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According to international trade body airlines body, International Air Transport Association (IATA), India’s domestic air travel demand grew at 14% in June compared to last year figures, making it the second fastest in the world. India recorded second fastest growth rate after Brazil, which reported a demand expansion of 15.1% in June  . The industry recorded a load factor of 78.3%, which is consistent with the global average of 79.4%  .
It has been observed that the air ticket prices vary with the passenger load across different time slots in a day and routes (different set of origin-destination (O-D) combinations).
The heavy traffic routes like Delhi-Mumbai have significant passenger volume for most of the time in a day and hence, the number of airlines operating between these routes is greater in number. This results in competitive pricing of tickets on these routes. Similarly, the prices also vary across different times, for example, the morning and evening flights having greater passenger volume. To reap maximum benefits, the airlines explore attractive pricing opportunities. Mostly, the aircrafts employed on these routes are bigger to cater to this heavy demand.
On the other hand, for low traffic routes like Mumbai-Nagpur, very few airlines operate and hence, they set monopolistic prices. The aircrafts employed on these routes are smaller, so to cover the fixed cost of operations, the airlines charge exorbitant prices per ticket on these routes. The airlines set different prices for the same distance on across different routes.
For the same distance, the saturated domestic airline industry in the US shows far more volatility in air ticket prices. The heavy competition in this market results into this kind of trend in pricing behaviour.
This implies that the price of an air ticket is a variable entity dependent on various factors. The objective of the project is to study the ticket pricing behaviour in Indian Airline Industry. Also, we aim to benchmark these on some of the parameters with saturated domestic airline ticket prices in the US.
For the purpose of this project, a set of air routes catering to tier-1 and tier-2 Indian cities were chosen. To compare and benchmark the outcome of the study, US domestic routes and a low distance international route were chosen. The details of the routes are given below  :
For the purpose of our study we have taken the following combinations of O-D pairs:
Heavy Traffic domestic routes in India
Low Traffic domestic routes in India
Domestic routes in the US
Low distance international route from India
The price points for the chosen routes were collected on a daily basis. The criteria used for collecting the price points are described below:
The price points collected were for one-way trip only.
Flights offering lowest fare between the selected O-D pairs were chosen.
Days to Fly: Two sets of price points were collected – Date of Journey (DoJ) as July 17th (15 days to DoJ) and August 1st (30 days to DoJ). During the initial phase of the project, the prices were recorded on alternate days. When the ‘days to fly’ were 14-15 days, the price points were recorded on a daily basis.
The price points were collected for different times of the day – morning slot (6 am – 9 am), afternoon slot (12 noon – 3pm) and evening slot (6 pm – 9 pm) for each O-D pair and for each of the ‘Days to fly’ combinations.
Channels for booking of air ticket: Only internet (online) mode for booking of air tickets was used. The price points were collected from airlines’ websites and travel websites (Makemytrip.com and Yatra.com) for Indian domestic and India-Singapore air ticket booking. For studying the US domestic routes, three travel websites were chosen – Expedia.com, Travelocity.com, and Orbitz.com.
The price points collected are inclusive of the base fare and all the taxes levied. The flights chosen for the study are non-stops flights as the prices change with the stops (distance increases with diversions).
The data collected for air ticket pricing was grouped as domestic heavy traffic routes and domestic light traffic routes. The data for routes, Delhi-Mumbai and Bangalore-Kolkata, was grouped as domestic heavy traffic. The data for routes Mumbai-Nagpur and Delhi-Indore was grouped as domestic light traffic. Then, two regressive models where developed based on these data sets with price as dependent variable and distance, time slot, days to fly, mode of booking (airline website and travel websites), and type of airlines as independent variables. The regressive model gave us the basic relationship of price with the independent variable parameters.
A correlation matrix was generated to assess the impact of competition on the prices of air tickets.
The data was analysed to compare the price per km for Indian domestic heavy traffic and Indian domestic light traffic routes.
