,754,428
4.4
6
England - Poland
6,160,721
4.0
7
England - Germany
5,353,365
3.5
8
Ireland- Spain
5,228,941
3.4
9
Italy - Spain
4,796,374
3.1
10
Italy - Germany
4,680,690
3.1
Fig. 2. Route distribution according to route length (as of 1st July, 2006).
[ Pobierz całość w formacie PDF ]

  • zanotowane.pl
  • doc.pisz.pl
  • pdf.pisz.pl
  • frania1320.xlx.pl

    • Tematy