airlines_marketing_analysis.knit

Checking the Data:

Importing necessary libraries:

library(readr)
library(skimr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(knitr)
library(ggplot2)
library(tidyr)

Reading the data file:

df <- read.csv("Data/airline.csv", header = TRUE)

Understanding the Data:

I did this step to get the name of the columns, so that later when I use the head() function the text won’t be long, because of the reviews from customers were long and it’s not appealing to the eye.

colnames(df)

##  [1] "Airline_Name"               "Review_Date"               
##  [3] "Rating"                     "Comment"                   
##  [5] "Reviewer_Name"              "Reviewer_Country"          
##  [7] "Verified"                   "Seat_Rating"               
##  [9] "Cabin_staff_service_Rating" "Food_Beverages_Rating"     
## [11] "Inflight_Rating"            "Ground_service_Rating"     
## [13] "Wifi_connectivity_Rating"   "Value_Rating"              
## [15] "Full_review"

And here I coppied them and didn’t use the Comment column.

head(df[, c("Airline_Name", "Review_Date", "Rating", "Reviewer_Name", "Reviewer_Country", "Verified", "Seat_Rating", "Cabin_staff_service_Rating", "Food_Beverages_Rating", "Inflight_Rating", "Ground_service_Rating", "Wifi_connectivity_Rating", "Value_Rating")])

##    Airline_Name Review_Date Rating  Reviewer_Name Reviewer_Country Verified
## 1   AB Aviation  2019-11-11      9     Tom Hansen      Netherlands      Yes
## 2   AB Aviation  2019-06-25      1  Gyan Fernando   United Kingdom      Yes
## 3   AB Aviation  2019-06-25      1  Gyan Fernando   United Kingdom      Yes
## 4 Adria Airways  2019-09-28      1 D Praetextatus           Serbia       No
## 5 Adria Airways  2019-09-24      1       D Meijer      Netherlands      Yes
## 6 Adria Airways  2019-09-17      1   Herbse Mayer          Austria      Yes
##   Seat_Rating Cabin_staff_service_Rating Food_Beverages_Rating Inflight_Rating
## 1           4                          5                     4              NA
## 2           2                          2                     1              NA
## 3           2                          1                     1              NA
## 4           1                          1                    NA              NA
## 5           1                          1                     1               1
## 6           1                          1                     1               1
##   Ground_service_Rating Wifi_connectivity_Rating Value_Rating
## 1                     4                       NA            3
## 2                     1                       NA            2
## 3                     1                       NA            2
## 4                     1                       NA            1
## 5                     1                        1            1
## 6                     1                        1            1

dim(df)

## [1] 139128     15

We have a total of 139128 rows and 15 columns

str(df[, c("Airline_Name", "Review_Date", "Rating", "Reviewer_Name", "Reviewer_Country", "Verified", "Seat_Rating", "Cabin_staff_service_Rating", "Food_Beverages_Rating", "Inflight_Rating", "Ground_service_Rating", "Wifi_connectivity_Rating", "Value_Rating")])

## 'data.frame':    139128 obs. of  13 variables:
##  $ Airline_Name              : chr  "AB Aviation" "AB Aviation" "AB Aviation" "Adria Airways" ...
##  $ Review_Date               : chr  "2019-11-11" "2019-06-25" "2019-06-25" "2019-09-28" ...
##  $ Rating                    : int  9 1 1 1 1 1 1 1 1 8 ...
##  $ Reviewer_Name             : chr  "Tom Hansen" "Gyan Fernando" "Gyan Fernando" "D Praetextatus" ...
##  $ Reviewer_Country          : chr  "Netherlands" "United Kingdom" "United Kingdom" "Serbia" ...
##  $ Verified                  : chr  "Yes" "Yes" "Yes" "No" ...
##  $ Seat_Rating               : num  4 2 2 1 1 1 1 1 1 4 ...
##  $ Cabin_staff_service_Rating: num  5 2 1 1 1 1 1 1 2 4 ...
##  $ Food_Beverages_Rating     : num  4 1 1 NA 1 1 1 1 1 3 ...
##  $ Inflight_Rating           : num  NA NA NA NA 1 1 NA 1 1 NA ...
##  $ Ground_service_Rating     : num  4 1 1 1 1 1 1 1 1 5 ...
##  $ Wifi_connectivity_Rating  : num  NA NA NA NA 1 1 NA 1 1 NA ...
##  $ Value_Rating              : num  3 2 2 1 1 1 1 1 1 5 ...

summary(df)

##  Airline_Name       Review_Date            Rating         Comment         
##  Length:139128      Length:139128      Min.   : 1.000   Length:139128     
##  Class :character   Class :character   1st Qu.: 1.000   Class :character  
##  Mode  :character   Mode  :character   Median : 3.000   Mode  :character  
##                                        Mean   : 4.452                     
##                                        3rd Qu.: 8.000                     
##                                        Max.   :10.000                     
##                                        NA's   :4338                       
##  Reviewer_Name      Reviewer_Country     Verified          Seat_Rating   
##  Length:139128      Length:139128      Length:139128      Min.   :0.000  
##  Class :character   Class :character   Class :character   1st Qu.:1.000  
##  Mode  :character   Mode  :character   Mode  :character   Median :3.000  
##                                                           Mean   :2.729  
##                                                           3rd Qu.:4.000  
##                                                           Max.   :5.000  
##                                                           NA's   :14402  
##  Cabin_staff_service_Rating Food_Beverages_Rating Inflight_Rating
##  Min.   :0.000              Min.   :0.000         Min.   :0.000  
##  1st Qu.:1.000              1st Qu.:1.000         1st Qu.:1.000  
##  Median :3.000              Median :3.000         Median :2.000  
##  Mean   :2.986              Mean   :2.666         Mean   :2.501  
##  3rd Qu.:5.000              3rd Qu.:4.000         3rd Qu.:4.000  
##  Max.   :5.000              Max.   :5.000         Max.   :5.000  
##  NA's   :14805              NA's   :37006         NA's   :53970  
##  Ground_service_Rating Wifi_connectivity_Rating  Value_Rating  
##  Min.   :1.000         Min.   :0.000            Min.   :0.000  
##  1st Qu.:1.000         1st Qu.:1.000            1st Qu.:1.000  
##  Median :2.000         Median :1.000            Median :2.000  
##  Mean   :2.401         Mean   :1.969            Mean   :2.631  
##  3rd Qu.:4.000         3rd Qu.:3.000            3rd Qu.:4.000  
##  Max.   :5.000         Max.   :5.000            Max.   :5.000  
##  NA's   :41954         NA's   :105344           NA's   :1816   
##  Full_review       
##  Length:139128     
##  Class :character  
##  Mode  :character  
##                    
##                    
##                    
##

Dealing with N/A values

Before we start with any manipulation, I want to mention that there are cases where flights got cancelled (either by the flight carrier or the clinets themselves); so this makes our cleaning not so accurate only if we apply some sentiment analysis.

Deleting old records:

Deleting old records make so much sense because in the old times, people didn’t have phones first, second it was a hassle to fill a survey as the only way would be in the airport itself by filling it in a paper form and handling it to some human which would fill the dataset and probably fill it with mistakes.

So for this I guess starting 10 years before the last record makes sense.

df$Review_Date <- as.Date(df$Review_Date, format = "%Y-%m-%d")

df <- df[format(df$Review_Date, "%Y") >= "2014", ]

Counting the amount of N/A values:

colSums(is.na(df))

##               Airline_Name                Review_Date 
##                          0                          0 
##                     Rating                    Comment 
##                       1626                          0 
##              Reviewer_Name           Reviewer_Country 
##                          0                          0 
##                   Verified                Seat_Rating 
##                          0                       6994 
## Cabin_staff_service_Rating      Food_Beverages_Rating 
##                       7386                      29538 
##            Inflight_Rating      Ground_service_Rating 
##                      45719                      22538 
##   Wifi_connectivity_Rating               Value_Rating 
##                      85784                         14 
##                Full_review 
##                          0

df_complete <- df[complete.cases(df), ]
percentage_of_full <- ( nrow(df_complete) / nrow(df) ) * 100
print(percentage_of_full)

## [1] 25.45999

We can see that we have out 139128 rows, only 30442 are full (ie, no NA values in these rows), which is equal to 25.45% of all full.

total_cells <- prod(dim(df))
total_missing <- sum(is.na(df))

percent_missing <- (total_missing / total_cells) * 100
print(percent_missing)

## [1] 11.1289

We can see that out of all the cells that we have; 11.12% are NA cells.

Ok, after taking a look at the dataset and the amount on N/A values, we get to see that some make sense that they are N/A; which are the Food_Beverages_Rating and Wifi_connectivity_Rating; it’s because not all flights provide food and beverages and not all of then provide Wifi, mostly in budget friendly airlines. The other ones are N/A because when answering the survey the clients did not answer because they didn’t feel like it and none of the fields were mandatory to be filled.

Deleting rows with all columns as N/A:

I am going to be deleting rows with ≤ 1 columns (out of 7) because with ≥ 1 N/As per row we can’t fix the data and replace the N/A because we wouldn’t understand the data then.

