Airline Pricing- Economy vs Premium Economy

Here, we are trying to analyse what factors cause the difference between economy and premium economy pricing in 6 top airlines- British Airways, Delta Airlines, Air France, Singapore Airlines, Virgin Airlines and Jet Airways.

The following data set has been used in this analysis:

setwd("C:\\Users\\Tejajay\\Desktop\\Internship\\3. Data Analytics")
SixAirlines <- read.csv(paste("SixAirlinesData.csv", sep=""))

On average, an airline has 236 seats, out of which around 203 are economy-class seats, and 33 seats are classified as premium-economy. That is, around 14.6% seats are premium-economy.

summary(SixAirlines$SeatsTotal)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      98     166     227     236     279     441

summary(SixAirlines$SeatsEconomy)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    78.0   133.0   185.0   202.3   243.0   389.0

summary(SixAirlines$SeatsPremium)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    8.00   21.00   36.00   33.65   40.00   66.00

mean(SixAirlines$PercentPremiumSeats)

## [1] 14.64541

The average price of an economy seat is USD 1327, while the average price of a premium-economy seat is USD 1845, i.e premium-economy seats are pricier by almost 49%.

summary(SixAirlines$PriceEconomy)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      65     413    1242    1327    1909    3593

summary(SixAirlines$PricePremium)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    86.0   528.8  1737.0  1845.3  2989.0  7414.0

mean(SixAirlines$PriceRelative)*100

## [1] 48.72052

This difference in price can be attributes to enhanced features in premium-economy seats, viz. greater legroom (a.k.a pitch) and wider seats. On average, we see that premium-economy seats have 6.6 inches more legroom than economy seats, and are wider by around 1.6 inches.

mean(SixAirlines$PitchDifference)

## [1] 6.687773

mean(SixAirlines$WidthDifference)

## [1] 1.633188

There are other, more indirect factors that influence this difference in pricing. Some of them are: flight duration, season / month of travel, whether the flight is an international or domestic one, and the make of the aircraft (Boeing or Airbus).

summary(SixAirlines$FlightDuration)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.250   4.260   7.790   7.578  10.620  14.660

summary(SixAirlines$TravelMonth)

## Aug Jul Oct Sep 
## 127  75 127 129

summary(SixAirlines$Aircraft)

## AirBus Boeing 
##    151    307

summary(SixAirlines$IsInternational)

##      Domestic International 
##            40           418

Trends in Pricing

We will analyse trends for one sample airline, say, British Airlines, to get a decent picture of how pricing changes with respect to different factors.

SixAirlines1 <- SixAirlines[ which(SixAirlines$Airline=='British'), ]

We take a look at the distribution of price difference with regards to difference in legroom and seat width.

pricevspitch <- table(SixAirlines$PriceRelative, SixAirlines$PitchDifference)
barplot(pricevspitch, main = "Price Difference With Respect to Legroom Difference", xlab = "Legroom", ylab = "Price Difference")

pricevswidth <- table(SixAirlines$PriceRelative, SixAirlines$WidthDifference)
barplot(pricevswidth, main = "Price Difference with Respect to Width Difference", xlab = "Width", ylab = "Price Difference")

The following charts describe the changes in pricing based on the duration of the flight (separate for domestic and international flights).

domestic <- SixAirlines[ which(SixAirlines$IsInternational=='Domestic'), ]
pricevsduration <- table(domestic$PriceRelative, domestic$FlightDuration)
barplot(pricevsduration, main = "Price Difference w.r.t Duration- Domestic Flights", xlab = "Duration", ylab = "Price Difference")

international <- SixAirlines[ which(SixAirlines$IsInternational=='International'), ]
pricevsduration2 <- table(international$PriceRelative, international$FlightDuration)
barplot(pricevsduration2, main = "Price Difference w.r.t Duration- International Flights", xlab = "Duration", ylab = "Price Difference")

This plot describes how prices change based on the month of travel (we take a sample for British Airways alone):

pricevsmonth <- table(SixAirlines1$PriceRelative, SixAirlines1$TravelMonth)
barplot(pricevsmonth, main = "Price Difference w.r.t Month of Travel", xlab = "Month", ylab = "Price Difference")

This plot describes how prices change based on the aircraft build (again, this is a sample for British Airways only):

pricevsaircraft <- table(SixAirlines1$PriceRelative, SixAirlines1$Aircraft)
barplot(pricevsaircraft, main = "Price Difference w.r.t Aircraft Build", xlab = "Type of Aircraft", ylab = "Price Difference")