Figure 5 describes Ryanair‘s network in terms of routes‘ length.
It comprises mainly short-length journeys, with all its routes ranging between 130 km (i.e. Prestwick – Belfast City) and 3470 km (i.e. Bremen – Tenerife Sur) and with a median value of 1270 km. The distribution proves symmetrical with respect to the median value, forming a bell-shape histogram with the exception of two peak levels at 850 and 1450 km.
Table 10 and 11 show the first ten domestic and non-domestic routes, respectively, characterized by the highest average price offered over two months prior departure.
The distributions of parameters α and β are shown in Figures 6 and 7 respectively.
The distribution of parameter α shows a higher frequency of routes with parameter a levels around 0.008–0.01: the low levels indicate that the fares will be higher the day before departure. Parameter β shows the maximum relative frequency with levels slightly above
2 The definition of the route is to be intended as directional: outbound and inbound routes between two airports are thus considered as two different routes.
3 Prices mentioned refers to pre-tax fares indicated on Ryanair‘s website, which includes other cost categories such as airport taxes, security fees and credit/debit card handling fees.
zero; the frequency then decreases as parameter β gets higher. Approximately 50% of the routes register a β level greater than 0.1: in these cases, the purchase of the ticket two months before departure captures a price less than one tenth of the highest fare, which may occur just a few days before the date of flight.
Route length (km)
Source: our analyses on web fares collected
Figure 5. Routes distribution according to route length.
Figure 6. Distribution of the number of routes according to coefficients alfa estimated by analysing flight fares. Source: our analyses on web fares collected.
Figure 7. Distribution of the number of routes according to coefficients beta estimated by analyzing flight fares. Source: our analyses on web fares collected.
Table 10. First ten domestic characterized by the highest average price offered over two months prior departure
Departure airport Arrival airport P1-60(€)
Dublin Edinburgh 40.09
Dublin Manchester 37.22
Dublin Newcastle 36.81
Edinburgh Dublin 36.61
Dublin London Stansted 36.07
Newcastle Dublin 34.97
Dublin London Gatwick 34.14
Dublin London Luton 33.52
Dublin Aberdeen Dyce 33.13
London Stansted Dublin 33.08
Source: our analyses on web fares collected
Table 11. First ten non-domestic characterized by the highest average price offered over two months prior departure
Departure airport Arrival airport P1-60 (€)
Kaunas Dublin 142.67
Dublin Kaunas 142.10
Tenerife Sur Bremen 140.07
Tenerife Sur Dublin 124.29
Tenerife Sur Shannon 123.10
Dublin Riga 121.74
Tenerife Sur Niederrhein 118.33
Alicante Skavsta 115.85
Charleroi Brussels Menara 112.06 Frankfurt Hahn Tenerife Sur 110.28 Source: our analyses on web fares collected
Table 12 and 13 show the first ten domestic and non-domestic routes, respectively, characterized by the highest intensity of dynamic pricing, i.e. the highest values for β: the results show that dynamic pricing is more intensive with respect to non domestic routes.
In Figure 8 we show the comparison between P1-7, P8-14 and P15-60 , with respect to the set of 1829 flights monitored.
The average price P1-7 offered over one week prior departure appears higher than the average price P8-14 offered over two weeks prior departure, which in turn is higher than the average price P15-60 offered over three weeks prior departure until two months.
Therefore, as stated before, the demand for air tickets depends both on price levels and on the time interval between the purchase date and the flight date: the function of demand is subject to an exponential decrease as the advance purchasing time increases.
Table 12. First ten domestic characterized by the highest intensity of dynamic pricing
Departure airport Arrival airport β
Bournemouth Prestwick 0.250495
Shannon London Luton 0.249586
Dublin East Midlands Nottingham 0.225326
London Luton Shannon 0.22486
Bristol Ireland West Knock 0.222231
Kerry County London Luton 0.219562
Dublin Leeds/Bradford 0.215114
East Midlands Nottingham Dublin 0.211987
Bristol Shannon 0.20783
London Stansted St Mawgan 0.207788
Source: our analyses on web fares collected.
Table 13. First ten non-domestic characterized by the highest intensity of dynamic pricing
Departure airport Arrival airport β
Bremen Kaunas 0.635438
Bergamo Orio Al Serio Riga 0.603511
Riga Bremen 0.428541
Szczecin - Goleniów London Luton 0.320859
Bremen Riga 0.312646
Kaunas Bremen 0.298162
Eindhoven Bristol 0.293299
Shannon Katowice 0.283854
Frankfurt Hahn Riga 0.278691
Shannon Gdansk 0.272728
Source: our analyses on web fares collected.
Source: our analyses on web fares collected.
Figure 8. Comparison between P1-7, P8-14 and P15-60.
Same conclusions can be reached observing the box plots of P1-7, P8-14 and P15-60 as illustrated in Figures 9-11, which show the sample minimum, lowerquartile, median, upper quartile and the sample maximum with respect to the average prices according to the different advance in purchasing the flight ticket.
Source: our analyses on web fares collected.
Figure 9. Boxplot for average price P1-7 offered over one week prior departure.
Source: our analyses on web fares collected.
Figure 10. Boxplot for average price P8-14 offered over two weeks prior departure.
Source: our analyses on web fares collected.
Figure 8. Boxplot for average price P15-60 offered over three weeks prior departure until two months.
Figures 12 and 13 shows the average price trend on the Madrid Barajas – Palma De Mallorca (the highest frequency route, i.e. 140) and the Dublin – London Gatwick route. No steady price trend can be observed in either case: over the 60 days leading to the flight date, lower fares are offered as the departure day approaches, even if 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.
Finally we summarize our previous results (Malighetti et al., 2010) about the question of whether Ryanair‘s pricing strategies have changed over time.
Looking at 2006-2007 fares we calculated the average fare over a 90-day period prior to departure and the intensity of dynamic pricing for each flight in the panel, in particular analyzing the changes in these variables observed between pairs of ‗‗equivalent‘‘ flights (same route, same departure time, same week, weekday and month. Their results show that overall, both average fares and the intensity of dynamic pricing decreased in 2007.
Source: our analyses on web fares collected.
Figure 12. Average price trend on Madrid Barajas – Palma de Mallorca route.
Source: our analyses on web fares collected.
Figure 13. Average price trend on Dublin-London Gatwick.
We find that more than one-third of flights saw a price reduction of more than 10%. Now that it has become the dominant low-cost carrier in Europe, Ryanair appears to be softening its dynamic pricing activities on existing routes, typically employed to stimulate additional touristic demand: thus, booking in advance becomes relatively more expensive.
C
ONCLUSIONSThis chapter represents an attempt to identify the main features of Ryanair‘s business model with respect to the competitive and the contextual factors that have driven the choice of the average fares and their relative dynamics. Empirical evidences confirm an increasing propensity of Ryanair to operate routes within the international community and show that dynamic pricing is effectively performed by Ryanair on both domestic and non-domestic routes.
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Chapter 5