I did not consider the columns Food_Beverages_Rating and Wifi_connectivity_Rating because they are a different case. So I created a list of the columns that need to be checked, counted the amount of na values in each row, and kept all the rows with at least 1 columns with a value.

cols_to_check <- c("Seat_Rating", "Cabin_staff_service_Rating","Inflight_Rating", "Ground_service_Rating", "Value_Rating")

na_count_subset <- rowSums(is.na(df[, cols_to_check]))

df_filtered <- df[na_count_subset >= 1, ]

I got a total of 63536 rows now, which is not the worst.

colSums(is.na(df_filtered))

##               Airline_Name                Review_Date 
##                          0                          0 
##                     Rating                    Comment 
##                       1625                          0 
##              Reviewer_Name           Reviewer_Country 
##                          0                          0 
##                   Verified                Seat_Rating 
##                          0                       6994 
## Cabin_staff_service_Rating      Food_Beverages_Rating 
##                       7386                      27442 
##            Inflight_Rating      Ground_service_Rating 
##                      45719                      22538 
##   Wifi_connectivity_Rating               Value_Rating 
##                      61284                         14 
##                Full_review 
##                          0

df_complete_filtered <- df_filtered[complete.cases(df), ]
percentage_of_full_filtered <- ( nrow(df_complete_filtered) / nrow(df_filtered) ) * 100
print(percentage_of_full_filtered)

## [1] 47.91299

Now it’s a bit better by reaching 47.91% of full nto containing any N/A values rows.

total_cells_filtered <- prod(dim(df_filtered))
total_missing_filtered <- sum(is.na(df_filtered))

percent_missing_filtered <- (total_missing_filtered / total_cells_filtered) * 100
print(percent_missing_filtered)

## [1] 18.15265

And now we have more N/A in comparison to the whole set with 18.15% but it makes sense becasue we reduced the size of the set by almost half.

Replacing and Filling N/As:

Now, I have to make sense when doing this and set a rule for this:

If I have only 1 value filled in the row:
- then we can copy the closest row to it with the same values knowing it has the same Rating (Because this column makes the most of sense and represents the full rating of the client (by rule) or at least almost).
- If the Rating is also N/A in that row than we can copy the reviews of another row with the same airlines.
If we have 2 or more filled:
- Then we can fill the rest based on one, because some are related to each other in a sense.
  - for example: Inflight_Rating and Cabin_staff_service_Rating.
  - for Food_Beverages_Rating and Wifi_connectivity_Rating we can just fill it up with the equation so that the score stays the same for the Rating _ If the Rating is also N/A then we fill with the average of the whole other columns except the Value_Rating

rating_vars <- c("Seat_Rating","Cabin_staff_service_Rating","Food_Beverages_Rating",
                 "Inflight_Rating","Ground_service_Rating","Wifi_connectivity_Rating","Value_Rating")

df_cleaned <- df_filtered

col_means_fill <- sapply(df_cleaned[, c("Food_Beverages_Rating","Wifi_connectivity_Rating","Ground_service_Rating")], mean, na.rm = TRUE)

for(i in 1:nrow(df_cleaned)) {
  
  rating_scaled <- ifelse(!is.na(df_cleaned$Rating[i]), df_cleaned$Rating[i] / 2, NA)
  row_vals <- df_cleaned[i, rating_vars]
  na_cols <- names(row_vals)[is.na(row_vals)]
  filled_cols <- names(row_vals)[!is.na(row_vals)]
  filled_count <- length(filled_cols)
  
  if(i > 1){
    prev_row <- df_cleaned[i-1, rating_vars]
  } else{
    prev_row <- df_cleaned[i, rating_vars]  # first row uses itself
  }

  if(filled_count == 1){
    df_cleaned[i, rating_vars] <- prev_row
  }
  # Ground Rating
  else if(filled_count >= 2){ 
    if("Ground_service_Rating" %in% na_cols) {
      df_cleaned$Ground_service_Rating[i] <- prev_row$Ground_service_Rating
      na_cols <- setdiff(na_cols, "Ground_service_Rating")
      filled_cols <- c(filled_cols, "Ground_service_Rating")
    }
    
   # Inflight and cabin staff:
    if(is.na(df_cleaned$Inflight_Rating[i]) & !is.na(df_cleaned$Cabin_staff_service_Rating[i])){
      df_cleaned$Inflight_Rating[i] <- df_cleaned$Cabin_staff_service_Rating[i]
    }
    if(is.na(df_cleaned$Cabin_staff_service_Rating[i]) & !is.na(df_cleaned$Inflight_Rating[i])){
      df_cleaned$Cabin_staff_service_Rating[i] <- df_cleaned$Inflight_Rating[i]
    }
    
    # Wifi, Food, Ground with mean:
    for(col in c("Food_Beverages_Rating", "Wifi_connectivity_Rating", "Ground_service_Rating")){
      if(col %in% na_cols) {
        df_cleaned[i, col] <- round(col_means_fill[col])
        na_cols <- setdiff(na_cols, col)
        filled_cols <- c(filled_cols, col)
      }
    }
    
    # The avrage matching:
    if(!is.na(rating_scaled)) {
      current_sum <- sum(as.numeric(df_cleaned[i, rating_vars][filled_cols]), na.rm=TRUE)
      current_n <- length(filled_cols)
      total_n <- length(rating_vars)
      
      missing_n <- total_n - current_n
      if(missing_n > 0) {
        target_sum <- rating_scaled * total_n
        fill_value <- (target_sum - current_sum) / missing_n
        fill_value <- max(fill_value, 1)
        for(col in na_cols){
          df_cleaned[i, col] <- fill_value
        }
      }
    }
  }
  
  # Rating
  if(is.na(df_cleaned$Rating[i]) & i > 1) {
    df_cleaned$Rating[i] <- df_cleaned$Rating[i-1]
  }
}

for (col in rating_vars) {
  med <- median(df_cleaned[[col]], na.rm = TRUE)
  df_cleaned[[col]][is.na(df_cleaned[[col]])] <- med
}

# Final cleanup:
df_cleaned <- df_cleaned %>%
  mutate(across(all_of(rating_vars), ~ pmax(pmin(round(as.numeric(.x)), 5), 0)))

summary(df_cleaned)

##  Airline_Name        Review_Date             Rating       Comment         
##  Length:63536       Min.   :2014-01-02   Min.   : 1.0   Length:63536      
##  Class :character   1st Qu.:2015-03-04   1st Qu.: 1.0   Class :character  
##  Mode  :character   Median :2018-01-25   Median : 2.0   Mode  :character  
##                     Mean   :2018-03-28   Mean   : 4.1                     
##                     3rd Qu.:2020-09-28   3rd Qu.: 8.0                     
##                     Max.   :2024-03-23   Max.   :10.0                     
##  Reviewer_Name      Reviewer_Country     Verified          Seat_Rating   
##  Length:63536       Length:63536       Length:63536       Min.   :0.000  
##  Class :character   Class :character   Class :character   1st Qu.:1.000  
##  Mode  :character   Mode  :character   Mode  :character   Median :2.000  
##                                                           Mean   :2.514  
##                                                           3rd Qu.:4.000  
##                                                           Max.   :5.000  
##  Cabin_staff_service_Rating Food_Beverages_Rating Inflight_Rating
##  Min.   :0.000              Min.   :0.000         Min.   :0.000  
##  1st Qu.:1.000              1st Qu.:2.000         1st Qu.:1.000  
##  Median :3.000              Median :3.000         Median :1.000  
##  Mean   :2.767              Mean   :2.834         Mean   :2.176  
##  3rd Qu.:4.000              3rd Qu.:3.000         3rd Qu.:4.000  
##  Max.   :5.000              Max.   :5.000         Max.   :5.000  
##  Ground_service_Rating Wifi_connectivity_Rating  Value_Rating  
##  Min.   :1.000         Min.   :1.000            Min.   :0.000  
##  1st Qu.:1.000         1st Qu.:2.000            1st Qu.:1.000  
##  Median :1.000         Median :2.000            Median :2.000  
##  Mean   :2.247         Mean   :2.008            Mean   :2.508  
##  3rd Qu.:4.000         3rd Qu.:2.000            3rd Qu.:4.000  
##  Max.   :5.000         Max.   :5.000            Max.   :5.000  
##  Full_review       
##  Length:63536      
##  Class :character  
##  Mode  :character  
##                    
##                    
##

Checking again the N/A values:

colSums(is.na(df_cleaned))

##               Airline_Name                Review_Date 
##                          0                          0 
##                     Rating                    Comment 
##                          0                          0 
##              Reviewer_Name           Reviewer_Country 
##                          0                          0 
##                   Verified                Seat_Rating 
##                          0                          0 
## Cabin_staff_service_Rating      Food_Beverages_Rating 
##                          0                          0 
##            Inflight_Rating      Ground_service_Rating 
##                          0                          0 
##   Wifi_connectivity_Rating               Value_Rating 
##                          0                          0 
##                Full_review 
##                          0

df_complete_cleaned <- df_cleaned[complete.cases(df), ]
percentage_of_full_cleaned <- ( nrow(df_complete_cleaned) / nrow(df_cleaned) ) * 100
print(percentage_of_full_cleaned)

## [1] 47.91299

total_cells_cleaned <- prod(dim(df_cleaned))
total_missing_cleaned <- sum(is.na(df_cleaned))

percent_missing_cleaned <- (total_missing_cleaned / total_cells_cleaned) * 100
print(percent_missing_cleaned)

## [1] 0

Everything 0 so now no N/A and now after that the data is all clean and makes sense, I can proceed with the marketing analytics.

Marketing Analytics :

Overall satisfaction index CSI:

sat_items <- c("Seat_Rating","Cabin_staff_service_Rating","Food_Beverages_Rating",
               "Inflight_Rating","Ground_service_Rating","Wifi_connectivity_Rating",
               "Value_Rating")

df_cleaned$CSI <- rowMeans(df_cleaned[, sat_items], na.rm = TRUE)
df_cleaned$CSI_Percentage <- df_cleaned$CSI * 20

CSI_overall_percent <- mean(df_cleaned$CSI_Percentage, na.rm = TRUE)
CSI_overall_percent

## [1] 48.72824

A CSI of 48% means that, on average, customers rate their overall experience just below the midpoint of your 0–100% scale (which corresponds to about 2.4 on a 0–5 scale).

It signals moderate to low satisfaction: customers are not completely unhappy, but they are far from being genuinely satisfied or enthusiastic. There is likely high variability between airlines and touchpoints (onboard vs ground vs wifi), so some areas or carriers perform much better than others.

but this can be seen also as a room for improvement as it’s so close the midpoint.