The concept of price volatility index (PVI) was developed to compare the price fluctuations over days for Indian domestic and US domestic airliners.
To understand the pricing mechanisms adopted by different airlines, telephonic interviews were conducted with the marketing and operations executives of MakeMyTrip.com and British Airways. The objective was also to understand the price variations on different travel websites. For example, the rationale for these price variations could be different fee/ commissions charged by travel websites.
Heavy Traffic domestic routes in India
Typically, the heavy traffic domestic routes in India are between the metro cities like Mumbai, Delhi, Chennai, Kolkata and Bangalore. These cities are commercial hubs and power centres in the country. Heavy traffic is observed between these pairs of cities in all modes of transport. The airway is no exception to this phenomenon. From the collected data points, it was observed that the price volatility on Delhi-Mumbai and Bangalore-Kolkata routes was not very significant. Also, the price fluctuations between the different times of the day were not significant. The prices for all the airlines flying on these routes were comparable. Mostly, the price increased during the last 3-4 days of departure of the flight. Otherwise, it was mostly constant. The price was more or less constant across various travel websites and airline websites. The main factors responsible for this typical behaviour exhibited in air ticket pricing on these routes are as follows:
Competition: Almost all the domestic airlines fly between these O-D pairs. Also, each player flies 4-5 flights between these O-D pairs. The competition has led to each airline charge its price close to its marginal cost of flying and operations, which explains why the fares of all the airlines are comparable. No airline has the incentive to deviate from their marginal cost covering pricing strategy. They rely on the volumes rather than higher margins on these routes. The reason for the ‘not varying prices’ across different times is the presence of option for leisure travellers to go for other airlines at slightly different time and get a better deal for flying. So, this has led to a constant price across different times. As all the airlines fly on these routes, all of them have tie ups with the all travel agencies and websites. Each of them has a similar discount schemes for each of these travel agencies and hence, the price variations across these websites cannot be observed for these O-D pairs.
Differential Slab-based Pricing Scheme: The air tickets for most of the domestic airlines in India are classified as Business class and Economy class. Business class travel is maximum between the metros and hence, all the airlines offer that at comparable prices. The economy class is further divided into O, P, M, N, etc. slabs. In each slab, there are 7-10 seats and are priced differently. At any given time, the prices indicated on the company or travel websites reflect the lowest fare slab ticket available at that time. If the tickets are booked well in advance, say 25-30 days before of the date of travel, passenger is expected to get a ticket in the lowest fare slab. As time for departure comes by the tickets in the lower fare slab get exhausted and the passenger gets exposed to higher price slab tickets. But on heavy traffic routes like those of Delhi-Mumbai, this effect of slab-based pricing is slightly diluted as the airlines adjust the prices to take into consideration the intense competition and fear of losing their passenger to other airlines. This slab-based effect is prominently observed when there are 3-4 days left for departure and almost all the airlines have sold their low fare slab tickets. At this point of time, it was observed that there was a significant increase in the price of tickets by all airlines across travel websites.
Bulk ticket deal for air travel websites: Many times, it is observed that the air tickets available on air travel websites are cheaper than those of airline website, as is the case with Air India. The reason for this is that these air travel websites go for a bulk deal with the airlines and tickets in the lower fare slab. So, once the tickets in lower slab are sold by the airline to passengers it moves to the higher slab, but the travel websites have tickets in lower slabs available and hence, the passenger benefits by booking the tickets from travel websites rather than from the airline websites.
Travel Distance: The air travel distance also determines the air fare for the metros. The distance between Delhi-Mumbai is less as compared to Bangalore-Kolkata. The ATF requirement is higher for Bangalore-Kolkata route than Delhi-Mumbai route. Also, the operational expense is lesser in Delhi-Mumbai case. As a result of this, the air fare is significantly less on this route as compared to Bangalore-Kolkata route.
The trends highlighted in the above discussion can also be observed graphically.