Sub-indices:

Onboard vs ground vs digital:

df_cleaned$Onboard_Index <- rowMeans(df_cleaned[, c("Seat_Rating","Cabin_staff_service_Rating",
                                                    "Food_Beverages_Rating","Inflight_Rating")], na.rm = TRUE)
df_cleaned$Onboard_Percentage <- df_cleaned$Onboard_Index * 20

df_cleaned$Ground_Index <- rowMeans(df_cleaned[, c("Ground_service_Rating","Wifi_connectivity_Rating")], na.rm = TRUE)
df_cleaned$Ground_Percentage <- df_cleaned$Ground_Index * 20

This code snippet is to show data in a better table.

agg <- aggregate(cbind(CSI_Percentage, Onboard_Percentage, Ground_Percentage) ~ Airline_Name,
          data = df_cleaned,
          FUN = mean,
          na.rm = TRUE)

kable(agg, digits = 2)

Airline_Name	CSI_Percentage	Onboard_Percentage	Ground_Percentage
AB Aviation	45.71	48.33	40.00
Adria Airways	61.76	63.09	57.65
Aegean Airlines	61.96	65.65	53.18
Aer Lingus	55.19	57.08	50.46
Aero VIP	73.33	78.33	63.33
Aeroflot Russian Airlines	56.84	58.98	49.17
AeroItalia	28.57	30.00	30.00
Aerolineas Argentinas	50.23	54.70	37.16
Aeromar	43.43	49.00	40.00
Aeromexico	48.06	52.02	41.03
Africa World Airlines	44.76	50.83	33.33
Aigle Azur	44.76	45.00	41.11
Air Algerie	42.42	42.69	42.31
Air Antilles	47.62	50.00	43.33
Air Arabia	39.90	42.13	36.21
Air Astana	68.49	74.52	54.25
Air Austral	34.29	37.14	32.86
Air Bagan	62.86	72.50	30.00
Air Berlin	55.52	55.49	54.82
Air Botswana	36.33	33.57	47.14
Air Burkina	61.43	67.50	45.00
Air Busan	69.43	71.00	62.00
Air Cairo	50.57	51.50	46.00
Air Canada	41.82	46.23	33.57
Air Canada rouge	37.77	37.10	40.84
Air Caraibes	33.06	37.86	30.00
Air China	52.68	53.25	49.87
Air Corsica	73.06	74.29	64.29
Air Costa	80.00	85.00	60.00
Air Djibouti	57.14	55.00	60.00
Air Dolomiti	57.14	62.29	45.71
Air Europa	41.57	44.64	34.89
Air France	56.78	57.57	55.66
Air Greenland	28.57	30.00	30.00
Air Iceland Connect	85.71	95.00	65.00
Air India	54.78	58.72	44.29
Air India Express	43.90	43.41	42.86
Air Italy	49.45	51.54	44.62
Air Juan	32.67	35.22	32.17
Air KBZ	74.92	78.89	61.11
Air Koryo	65.55	69.71	54.12
Air Macau	42.29	41.00	40.00
Air Madagascar	47.81	51.33	40.00
Air Malta	42.48	44.28	37.89
Air Mauritius	50.60	54.08	42.63
Air Mediterranee	57.14	60.00	40.00
Air Moldova	45.84	47.17	41.30
Air Namibia	55.54	58.80	49.60
Air New Zealand	56.59	59.39	51.95
Air Niugini	30.11	30.00	35.38
Air North Yukon’s Airline	81.43	88.75	65.00
Air Nostrum	59.57	61.50	52.00
Air Panama	46.94	48.21	43.57
Air Pegasus	50.00	51.25	45.00
Air Rarotonga	89.52	98.33	70.00
Air Serbia	58.67	61.60	51.05
Air Seychelles	56.29	57.00	54.00
Air Tahiti Nui	50.65	49.70	53.94
Air Tanzania	42.38	47.50	35.56
Air Transat	48.16	54.31	33.26
Air Vanuatu	43.47	47.14	36.07
Air Zimbabwe	44.29	47.50	35.00
AirAsia	52.53	52.73	49.07
AirAsia India	52.71	56.20	45.70
AirAsia Philippines	41.63	43.61	36.58
AirAsia X	59.06	61.48	46.15
AirAsia Zest	52.50	52.50	45.00
airBaltic	52.39	56.72	43.50
airblue	51.60	52.06	48.82
Aircalin	42.60	39.55	46.36
AirConnect	14.29	0.00	50.00
Airlink	63.49	69.50	52.00
Airnorth	46.31	45.00	50.83
AirSWIFT	44.29	45.00	43.33
Akasa Air	53.39	55.62	48.43
Alaska Airlines	50.40	52.51	47.48
Alitalia	52.49	52.57	51.13
Allegiant Air	39.48	40.68	36.13
Alliance Air	36.68	37.84	35.68
Alliance Airlines	42.86	45.00	40.00
Amaszonas	45.93	48.85	40.77
American Airlines	38.05	41.00	34.24
American Eagle	50.71	51.12	50.53
ANA All Nippon Airways	71.94	76.05	60.46
AnadoluJet	51.60	52.06	46.47
Andes Líneas Aéreas	23.57	22.50	30.00
Arajet	72.86	82.50	50.00
Ariana Afghan Airlines	60.00	55.00	50.00
Arik Air	37.78	40.00	33.33
Arkefly	17.50	11.88	30.00
Arkia Israeli Airlines	38.80	37.89	42.63
Armenia Air Company	28.57	30.00	30.00
Asiana Airlines	68.78	71.34	61.03
ASKY Airlines	36.19	38.75	35.00
ATA Airlines	30.00	25.00	35.00
Atlantic Airways	85.71	95.00	65.00
Atlasglobal	52.93	57.62	41.43
Auric Air	80.00	80.00	70.00
Aurigny Air	56.55	60.86	50.34
Austrian Airlines	53.33	56.82	46.36
Avelo Airlines	47.93	50.98	42.50
Avianca	42.77	44.06	42.35
Avianca Brazil	55.56	63.33	37.78
Avianca Ecuador	34.03	36.82	34.55
Avior Airlines	42.86	40.00	40.00
Azerbaijan Airlines	51.87	53.85	47.69
Azul Airlines	36.11	36.60	35.60
Azur Air Germany	28.57	30.00	30.00
BA CityFlyer	78.44	85.68	61.59
Badr Airlines	74.29	90.00	40.00
Bahamasair	40.26	44.70	37.58
Bamboo Airways	56.34	61.20	46.40
Bangkok Airways	69.63	73.06	59.61
Bassaka Air	78.10	83.89	62.22
Batik Air	48.52	50.44	43.33
Beijing Capital Airlines	44.29	47.50	30.00
Belavia	61.04	64.77	48.64
BH Air	52.00	51.00	50.00
Bhutan Airlines	61.14	69.00	36.00
Biman Bangladesh Airlines	65.45	71.67	46.67
Binter Canarias	73.65	76.11	62.22
Blu-express	28.57	30.00	30.00
Blue Air	46.04	48.54	41.67
Blue Islands	40.43	45.25	38.00
Blue Panorama Airlines	33.66	35.87	33.04
Blue1	22.86	20.00	30.00
Bluebird Airways	43.43	45.33	41.33
bmi Regional	48.78	53.93	41.43
Boliviana de Aviacion	56.95	59.00	44.67
BoraJet	40.00	50.00	30.00
Boutique Air	43.04	46.41	38.75
Breeze Airways	49.25	52.84	41.96
British Airways	55.04	56.81	51.43
Brussels Airlines	46.23	48.75	41.50
Buddha Air	47.38	49.17	44.17
Bulgaria Air	45.38	45.50	42.67
Buta Airways	47.50	49.38	43.75
Camair-Co	30.00	27.50	30.00
Cambodia Airways	68.57	70.00	60.00
Cambodia Angkor Air	44.35	44.92	42.92
Cambodia Bayon Airlines	54.29	50.00	50.00
Canadian North	67.62	75.00	53.33
Canaryfly	68.57	70.83	55.00
Cape Air	33.37	33.57	36.43
Caribbean Airlines	49.14	54.38	39.00
Cathay Dragon	72.00	77.00	58.00
Cathay Pacific Airways	59.24	70.48	31.51
Cayman Airways	43.12	45.00	40.91
Cebu Pacific	49.28	50.55	43.04
CemAir	57.71	61.00	48.00
Central Mountain Air	45.14	48.00	46.00
China Airlines	63.64	62.34	64.53
China Eastern Airlines	55.87	55.42	53.27
China Southern Airlines	73.55	75.63	65.11
China United Airlines	40.00	40.00	35.00
Citilink	63.14	66.33	52.00
CityJet	52.51	58.02	38.97
Coastal Aviation	71.43	72.50	60.00
Cobalt	63.88	66.43	53.57
Comair	53.25	57.64	42.50
Condor Airlines	41.87	45.88	32.69
Contour Airlines	28.57	30.00	30.00
Copa Airlines	43.64	47.64	37.54
Corendon Airlines	43.71	45.81	39.19
Corsair	31.43	35.00	30.00
Croatia Airlines	49.02	51.84	41.32
CSA Czech Airlines	44.08	45.00	42.60
Cubana Airlines	39.73	41.55	33.81
Cyprus Airways	46.35	46.67	36.67
Danish Air Transport	57.14	60.00	50.00
Darwin Airline	50.89	58.44	34.38
Delta Air Lines	49.28	50.74	48.65
Dimonim Air	71.43	65.00	70.00
Discover Airlines	28.57	30.00	30.00
Dragonair	67.92	70.42	59.58
Druk Air	76.07	82.50	63.75
Dynamic International Airways	37.14	40.00	40.00
Eastar Jet	53.19	53.46	47.69
Eastern Airlines	29.52	31.67	30.00
Eastern Airways	49.63	51.67	49.63
Easyfly	59.59	60.71	52.86
easyJet	53.74	57.99	41.89
Edelweiss Air	59.71	68.17	40.33
Egyptair	44.86	49.57	33.46
El Al Israel Airlines	46.01	51.64	33.73
Ellinair	73.77	81.36	56.36
Emirates	56.02	65.92	31.93
Ernest Airlines	42.86	45.00	40.00
Estonian Air	71.43	75.71	58.57
Ethiopian Airlines	55.54	54.64	56.39
Etihad Airways	53.84	52.33	58.40
euroAtlantic Airways	29.52	30.00	33.33
Europe Airpost	65.71	75.00	30.00
Eurowings	44.04	46.63	39.26
EVA Air	71.06	73.04	64.26
EWA Air	34.29	40.00	30.00
Far Eastern Air Transport	41.43	35.00	45.00
fastjet	55.03	58.70	47.04
Felix Airways	80.00	80.00	70.00
Fiji Airways	53.47	59.73	37.43
Finnair	51.66	56.42	39.76
Firefly	60.06	63.75	50.62
Flair Airlines	43.44	45.91	39.71
Fly540.com	38.10	37.92	37.50
flyadeal	35.97	37.06	34.12
FlyArystan	74.29	80.00	56.67
flybe	62.14	65.00	55.00
flybondi	31.43	25.00	30.00
flydubai	45.77	48.56	40.38
Flynas	32.78	33.42	34.21
FLYONE	38.39	39.38	40.62
FlySafair	53.12	55.70	47.10
FMI Air	84.29	90.00	70.00
Freebird Airlines	40.87	40.15	41.21
French Bee	33.86	36.30	32.59
Frontier Airlines	34.43	35.94	33.57
Fuji Dream Airlines	88.57	95.00	70.00
Garuda Indonesia	78.81	84.98	62.77
Georgian Airways	42.10	44.83	38.00
Germania Airline	37.14	39.29	34.29
Germanwings	48.18	50.26	36.03
Go First	37.47	39.50	35.95
GoAir	38.64	40.64	37.07
Gol Transportes Aéreos	54.11	55.15	47.58
Golden Myanmar Airlines	80.95	86.67	63.33
Grand Cru Airlines	91.43	100.00	70.00
Greater Bay Airlines	40.95	43.33	36.67
Gulf Air	45.32	51.08	33.54
Hainan Airlines	67.58	71.59	56.36
Hawaiian Airlines	46.27	50.58	36.75
Helvetic Airways	67.90	73.82	54.12
Hi Fly	64.76	70.83	53.33
Himalaya Airlines	32.38	33.33	30.00
HiSky	58.37	60.00	52.86
HK Express	49.78	49.67	46.67
Hong Kong Airlines	53.29	55.58	45.12
HOP!	61.67	68.33	49.17
Horizon Air	70.95	75.83	58.33
Hunnu Air	82.86	95.00	60.00
Iberia	51.04	51.49	49.12
Iberia Express	43.59	44.61	41.57
Icelandair	49.18	53.64	36.39
IndiGo	59.66	61.54	52.43
Insel Air	31.17	33.11	30.89
Intercaribbean Airways	41.22	47.14	35.71
Interjet	37.43	40.18	34.75
InterSky	64.29	67.50	50.00
Iran Air	69.80	75.71	52.86
Iran Aseman Airlines	52.38	60.00	30.00
Island Air	42.08	36.82	50.91
Israir Airlines	36.67	35.00	43.33
ITA Airways	38.40	40.54	36.67
Japan Airlines	69.40	71.51	65.00
Jazeera Airways	36.24	38.16	35.26
Jazz	75.24	78.33	70.00
JC International Airlines	74.29	80.00	70.00
Jeju Air	49.62	51.58	44.21
Jet Airways	51.01	54.35	41.47
Jet2.com	65.96	68.02	58.73
Jetairfly	56.19	55.00	58.33
Jetblue Airways	39.98	43.33	35.83
JetSmart	42.60	43.18	42.73
Jetstar Airways	43.34	45.94	36.92
Jetstar Asia	47.39	47.90	42.29
Jetstar Japan	38.07	39.73	35.14
Jetstar Pacific	40.59	39.48	42.76
JetsuiteX	91.43	100.00	70.00
Jin Air	69.18	72.14	56.43
Joon	35.71	35.00	45.00
Jordan Aviation	88.57	95.00	70.00
Juneyao Air	53.43	54.25	50.00
Kan Air	61.43	67.50	50.00
KD Air	28.57	30.00	30.00
Kenya Airways	51.59	57.92	37.04
Kish Airlines	68.57	65.00	70.00
KLM Royal Dutch Airlines	57.21	63.74	42.45
Korean Air	67.52	73.92	50.50
Kulula	45.78	48.60	40.47
Kuwait Airways	42.04	45.00	33.21
La Compagnie	46.67	49.17	41.67
LAM Mozambique	41.51	45.88	35.88
LAN Airlines	56.16	65.71	31.43
LAN Colombia	57.14	60.00	35.00
LAN Peru	35.71	42.50	30.00
Lao Airlines	70.00	75.00	56.67
Lao Skyway	67.96	73.75	56.43
LATAM Airlines	39.76	42.34	37.57
Laudamotion	42.09	42.31	38.46
LC Perú	44.84	48.46	37.69
Level	41.98	43.59	39.49
LIAT	36.57	38.00	35.14
Lion Air	43.27	44.42	39.77
Loganair	64.45	71.03	52.65
LOT Polish Airlines	52.41	55.78	44.68
Lubeck Air	91.43	100.00	70.00
Lucky Air	28.57	30.00	30.00
Lufthansa	57.79	62.59	47.20
Luxair	57.40	61.82	48.18
Lynx Air	45.12	47.70	40.45
Mack Air	77.14	75.00	70.00
Madagasikara Airways	87.14	92.50	70.00
Mahan Air	45.71	38.33	60.00
Malawian Airlines	51.43	56.67	42.50
Malaysia Airlines	54.28	61.40	34.49
Maldivian	57.62	57.50	60.00
Malindo Air	48.78	50.00	45.21
Mandarin Airlines	71.43	77.50	50.00
Mango Airlines	44.91	47.39	39.30
Mann Yadanarpon Airlines	65.71	80.00	45.00
Manta Air	82.86	100.00	60.00
Mauritania Airlines	40.00	40.00	40.00
Maya Island Air	30.00	32.50	30.00
MAYAir	57.14	55.00	50.00
MEGA Maldives Airlines	40.00	45.00	40.00
Meridiana	50.36	52.88	42.25
MIAT Mongolian Airlines	50.95	53.33	45.00
Middle East Airlines	39.00	36.75	45.00
Mihin Lanka	49.14	48.00	42.00
Mokulele Airlines	38.86	37.00	42.00
Monarch Airlines	56.55	53.76	57.92
Montenegro Airlines	48.10	48.33	43.33
Motor Sich Airlines	65.71	66.67	56.67
Myanmar Airways	62.14	56.88	70.00
Myanmar National Airlines	75.00	84.38	56.25
NAM Air	56.48	57.69	52.31
Nature Air	37.86	40.31	37.50
Nauru Airlines	88.57	100.00	70.00
Nepal Airlines	59.18	65.00	47.14
NIKI	61.04	60.45	60.00
Nile Air	74.29	80.00	50.00
Nok Air	54.81	56.27	48.27
NokScoot	50.00	47.50	50.00
Nordavia	35.71	40.00	30.00
Nordic Regional Airlines	60.00	60.00	60.00
Nordica	77.14	80.00	70.00
NordStar	71.43	65.00	70.00
Nordwind Airlines	74.29	72.50	70.00
Norse Atlantic Airways	41.61	44.87	36.41
Norwegian	49.26	52.93	37.90
Nouvelair	50.29	49.50	49.00
Novoair	68.57	75.00	50.00
Okay Airways	40.00	25.00	50.00
Olympic Air	58.53	63.33	49.49
Oman Air	44.93	48.36	33.44
Omni Air International	85.71	90.00	70.00
Onur Air	34.93	34.19	31.29
Openskies	14.29	8.75	30.00
Orient Thai Airlines	50.00	40.00	70.00
Pacific Airlines	44.68	49.09	39.09
Pakistan Intl Airlines	45.17	46.81	39.83
PAL Express	50.95	44.17	63.33
Pan Pacific Airlines	22.86	20.00	30.00
Passaredo	31.43	30.00	40.00
Passion Air	46.67	48.33	40.00
PAWA Dominicana	65.71	72.50	45.00
Peach Aviation	48.42	49.91	41.90
Pegasus Airlines	44.51	45.13	40.34
PenAir	41.90	43.33	43.33
Peruvian Airlines	34.46	35.94	33.12
Petroleum Air Services	65.71	67.50	55.00
Philippine Airlines	53.08	55.41	47.03
PLAY	62.55	66.35	52.01
Pobeda Airlines	38.37	38.86	36.57
Porter Airlines	46.76	50.67	40.49
Precision Air	43.64	46.09	40.55
Primera Air	45.14	39.00	46.00
Privilege Style	73.71	80.00	52.00
Qantas Airways	63.33	67.12	55.26
QantasLink	56.74	55.47	61.25
Qatar Airways	62.32	69.65	42.56
Qazaq Air	81.43	87.50	60.00
Ravn Alaska	37.74	40.00	35.79
Red Wings Airlines	47.14	55.00	40.00
Regent Airways	54.64	53.75	50.00
Regional Express	66.75	69.39	60.00
Rex Airlines	81.39	87.95	65.61
Rhein-Neckar Air	88.57	95.00	70.00
Rossiya Airlines	61.63	62.14	55.00
Royal Air Maroc	42.72	46.50	34.80
Royal Brunei Airlines	76.37	77.69	67.95
Royal Jordanian Airlines	51.98	53.88	48.21
Rwandair	52.92	56.09	45.22
Ryanair	49.81	52.70	39.91
S7 Siberia Airlines	62.32	65.00	52.19
SA Express	27.06	25.59	34.12
Safarilink	61.43	67.50	55.00
Safi Airways	25.71	27.50	25.00
Salam Air	30.00	31.25	28.75
Sansa Airlines	52.14	56.25	45.00
Santa Barbara Airlines	31.43	35.00	30.00
SAS Scandinavian	47.51	48.78	44.71
SATA Air Azores	42.38	42.59	42.89
SATA International	44.89	47.33	36.00
SATENA	54.29	55.00	50.00
Saudi Arabian Airlines	52.66	56.13	44.22
SCAT Airlines	67.14	68.75	50.00
Scoot	49.19	50.12	43.77
Seaborne Airlines	35.05	37.67	34.00
SereneAir	44.44	47.22	41.11
Shaheen Air	66.98	71.67	47.78
Shandong Airlines	44.00	45.00	42.00
Shanghai Airlines	46.94	47.50	42.14
Shenzhen Airlines	43.67	46.43	38.57
Sichuan Airlines	49.64	50.62	46.25
SilkAir	61.07	60.60	61.04
Silver Airways	35.26	37.41	34.30
Singapore Airlines	66.24	71.30	55.28
Sky Airline	46.13	48.90	40.24
Sky Express Airlines	48.74	51.52	43.77
Skybus	40.00	40.00	30.00
Skymark Airlines	61.90	62.50	50.00
Skytrans Airlines	44.00	48.00	38.00
SkyWest Airlines	60.95	58.75	65.00
SkyWork Airlines	50.00	55.00	45.00
Small Planet Airlines	65.46	67.87	57.06
Smartavia	32.14	31.25	32.50
SmartLynx Airlines	38.29	39.67	38.67
SmartWings	46.73	47.40	44.17
Solomon Airlines	74.29	80.00	62.00
Somon Air	34.29	30.00	50.00
South African Airways	54.95	58.55	47.35
Southern Airways Express	28.57	30.00	30.00
Southwest Airlines	50.39	52.23	46.80
SpiceJet	50.38	52.75	45.08
Spirit Airlines	37.30	38.59	35.24
Spring Airlines	45.18	45.16	40.94
SriLankan Airlines	56.85	58.65	52.26
Sriwijaya Air	53.18	54.20	46.82
Star Peru	40.95	40.42	42.50
STARLUX Airlines	37.14	38.33	33.33
Sun Country Airlines	42.38	45.44	37.44
Sun-Air	91.43	100.00	70.00
SunExpress	49.78	52.78	44.67
Sunwing Airlines	43.93	46.07	39.01
Super Air Jet	39.52	40.83	38.33
Surinam Airways	35.14	35.00	39.00
SVG Air	60.00	60.00	60.00
Swiss Intl Air Lines	55.73	60.02	46.42
Swoop	44.50	46.69	41.16
T’Way Air	50.00	52.19	45.62
TAAG Angola Airlines	34.45	34.41	34.12
TACA Airlines	54.29	60.00	30.00
TACV Cabo Verde Airlines	47.01	51.36	38.18
TAG Airlines	47.79	49.55	44.55
Tailwind Airlines	47.14	47.50	45.00
TAM Airlines	51.16	58.24	33.15
TAME	32.95	35.00	32.00
Tame Línea Aérea Del Ecuador	37.50	36.88	40.00
TAP Portugal	47.40	51.11	39.73
TAR Aerolineas	53.88	55.00	50.00
TAROM Romanian	60.90	66.45	46.16
Thai AirAsia	59.31	61.34	50.74
Thai Airways	64.44	65.83	60.78
Thai Lion Air	48.50	50.42	44.07
Thai Smile Airways	74.07	79.46	59.60
Thomas Cook Airlines	53.46	51.76	56.04
Thomas Cook Airlines Scandinavia	62.86	65.00	60.00
Thomson Airways	50.84	57.34	34.15
Tianjin Airlines	50.00	52.50	45.00
Tigerair	52.48	53.01	47.14
Tigerair Australia	42.86	45.08	38.24
Tigerair Taiwan	47.76	50.00	41.43
Titan Airways	70.07	75.00	57.62
Transaero Airlines	41.79	43.75	30.00
Transavia	46.54	49.32	39.88
TransNusa	64.29	62.50	60.00
Trigana Air	70.00	72.50	65.00
Tropic Air Belize	60.95	63.33	56.67
TUI Airways	55.84	59.13	46.09
TUIfly	58.51	60.22	52.17
Tunisair	47.65	50.00	37.50
Turkish Airlines	49.06	55.47	34.09
Turkmenistan Airlines	49.29	52.50	35.00
TUS Airways	67.86	76.25	52.50
Uganda Airlines	76.43	85.00	57.50
Ukraine International	49.00	49.53	44.75
Ultra Air	81.43	82.50	70.00
United Airlines	39.91	43.21	34.65
UP by El Al	34.29	30.00	40.00
Ural Airlines	54.29	57.33	45.33
US Airways	38.33	41.21	30.99
US-Bangla Airlines	57.14	58.75	51.25
Utair Aviation	43.57	37.50	52.50
Uzbekistan Airways	52.32	54.38	41.25
V Air	59.52	62.50	50.00
Vanilla Air	58.18	59.55	50.00
ViaAir	30.29	32.00	32.00
VietJet Air	41.92	43.97	38.36
Vietnam Airlines	58.93	59.46	54.15
Virgin America	52.11	59.94	32.75
Virgin Atlantic	64.07	63.01	67.03
Virgin Australia	54.99	57.79	48.93
Vistara	68.18	74.53	56.07
Viva Air	31.14	32.80	30.40
VivaAerobús	37.87	39.03	36.43
VivaColombia	33.71	34.33	33.33
VLM Airlines	71.90	79.17	58.33
Volaris	34.51	35.67	33.88
Volotea	61.16	64.53	51.71
Voyage Air	45.71	40.00	30.00
Vueling Airlines	40.16	41.85	37.00
Wamos Air	57.14	60.00	50.00
WestJet Airlines	45.28	48.49	40.82
Wideroe	44.90	48.57	40.00
Winair	37.14	41.11	35.56
Wingo	35.51	37.38	34.76
Wings Air	45.96	49.35	41.74
Wizz Air	43.06	44.99	38.15
World2Fly	60.00	65.00	30.00
WOW air	44.43	45.92	40.75
Xiamen Airlines	49.66	55.52	40.00
XL Airways France	42.29	45.00	32.00
Yakutia Airlines	54.29	55.00	50.00
Yangon Airways	62.86	70.00	40.00
Yeti Airlines	52.00	52.00	48.00
Zambia Airways	42.86	40.00	40.00
ZIPAIR	43.71	45.38	40.75