Figure 1: Price patterns from company website for morning slot flight (Bangalore – Kolkata route)
Figure 2: Price patterns for morning slot flight [data recorded from three different websites] (Bangalore – Kolkata route)
Low Traffic Domestic Routes in India
Typically, these routes are between metros and tier-2 cities like Delhi-Indore, Mumbai-Nagpur, etc. There are few frequent fliers on these routes. Mostly people travel between these routes for business purposes and vacation. Vacation travel forms a bulk of the traffic. These travellers are very price sensitive and mostly look out for the cheapest of the options available. They are willing to travel at later dates and times if they get better deal. Few airlines operate with a direct flight on these routes; most of them have break-journey (one-stop). Mostly, they exhibit a monopolistic behaviour but consider the elastic buying behaviour of the customer while pricing the tickets. There is a difference in the prices of the tickets at different times of the day. The main factors which determine the air ticket prices on these routes are as follows:
Competition: Very few airlines operate on these routes. There are very few players who offer direct flights. The lack of competition gives a monopoly to the operators. They charge the tickets at a higher price for morning flights, but charge them at lower prices during afternoon. The morning flights usually cater to the needs of the people who go to metros for business purposes and are indifferent to prices and hence, are willing to pay higher price. The afternoon flights usually cater to the needs of price sensitive vacation travellers and hence, provide them with lower prices.
Differential pricing slabs: On these routes, the airlines don’t put differential pricing slabs but change the prices purely on demand at given point of the time. They keep the prices low if you book the tickets 25-30 days in advance. Then, based on the demand they increase it. But as the departure date approaches, if they find that there is not enough demand and there is a probability of some seats going vacant due to higher fare, they reduce the fare to make people travelling by train switch to their airlines. The airlines slightly mark-up the ticket prices on Friday evenings and Monday mornings to benefit from the inelastic demand on these routes. The airlines operate on these routes clearly to cater price sensitive crowd and IT employees from tier-2 cities working in metros.
Seasonality: The prices of tickets for these routes are significantly higher during the festive seasons as the airlines predict a heavy demand for the people travelling to their home towns during festivals and switching air travel due to shortage of train and bus tickets during festivals.
Travel Distance: The air travel distance and occupancy per Km determines the price of the air tickets on these routes. The Mumbai-Nagpur distance is greater and also its occupancy per Km is more as compared to Delhi-Indore. Hence, Mumbai-Nagpur air ticket price is higher as compared to Delhi-Indore.
The graphical representation of the trends observed on Delhi – Indore route are shown below.
Figure 3: Price patterns for evening slot flight [data recorded from three different websites]
Figure 4: Price patterns for evening slot flight for the airlines offering non-stop flights
Figure 5: Price patterns for morning and evening flights (Airline: Jetlite)
Pricing of Air Tickets on the US Domestic Routes
As with any airliner, the operators on US domestic routes set price bands for various categories, for example, L, M, N, O, etc., of seats. These prices depend on the flexibility given to a passenger for cancellation, upgrading to next level, refunds, etc. The airlines allocate only a certain number of seats at each fare level for each flight. The number of seats allocated at each fare level depends on many factors, such as the route involved, the time of year, the usual business/leisure passenger breakdown on that route, the time of day, etc. Airlines have inventory control departments to determine how many seats are allocated at each fare on each flight. Since the time span of the project is limited, the analysis may not capture all the parameters that play a role in the pricing of air tickets.
For studying the pricing trends on US domestic routes, three travel websites – Expedia, Travelocity and Orbitz – were used for collecting the prices. The routes chosen for the study include New York (JFK International Airport) to Los Angeles (NYC – LA) [flight distance: 3,994 Kms], New York to Chicago (NYC – CHI) [flight distance: 1,149 Kms], and New York to Pittsburgh (NYC – PIT) [flight distance: 4,150 Kms]  .
The airlines sell on first-come first-serve basis. Passengers get cheaper fares by booking earlier. The price thus automatically responds to variations in demand. From the data collected, it was observed that the volatility in the prices was higher prior to 12 – 14 days from the date of journey.