df_cleaned$NPS_group <- cut(df_cleaned$Rating,
                            breaks = c(-Inf, 6, 8, Inf),
                            labels = c("Detractor","Passive","Promoter"))

nps_by_airline <- df_cleaned %>%
  group_by(Airline_Name) %>%
  summarise(
    promoters = mean(NPS_group == "Promoter") * 100,
    detractors = mean(NPS_group == "Detractor") * 100,
    NPS = promoters - detractors
  )
kable(nps_by_airline, digits = 2)

Airline_Name	promoters	detractors	NPS
AB Aviation	33.33	66.67	-33.33
ANA All Nippon Airways	46.71	29.61	17.11
ASKY Airlines	8.33	91.67	-83.33
ATA Airlines	0.00	100.00	-100.00
Adria Airways	32.35	47.06	-14.71
Aegean Airlines	38.28	46.65	-8.37
Aer Lingus	25.36	58.70	-33.33
Aero VIP	66.67	0.00	66.67
AeroItalia	0.00	100.00	-100.00
Aeroflot Russian Airlines	23.79	54.37	-30.58
Aerolineas Argentinas	14.93	59.70	-44.78
Aeromar	0.00	80.00	-80.00
Aeromexico	15.88	74.68	-58.80
Africa World Airlines	16.67	83.33	-66.67
Aigle Azur	11.11	66.67	-55.56
Air Algerie	15.38	76.92	-61.54
Air Antilles	33.33	66.67	-33.33
Air Arabia	8.05	86.21	-78.16
Air Astana	56.16	26.03	30.14
Air Austral	0.00	85.71	-85.71
Air Bagan	0.00	0.00	0.00
Air Berlin	21.34	59.15	-37.80
Air Botswana	57.14	42.86	14.29
Air Burkina	0.00	50.00	-50.00
Air Busan	50.00	30.00	20.00
Air Cairo	20.00	70.00	-50.00
Air Canada	11.37	79.91	-68.54
Air Canada rouge	6.27	87.47	-81.20
Air Caraibes	0.00	100.00	-100.00
Air China	17.88	68.21	-50.33
Air Corsica	42.86	42.86	0.00
Air Costa	0.00	0.00	0.00
Air Djibouti	0.00	100.00	-100.00
Air Dolomiti	22.86	45.71	-22.86
Air Europa	7.69	78.02	-70.33
Air France	21.63	61.59	-39.97
Air Greenland	0.00	100.00	-100.00
Air Iceland Connect	100.00	0.00	100.00
Air India	21.74	55.90	-34.16
Air India Express	4.76	74.60	-69.84
Air Italy	15.38	76.92	-61.54
Air Juan	0.00	95.65	-95.65
Air KBZ	44.44	0.00	44.44
Air Koryo	23.53	35.29	-11.76
Air Macau	0.00	100.00	-100.00
Air Madagascar	6.67	73.33	-66.67
Air Malta	7.89	76.32	-68.42
Air Mauritius	13.16	57.89	-44.74
Air Mediterranee	0.00	0.00	0.00
Air Moldova	17.39	65.22	-47.83
Air Namibia	28.00	60.00	-32.00
Air New Zealand	29.59	56.21	-26.63
Air Niugini	0.00	84.62	-84.62
Air North Yukon’s Airline	75.00	25.00	50.00
Air Nostrum	20.00	50.00	-30.00
Air Panama	7.14	64.29	-57.14
Air Pegasus	0.00	75.00	-75.00
Air Rarotonga	100.00	0.00	100.00
Air Serbia	34.55	51.83	-17.28
Air Seychelles	25.00	65.00	-40.00
Air Tahiti Nui	6.06	75.76	-69.70
Air Tanzania	5.56	83.33	-77.78
Air Transat	28.74	60.15	-31.42
Air Vanuatu	10.71	71.43	-60.71
Air Zimbabwe	0.00	100.00	-100.00
AirAsia	14.49	59.97	-45.48
AirAsia India	18.00	69.00	-51.00
AirAsia Philippines	11.39	87.34	-75.95
AirAsia X	31.84	44.13	-12.29
AirAsia Zest	37.50	62.50	-25.00
AirConnect	0.00	100.00	-100.00
AirSWIFT	16.67	66.67	-50.00
Aircalin	9.09	63.64	-54.55
Airlink	42.00	40.00	2.00
Airnorth	29.17	58.33	-29.17
Akasa Air	25.84	60.67	-34.83
Alaska Airlines	27.08	66.27	-39.19
Alitalia	22.01	61.49	-39.48
Allegiant Air	13.08	79.53	-66.45
Alliance Air	5.41	89.19	-83.78
Alliance Airlines	20.00	80.00	-60.00
Amaszonas	7.69	69.23	-61.54
American Airlines	7.29	88.30	-81.01
American Eagle	23.68	68.42	-44.74
AnadoluJet	17.65	58.82	-41.18
Andes Líneas Aéreas	0.00	100.00	-100.00
Arajet	50.00	50.00	0.00
Ariana Afghan Airlines	0.00	100.00	-100.00
Arik Air	0.00	88.89	-88.89
Arkefly	12.50	37.50	-25.00
Arkia Israeli Airlines	10.53	84.21	-73.68
Armenia Air Company	0.00	100.00	-100.00
Asiana Airlines	43.30	37.11	6.19
Atlantic Airways	100.00	0.00	100.00
Atlasglobal	23.81	54.76	-30.95
Auric Air	100.00	0.00	100.00
Aurigny Air	34.48	51.72	-17.24
Austrian Airlines	21.76	62.53	-40.77
Avelo Airlines	19.64	77.68	-58.04
Avianca	10.38	82.69	-72.31
Avianca Brazil	33.33	44.44	-11.11
Avianca Ecuador	0.00	100.00	-100.00
Avior Airlines	0.00	100.00	-100.00
Azerbaijan Airlines	23.08	53.85	-30.77
Azul Airlines	16.00	72.00	-56.00
Azur Air Germany	0.00	100.00	-100.00
BA CityFlyer	52.27	13.64	38.64
BH Air	20.00	60.00	-40.00
Badr Airlines	0.00	0.00	0.00
Bahamasair	6.06	84.85	-78.79
Bamboo Airways	20.00	56.00	-36.00
Bangkok Airways	50.49	29.13	21.36
Bassaka Air	66.67	22.22	44.44
Batik Air	12.28	71.93	-59.65
Beijing Capital Airlines	50.00	50.00	0.00
Belavia	36.36	36.36	0.00
Bhutan Airlines	40.00	60.00	-20.00
Biman Bangladesh Airlines	30.30	27.27	3.03
Binter Canarias	44.44	33.33	11.11
Blu-express	0.00	100.00	-100.00
Blue Air	15.62	70.83	-55.21
Blue Islands	20.00	65.00	-45.00
Blue Panorama Airlines	0.00	95.65	-95.65
Blue1	0.00	100.00	-100.00
Bluebird Airways	20.00	80.00	-60.00
Boliviana de Aviacion	13.33	53.33	-40.00
BoraJet	0.00	100.00	-100.00
Boutique Air	15.62	84.38	-68.75
Breeze Airways	18.92	73.65	-54.73
British Airways	18.86	61.71	-42.84
Brussels Airlines	11.43	75.36	-63.93
Buddha Air	8.33	75.00	-66.67
Bulgaria Air	30.00	45.00	-15.00
Buta Airways	12.50	62.50	-50.00
CSA Czech Airlines	13.54	78.12	-64.58
Camair-Co	0.00	100.00	-100.00
Cambodia Airways	0.00	50.00	-50.00
Cambodia Angkor Air	7.69	73.85	-66.15
Cambodia Bayon Airlines	0.00	100.00	-100.00
Canadian North	66.67	33.33	33.33
Canaryfly	16.67	50.00	-33.33
Cape Air	7.14	92.86	-85.71
Caribbean Airlines	17.50	65.00	-47.50
Cathay Dragon	60.00	20.00	40.00
Cathay Pacific Airways	37.08	42.02	-4.94
Cayman Airways	9.09	81.82	-72.73
Cebu Pacific	15.81	70.36	-54.55
CemAir	20.00	40.00	-20.00
Central Mountain Air	20.00	80.00	-60.00
China Airlines	25.78	44.53	-18.75
China Eastern Airlines	25.60	55.36	-29.76
China Southern Airlines	43.56	26.70	16.86
China United Airlines	0.00	100.00	-100.00
Citilink	18.33	40.00	-21.67
CityJet	34.48	51.72	-17.24
Coastal Aviation	50.00	25.00	25.00
Cobalt	35.71	50.00	-14.29
Comair	13.89	52.78	-38.89
Condor Airlines	15.97	74.79	-58.82
Contour Airlines	0.00	100.00	-100.00
Copa Airlines	12.32	74.64	-62.32
Corendon Airlines	13.51	78.38	-64.86
Corsair	0.00	100.00	-100.00
Croatia Airlines	15.79	57.89	-42.11
Cubana Airlines	7.14	73.81	-66.67
Cyprus Airways	0.00	77.78	-77.78
Danish Air Transport	66.67	33.33	33.33
Darwin Airline	25.00	50.00	-25.00
Delta Air Lines	19.93	70.35	-50.42
Dimonim Air	0.00	0.00	0.00
Discover Airlines	33.33	66.67	-33.33
Dragonair	37.50	31.25	6.25
Druk Air	62.50	12.50	50.00
Dynamic International Airways	0.00	100.00	-100.00
EVA Air	45.95	31.76	14.19
EWA Air	0.00	100.00	-100.00
Eastar Jet	30.77	46.15	-15.38
Eastern Airlines	0.00	100.00	-100.00
Eastern Airways	37.04	59.26	-22.22
Easyfly	28.57	42.86	-14.29
Edelweiss Air	43.33	40.00	3.33
Egyptair	14.17	71.65	-57.48
El Al Israel Airlines	13.43	70.15	-56.72
Ellinair	63.64	27.27	36.36
Emirates	25.40	55.85	-30.45
Ernest Airlines	25.00	75.00	-50.00
Estonian Air	42.86	0.00	42.86
Ethiopian Airlines	15.66	64.46	-48.80
Etihad Airways	15.04	72.37	-57.33
Europe Airpost	100.00	0.00	100.00
Eurowings	8.42	80.00	-71.58
FLYONE	6.25	93.75	-87.50
FMI Air	100.00	0.00	100.00
Far Eastern Air Transport	0.00	100.00	-100.00
Felix Airways	0.00	0.00	0.00
Fiji Airways	25.68	55.41	-29.73
Finnair	26.77	59.06	-32.28
Firefly	35.42	43.75	-8.33
Flair Airlines	13.76	79.92	-66.16
Fly540.com	0.00	83.33	-83.33
FlyArystan	50.00	33.33	16.67
FlySafair	29.03	60.22	-31.18
Flynas	0.00	92.11	-92.11
Freebird Airlines	27.27	54.55	-27.27
French Bee	3.70	92.59	-88.89
Frontier Airlines	6.88	90.34	-83.46
Fuji Dream Airlines	100.00	0.00	100.00
Garuda Indonesia	66.15	20.00	46.15
Georgian Airways	3.33	83.33	-80.00
Germania Airline	0.00	71.43	-71.43
Germanwings	25.86	44.83	-18.97
Go First	2.48	89.26	-86.78
GoAir	6.37	87.26	-80.89
Gol Transportes Aéreos	3.03	51.52	-48.48
Golden Myanmar Airlines	66.67	0.00	66.67
Grand Cru Airlines	100.00	0.00	100.00
Greater Bay Airlines	0.00	100.00	-100.00
Gulf Air	16.46	72.15	-55.70
HK Express	15.56	64.44	-48.89
HOP!	16.67	33.33	-16.67
Hainan Airlines	31.82	34.85	-3.03
Hawaiian Airlines	17.53	68.83	-51.30
Helvetic Airways	29.41	35.29	-5.88
Hi Fly	50.00	33.33	16.67
HiSky	28.57	42.86	-14.29
Himalaya Airlines	0.00	100.00	-100.00
Hong Kong Airlines	11.63	67.44	-55.81
Horizon Air	33.33	16.67	16.67
Hunnu Air	100.00	0.00	100.00
ITA Airways	7.14	89.29	-82.14
Iberia	20.54	62.35	-41.81
Iberia Express	7.84	78.43	-70.59
Icelandair	22.77	62.87	-40.10
IndiGo	35.38	49.54	-14.15
Insel Air	0.00	95.56	-95.56
InterSky	75.00	25.00	50.00
Intercaribbean Airways	14.29	85.71	-71.43
Interjet	4.96	91.49	-86.52
Iran Air	42.86	14.29	28.57
Iran Aseman Airlines	33.33	66.67	-33.33
Island Air	36.36	45.45	-9.09
Israir Airlines	0.00	66.67	-66.67
JC International Airlines	0.00	100.00	-100.00
Japan Airlines	43.02	36.05	6.98
Jazeera Airways	5.26	89.47	-84.21
Jazz	33.33	0.00	33.33
Jeju Air	5.26	68.42	-63.16
Jet Airways	18.93	60.27	-41.33
Jet2.com	49.37	32.28	17.09
JetSmart	18.18	81.82	-63.64
Jetairfly	16.67	66.67	-50.00
Jetblue Airways	11.33	87.08	-75.75
Jetstar Airways	12.92	72.67	-59.75
Jetstar Asia	13.74	66.41	-52.67
Jetstar Japan	5.41	94.59	-89.19
Jetstar Pacific	5.17	81.03	-75.86
JetsuiteX	100.00	0.00	100.00
Jin Air	50.00	28.57	21.43
Joon	0.00	100.00	-100.00
Jordan Aviation	100.00	0.00	100.00
Juneyao Air	10.00	65.00	-55.00
KD Air	0.00	100.00	-100.00
KLM Royal Dutch Airlines	30.86	51.74	-20.88
Kan Air	50.00	50.00	0.00
Kenya Airways	21.60	60.80	-39.20
Kish Airlines	0.00	0.00	0.00
Korean Air	50.83	29.17	21.67
Kulula	13.95	79.07	-65.12
Kuwait Airways	7.14	73.21	-66.07
LAM Mozambique	5.88	88.24	-82.35
LAN Airlines	36.19	36.19	0.00
LAN Colombia	0.00	50.00	-50.00
LAN Peru	0.00	100.00	-100.00
LATAM Airlines	5.92	87.50	-81.58
LC Perú	7.69	69.23	-61.54
LIAT	17.14	65.71	-48.57
LOT Polish Airlines	21.69	63.05	-41.36
La Compagnie	16.67	66.67	-50.00
Lao Airlines	50.00	16.67	33.33
Lao Skyway	35.71	17.86	17.86
Laudamotion	11.54	88.46	-76.92
Level	15.38	79.49	-64.10
Lion Air	3.85	80.77	-76.92
Loganair	41.18	47.06	-5.88
Lubeck Air	100.00	0.00	100.00
Lucky Air	0.00	100.00	-100.00
Lufthansa	25.65	53.15	-27.50
Luxair	31.82	45.45	-13.64
Lynx Air	17.42	77.18	-59.76
MAYAir	0.00	100.00	-100.00
MEGA Maldives Airlines	0.00	100.00	-100.00
MIAT Mongolian Airlines	33.33	50.00	-16.67
Mack Air	100.00	0.00	100.00
Madagasikara Airways	100.00	0.00	100.00
Mahan Air	33.33	33.33	0.00
Malawian Airlines	8.33	66.67	-58.33
Malaysia Airlines	24.30	53.43	-29.13
Maldivian	0.00	66.67	-66.67
Malindo Air	9.20	74.23	-65.03
Mandarin Airlines	0.00	0.00	0.00
Mango Airlines	14.08	80.28	-66.20
Mann Yadanarpon Airlines	50.00	0.00	50.00
Manta Air	100.00	0.00	100.00
Mauritania Airlines	0.00	100.00	-100.00
Maya Island Air	0.00	100.00	-100.00
Meridiana	32.50	45.00	-12.50
Middle East Airlines	25.00	65.00	-40.00
Mihin Lanka	0.00	60.00	-60.00
Mokulele Airlines	20.00	80.00	-60.00
Monarch Airlines	36.73	41.59	-4.87
Montenegro Airlines	16.67	50.00	-33.33
Motor Sich Airlines	33.33	33.33	0.00
Myanmar Airways	50.00	12.50	37.50
Myanmar National Airlines	50.00	12.50	37.50
NAM Air	7.69	53.85	-46.15
NIKI	45.45	27.27	18.18
Nature Air	6.25	87.50	-81.25
Nauru Airlines	100.00	0.00	100.00
Nepal Airlines	14.29	57.14	-42.86
Nile Air	0.00	0.00	0.00
Nok Air	24.55	56.36	-31.82
NokScoot	0.00	50.00	-50.00
NordStar	0.00	0.00	0.00
Nordavia	0.00	50.00	-50.00
Nordic Regional Airlines	0.00	100.00	-100.00
Nordica	50.00	0.00	50.00
Nordwind Airlines	50.00	0.00	50.00
Norse Atlantic Airways	10.26	89.74	-79.49
Norwegian	30.51	57.37	-26.86
Nouvelair	30.00	70.00	-40.00
Novoair	0.00	0.00	0.00
Okay Airways	0.00	100.00	-100.00
Olympic Air	25.64	56.41	-30.77
Oman Air	13.28	67.97	-54.69
Omni Air International	50.00	0.00	50.00
Onur Air	12.90	87.10	-74.19
Openskies	37.50	50.00	-12.50
Orient Thai Airlines	100.00	0.00	100.00
PAL Express	33.33	66.67	-33.33
PAWA Dominicana	0.00	50.00	-50.00
PLAY	35.22	47.17	-11.95
Pacific Airlines	9.09	81.82	-72.73
Pakistan Intl Airlines	6.90	70.69	-63.79