Comparing the airfares on three travel websites considered, it was observed that the fares were more or less same on all of them. Some of the websites (for example, Travelocity) also have provision for price comparison functionalities. This indicates that the market in the US is very competitive.
The pricing of air tickets also depends on the route. On the NYC – LA route, the demand is higher compared to NYC – CHI, or NYC – PIT route. Los Angeles being the hub of business, international trade, entertainment, culture, media, fashion, science, technology, and education in the US, the demand is much higher. This is the reason for a large number of operators scheduling multiples flights on this route. Because of the high demand, the airfare is usually higher and there is a significant movement in the prices compared to other routes. The flight distance of NYC – PIT route is higher by 161 Km than the NYC – LA route, but the fares are much less than the latter.
Similar kind of argument can be extended for NYC – CHI route. The flight distance of the route is 1,149 Kms, much less than the distance of 4,105 Kms for the NYC – PIT route. However, the prices on both the routes are very similar. There is more number of players offering non-stop flights on NYC – CHI route than the NYC – PIT route. This implies that the pricing on US domestic routes is governed by the demand and the number of flights to compete on the route.
As can be observed from the charts below, higher volatility in price movements is seen 12-14 days prior to the date of travel. This volatility increases during the last 4-5 days depending on the availability of seats. Companies and travel sites charge very high prices for the last few seats.
Figure 6: Price comparison for three time slots for New York (JFK) – Los Angeles route (Delta Airlines)
Figure 7: Price comparison across three travel websites for New York (JFK) – Los Angeles morning flight (Delta Airlines)
Figure 8: Price movement for New York (JFK) – Los Angeles morning flight across airlines
Comparison between Indian and US domestic Routes
The demand for air travel is always higher in the US domestic routes than on the Indian domestic routes, although the latter is increasing at a fast rate. This demand is primarily generated by business travellers.
The price movements on the US domestic routes are more volatile than the Indian routes. It is our understanding that there is deeper categorization of seats based on sub-classes, compared to Indian airlines. Therefore, there are multiple price bands. The price gap among the bands increases as the booking of seats approaches the date of travel. Higher volatility on US domestic routes can be due to higher number of cancellations, which is quite common in case of business travel. Indian consumers show reluctance for cancellations.
Also, since the demand for business travel is very high in the US, the US airlines charge exorbitant prices if the number of seats left is very low. In Indian context, the consumer is very price conscious. Indian customer always takes into consideration the price and time factors before choosing a mode of transportation. If the prices will be very high, Indian customer will choose other mode of transportation such as train or road transportation. Therefore, Indian airline companies can reap benefits of shorter supply only to an extent.
Low Distance International Route from India
In order to study the price volatility on international routes with respect to the chosen Indian domestic airlines, Mumbai – Singapore, Chennai – Singapore and Bangalore – Singapore routes were chosen. Mumbai-Singapore and Chennai-Singapore routes are mature routes with a good amount of competition on these routes with Air India, Jet Airways, Kingfisher and Singapore Airlines being the main players. The prices on these routes are more or less constant across the 15 and 30 days time frame with a drastic increase in prices during last 4-5 days before the day of departure. The ticket prices for all the players are almost comparable because there is stiff competition. The players price the tickets so as to cover their marginal cost of flying and operations. Towards the last 4-5 days to the date of departure, the increase in price across all airlines is sharper because they intend to take benefit of the last minute booking by price insensitive business passengers.
Singapore Airlines is only airline which has a direct flight on the Bangalore-Singapore route. Due to its monopoly on this route, the prices of tickets are quite high as compared to a similar distance route of Chennai-Singapore across all time frames. The monopoly on this route helps Singapore Airlines to serve the entire demand and charge very high premium.
A regression analysis was done for driving a pricing model for the routes. The parameters considered for the analysis were air fare from different travel websites (including company websites), days to fly, time of flight (i.e. morning, afternoon, or evening slot), flight distance, and type of flight (whether a low cost carrier, or not). The variables ‘time of flight’ and ‘type of flight’ were treated as dummy variables.