Pan Pacific Airlines	0.00	100.00	-100.00
Passaredo	0.00	100.00	-100.00
Passion Air	0.00	100.00	-100.00
Peach Aviation	24.14	67.24	-43.10
Pegasus Airlines	18.10	71.98	-53.88
PenAir	0.00	66.67	-66.67
Peruvian Airlines	0.00	93.75	-93.75
Petroleum Air Services	50.00	50.00	0.00
Philippine Airlines	21.76	63.82	-42.06
Pobeda Airlines	8.57	88.57	-80.00
Porter Airlines	17.04	79.82	-62.78
Precision Air	7.27	80.00	-72.73
Primera Air	0.00	100.00	-100.00
Privilege Style	60.00	40.00	20.00
Qantas Airways	33.43	46.22	-12.79
QantasLink	34.38	29.69	4.69
Qatar Airways	30.23	46.51	-16.28
Qazaq Air	100.00	0.00	100.00
Ravn Alaska	5.26	94.74	-89.47
Red Wings Airlines	0.00	100.00	-100.00
Regent Airways	12.50	75.00	-62.50
Regional Express	57.58	27.27	30.30
Rex Airlines	77.27	19.70	57.58
Rhein-Neckar Air	100.00	0.00	100.00
Rossiya Airlines	42.86	35.71	7.14
Royal Air Maroc	13.82	78.86	-65.04
Royal Brunei Airlines	57.69	24.36	33.33
Royal Jordanian Airlines	23.88	62.69	-38.81
Rwandair	26.09	65.22	-39.13
Ryanair	24.29	58.65	-34.36
S7 Siberia Airlines	31.25	37.50	-6.25
SA Express	23.53	76.47	-52.94
SAS Scandinavian	16.55	68.07	-51.52
SATA Air Azores	22.89	63.86	-40.96
SATA International	15.56	73.33	-57.78
SATENA	50.00	50.00	0.00
SCAT Airlines	0.00	25.00	-25.00
STARLUX Airlines	0.00	100.00	-100.00
SVG Air	0.00	100.00	-100.00
Safarilink	50.00	0.00	50.00
Safi Airways	0.00	100.00	-100.00
Salam Air	0.00	100.00	-100.00
Sansa Airlines	50.00	50.00	0.00
Santa Barbara Airlines	0.00	33.33	-33.33
Saudi Arabian Airlines	20.59	60.78	-40.20
Scoot	18.62	66.11	-47.49
Seaborne Airlines	6.67	93.33	-86.67
SereneAir	22.22	77.78	-55.56
Shaheen Air	33.33	22.22	11.11
Shandong Airlines	0.00	80.00	-80.00
Shanghai Airlines	0.00	78.57	-78.57
Shenzhen Airlines	0.00	85.71	-85.71
Sichuan Airlines	25.00	62.50	-37.50
SilkAir	22.39	46.27	-23.88
Silver Airways	6.96	90.51	-83.54
Singapore Airlines	40.05	40.82	-0.77
Sky Airline	14.63	78.05	-63.41
Sky Express Airlines	18.84	72.46	-53.62
SkyWest Airlines	25.00	33.33	-8.33
SkyWork Airlines	0.00	50.00	-50.00
Skybus	0.00	100.00	-100.00
Skymark Airlines	50.00	16.67	33.33
Skytrans Airlines	0.00	80.00	-80.00
Small Planet Airlines	47.06	38.24	8.82
SmartLynx Airlines	6.67	80.00	-73.33
SmartWings	8.33	70.83	-62.50
Smartavia	0.00	100.00	-100.00
Solomon Airlines	40.00	40.00	0.00
Somon Air	0.00	100.00	-100.00
South African Airways	20.51	62.39	-41.88
Southern Airways Express	0.00	100.00	-100.00
Southwest Airlines	23.55	70.42	-46.88
SpiceJet	19.27	67.71	-48.44
Spirit Airlines	11.44	84.05	-72.61
Spring Airlines	9.38	59.38	-50.00
SriLankan Airlines	26.45	59.35	-32.90
Sriwijaya Air	15.91	63.64	-47.73
Star Peru	8.33	75.00	-66.67
Sun Country Airlines	17.44	78.49	-61.05
Sun-Air	100.00	0.00	100.00
SunExpress	17.78	71.11	-53.33
Sunwing Airlines	10.16	79.95	-69.79
Super Air Jet	0.00	83.33	-83.33
Surinam Airways	10.00	70.00	-60.00
Swiss Intl Air Lines	27.44	54.88	-27.44
Swoop	15.17	79.40	-64.23
T’Way Air	18.75	68.75	-50.00
TAAG Angola Airlines	0.00	94.12	-94.12
TACA Airlines	33.33	33.33	0.00
TACV Cabo Verde Airlines	18.18	81.82	-63.64
TAG Airlines	18.18	81.82	-63.64
TAM Airlines	12.96	59.26	-46.30
TAME	6.67	86.67	-80.00
TAP Portugal	19.73	68.44	-48.71
TAR Aerolineas	28.57	71.43	-42.86
TAROM Romanian	40.70	39.53	1.16
TUI Airways	26.09	56.52	-30.43
TUIfly	4.35	52.17	-47.83
TUS Airways	50.00	25.00	25.00
Tailwind Airlines	0.00	75.00	-75.00
Tame Línea Aérea Del Ecuador	0.00	100.00	-100.00
Thai AirAsia	20.37	53.70	-33.33
Thai Airways	27.80	46.78	-18.98
Thai Lion Air	11.02	78.81	-67.80
Thai Smile Airways	53.98	24.43	29.55
Thomas Cook Airlines	27.56	55.56	-28.00
Thomas Cook Airlines Scandinavia	100.00	0.00	100.00
Thomson Airways	24.30	50.70	-26.41
Tianjin Airlines	25.00	75.00	-50.00
Tigerair	20.41	50.00	-29.59
Tigerair Australia	11.76	78.43	-66.67
Tigerair Taiwan	28.57	71.43	-42.86
Titan Airways	61.90	28.57	33.33
TransNusa	50.00	50.00	0.00
Transaero Airlines	6.25	62.50	-56.25
Transavia	13.61	74.56	-60.95
Trigana Air	0.00	0.00	0.00
Tropic Air Belize	66.67	33.33	33.33
Tunisair	14.29	60.71	-46.43
Turkish Airlines	16.33	70.32	-53.99
Turkmenistan Airlines	8.33	50.00	-41.67
UP by El Al	0.00	100.00	-100.00
US Airways	17.61	67.96	-50.35
US-Bangla Airlines	12.50	50.00	-37.50
Uganda Airlines	50.00	50.00	0.00
Ukraine International	17.32	66.48	-49.16
Ultra Air	50.00	0.00	50.00
United Airlines	9.01	84.53	-75.51
Ural Airlines	6.67	66.67	-60.00
Utair Aviation	0.00	50.00	-50.00
Uzbekistan Airways	6.25	56.25	-50.00
V Air	33.33	50.00	-16.67
VLM Airlines	50.00	16.67	33.33
Vanilla Air	36.36	54.55	-18.18
ViaAir	0.00	100.00	-100.00
VietJet Air	5.94	82.19	-76.26
Vietnam Airlines	24.23	48.85	-24.62
Virgin America	38.75	52.50	-13.75
Virgin Atlantic	26.84	54.24	-27.40
Virgin Australia	19.36	56.72	-37.36
Vistara	49.57	38.46	11.11
Viva Air	0.00	100.00	-100.00
VivaAerobús	4.08	89.80	-85.71
VivaColombia	6.67	90.00	-83.33
Volaris	5.22	91.79	-86.57
Volotea	38.37	46.12	-7.75
Voyage Air	0.00	100.00	-100.00
Vueling Airlines	10.51	79.69	-69.19
WOW air	15.04	75.49	-60.45
Wamos Air	50.00	50.00	0.00
WestJet Airlines	16.32	78.87	-62.55
Wideroe	21.43	71.43	-50.00
Winair	0.00	77.78	-77.78
Wingo	4.76	90.48	-85.71
Wings Air	4.35	73.91	-69.57
Wizz Air	12.99	76.15	-63.16
World2Fly	0.00	100.00	-100.00
XL Airways France	30.00	55.00	-25.00
Xiamen Airlines	6.90	79.31	-72.41
Yakutia Airlines	0.00	100.00	-100.00
Yangon Airways	0.00	0.00	0.00
Yeti Airlines	0.00	60.00	-60.00
ZIPAIR	12.50	82.50	-70.00
Zambia Airways	0.00	50.00	-50.00
airBaltic	16.79	67.88	-51.09
airblue	23.53	64.71	-41.18
bmi Regional	19.05	73.81	-54.76
easyJet	28.20	56.49	-28.29
euroAtlantic Airways	0.00	100.00	-100.00
fastjet	29.63	66.67	-37.04
flyadeal	5.88	94.12	-88.24
flybe	50.00	50.00	0.00
flybondi	0.00	100.00	-100.00
flydubai	15.38	76.92	-61.54