The regression output for the heavy traffic routes (Delhi-Mumbai and Bangalore-Kolkata) is given below:
Price, P = 7415.83 – 100.28 (Days to Fly) + 1194.05 (Morning) + 1200.53 (Afternoon) – 2.43 (Distance) + 233.09 (company website) + 36.80 (Yatra.com)
Similarly, a regression model was run for the light traffic (Mumbai-Nagpur and Delhi-Indore) route. The regression output is given below:
Price, P = 7550.48 – 64.9 (Days to Fly) + 664 (Morning) + 0 (Afternoon) -7.25 (Distance) + 649.84 (Flight Type) + 1083.266 (company website) + 1124.26 (Yatra.com)
The two regressive models clearly justify our qualitative observations. The R2 values in range of 0.4 for these two regressions make the regressive models reliable tools to show the relationship between the price and various other independent parameters. The negative co-efficient of the independent variable ‘Days to Fly’ explains that as the days to fly come close by, it has a positive impact on the price of the air ticket and hence, result in increasing it. The positive co-efficient of independent variable ‘Morning’ clearly indicates that the price for morning flights is higher. Distance has negative impact on the price; this may be mostly due the fact that the regression data was not sufficient to show the real trend on this parameter. The positive co-efficient for ‘Flight Type’ indicates that the prices for airlines like Indian, Kingfisher and Jet Airways are more as compared to airlines like Indigo, Spice Jet, etc. It also explains the fact that due to bulk dealing many times the travel websites are able to offer the air tickets at a better price than the airline website in India.
Thus, the regressive models are successful in explaining the relationship between price and independent variables in a statistically significant way.
Impact of Competition on price
As we could not capture the impact of competition in the regressive models, we developed a correlation matrix for this. A high value of correlation of -0.7219 between price and competition clearly indicates that price of air tickets decreases with increase in competition on the route, whereas it increases significantly when there is monopoly kind of situation on the route.
Comparison of price per unit km for low and high traffic routes
Days to fly
Price per Km (RS)
Figure 6: Comparison of price per unit Km for low and high traffic routes
It is observed that price per km on Indian domestic heavy traffic routes is more as compared to Indian domestic light traffic routes. The reason for this is that on low traffic routes people tend to travel more by train and road. So as to encourage the people to switch to air travel, the airlines charge the prices low as compared to more matured air routes.
Days to Fly
Impact of timeslot on the average of prices of air tickets
Air fare (Rs)
Figure 7: Impact of timeslot on the average of prices of air tickets
On heavy traffic Indian domestic routes, there is demand for air tickets at all the times of the day, so the prices do not vary much across various time slots of the day. On the other hand, for low traffic Indian domestic routes, there is more demand for air tickets during the morning and evening slots and less demand during the afternoon slot. Hence, the prices are higher during morning and evening slots of the day, whereas it is less during afternoon slot.
Price Volatility Index (PVI)
The price volatility index has been defined as the percentage change in price of air ticket today as compared to previous recording day. It is given by the formula:
PVI = (Pt-Pt-1)/ Pt-1
where Pt : Today’s air ticket price
Pt-1: Price of air ticket on previous day
It is observed that the price volatility on US domestic routes is more as compared to Indian domestic routes. The main reason for this is attributed to the real time responding air ticket pricing mechanism which responds immediately to change in demand rates in US as compared to Indian pricing systems which updates prices only at the end of the day.
Figure 8: Price Volatility Index (PVI) across India and US domestic routes
Scope for Further Research
It is advised that a larger time frame is taken for data collection. The ideal situation would be to choose the ‘date of journey’ and start recording the price points from the day bookings are opened. Our time frame for data collection was one month, and the bookings usually open before three months from the date of journey.
Another constraint due to shorter time frame is that not all the parameters (such as vacations, weather, etc.) can be included in the study and hence, their impact cannot be gauged.
The Indian Airline Industry in 2008 by Rishikesha.T. Krishnan
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