Plots:

1. CSI % by airline (top/bottom):

Here I will showcase the top 20 airlines according to the CSI

aggregates_by_airline <- df_cleaned %>%
  group_by(Airline_Name) %>%
  summarise(
    Avg_CSI      = mean(CSI_Percentage, na.rm = TRUE),
    Avg_Onboard  = mean(Onboard_Percentage, na.rm = TRUE),
    Avg_Ground   = mean(Ground_Percentage, na.rm = TRUE),
    .groups = "drop"
)

# use aggregates_by_airline from earlier
top20_csi <- aggregates_by_airline %>%
  arrange(desc(Avg_CSI)) %>%
  slice_head(n = 20)

ggplot(top20_csi, aes(x = reorder(Airline_Name, Avg_CSI), y = Avg_CSI)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(x = "Airline",
       y = "Average CSI (%)",
       title = "Top 20 airlines by Customer Satisfaction Index")

2. Onboard vs ground satisfaction:

ggplot(aggregates_by_airline,
       aes(x = Avg_Onboard, y = Avg_Ground)) +
  geom_point(alpha = 0.6) +
  labs(x = "Onboard satisfaction (%)",
       y = "Ground & wifi satisfaction (%)",
       title = "Onboard vs ground experience by airline") +
  geom_hline(yintercept = mean(aggregates_by_airline$Avg_Ground), linetype = "dashed") +
  geom_vline(xintercept = mean(aggregates_by_airline$Avg_Onboard), linetype = "dashed")

3. Top 50 NPS per Airline:

The NPS bar plot for the top 20 airlines shows how strongly each airline’s customers would recommend it, based on the balance of promoters and detractors.

What the plot displays Each bar is one airline, ordered from highest to lowest NPS. The height of the bar (NPS) is computed as: Positive bars (above zero) mean that more customers are promoters than detractors; negative bars mean the opposite.

top20_nps <- nps_by_airline %>%
  arrange(desc(NPS)) %>%
  slice_head(n = 50)

ggplot(top20_nps,
       aes(x = reorder(Airline_Name, NPS), y = NPS)) +
  geom_col(fill = "darkgreen") +
  coord_flip() +
  labs(x = "Airline",
       y = "NPS (promoters − detractors, % points)",
       title = "Top 20 airlines by NPS")

4. Distribution of Customer satisfaction:

ggplot(df_cleaned, aes(x = CSI_Percentage)) +
  geom_histogram(binwidth = 5, fill = "skyblue", color = "white") +
  labs(x = "CSI (%)",
       y = "Number of passengers",
       title = "Distribution of customer satisfaction scores")

These results show that customer satisfaction varies widely by airline, with clear groups of strong, average, and weak performers when using your CSI and sub-indices in percent. Overall level and spread - Many airlines cluster roughly between 40–60% CSI, which indicates moderate satisfaction (customers neither very happy nor very unhappy on average). - A sizeable number achieve CSI above 70–80% (e.g. Air Costa 80%, Air North Yukon’s Airline 81.43%, Air Rarotonga 89.52%, Grand Cru Airlines 91.43%), which signals very strong perceived experience and loyalty potential. - At the low end, some carriers are below 35% CSI (e.g. AirConnect 14.29%, Openskies 14.29%, Andes Líneas Aéreas 23.57%), pointing to serious service or perception issues. Onboard vs ground experience Looking at CSI_Percentage against Onboard_Percentage and Ground_Percentage: - For many airlines, Onboard_Percentage is noticeably higher than Ground_Percentage, meaning passengers are relatively more satisfied with in‑flight service (seats, crew, food, inflight experience) than with ground or connectivity aspects (check‑in, boarding, wifi). - Examples: Cathay Pacific Airways (Onboard 70.48% vs Ground 31.51%), Emirates (65.92% vs 31.93%), Air Transat (54.31% vs 33.26%). - Some airlines have balanced or even better ground scores, which is rarer and can be positioned as a differentiator (e.g. QantasLink 55.47% onboard vs 61.25% ground, SkyWest Airlines 58.75% vs 65%). - High‑CSI airlines typically have both onboard and ground above ~60–65%; where one dimension lags, overall CSI drops, even if the other is strong. High-CSI airlines: strengths - Airlines like Air Costa, Air Rarotonga, Air Iceland Connect, BA CityFlyer, Druk Air, Garuda Indonesia, Grand Cru Airlines, JetsuiteX, Hunnu Air, Nauru Airlines, Rex Airlines, Rhein‑Neckar Air, Sun‑Air, etc. show CSI often above 75–80% with very high Onboard_Percentage (often 85–100%). - These carriers usually also have solid Ground_Percentage (often 60–70%), suggesting a consistently strong end‑to‑end experience (check‑in, punctuality, comfort, and staff interactions). - In a marketing context, they can be benchmark “best practice” examples and used in your report as proof that high satisfaction is achievable. Low-CSI airlines: weaknesses - Airlines in the 20–40% CSI range often show either: - Weak onboard and ground simultaneously (e.g. AirConnect 0% onboard, 50% ground; Openskies 8.75% onboard, 30% ground), or - Reasonable onboard but very weak ground, dragging CSI down (e.g. some long‑haul or low‑cost operators with good cabin but poor ground or wifi). - For these airlines, marketing and operations should focus on: - Fixing basic pain points first (delays, baggage, check‑in, call centres). - Clarifying service promises in campaigns so expectations and reality align, reducing dissatisfaction caused by “over‑promising”.