Those of us who have had the misfortune to travel on US domestic airlines know that they are simply the flying versions of NYC cabs (or probably Greyhound buses). The customer service is pathetic, the staff unfreindly, the airlines charge for every small thing…the list goes on and on.
University of Michigan carries out surveys of American customers and publishes the average scores annually as American Customer Satisfaction Index (ACSI). You can check the scores for several industries by visiting their website. For airlines, the chart looks like this:
The airlines appear in the descending order of the 2015 ACSI scores, which range from 81 for JetBlue to 54 for Spirit.
ACSI is published for a given brand only once a year. But companies are interested in knowing about customer satisfaction round the clock. So I decided to use Twitter sentiment as a measure for customer satisfaction. This is a very rough exerise to see whether we get any results that have face validity. My students will realize that, for airlines, Twitter is one of the key social networks for addressing customer complaints. Therefore, Twitter will likely capture customer satisfaction in real time. So the validity is actually about ACSI and not about Twitter sentiment. However, there is a commonly discussed issue about Twitter — it’s not representative of the general population. Still, we must keep in mind that ACSI may not be a good representative of American flyers sentiment either.
I decided to focus on the 9 airlines for which the ACSI scores for 2015 are available – JetBlue, Southwest, Alaska, Delta, American Air, Allegiant, United, Frontier, and Spirit. The graph looks as follows:
The average score for these 9 airlines is 68.11. As the maximum possible ACSI score is 100, 68 is not a great score. However, I am amazed at how thoroughly the epxectations of American flyers have gone down. I am sure that if the survey respondents were from Asia, you would get an average of less than 50. But that’s a story for another post where I will compare the sentiment about the best airlines including Singapore Airlines, Emirates, Qatar, etc.
Next, I went on Twitter and downloaded tweets that were directed at these airlines. My condition was simply that the Twitter handle of the airline should appear in the tweet. For example, a tweet mentioning @JetBlue would indicate that this is a tweet targeted towards JetBlue and therefore should be included for the analysis. I carried out this data collection on 2 April 2016 from Singapore. Following this, I categorized the tweets as either positive, negative, or neutral. To compare to ACSI, I created a metric similar to Net Promoter Score (NPS). The formula for that is given as follows:
Here is the graph when I plotted net sentiment scores of all the 9 airlines:
The score is bounded between -1 and 1. If all the tweets are negative then the score will be -1 and if all the tweets are positive then the score will be 1.
The average score is 0.19, which is around 60% of the scale range (1.19/2.0). We see that similar to ACSI graph, 4 airlines–JetBlue, Alaska, Southwest, and Delta–are above the mean while remaining 5 are below the mean. Interestingly, these are the same 4 airlines which have above average scores on ACSI. The ordering is a bit off though. In order to better compare the two graphs, I decided to plot them in the same space. However, for that I need to have the same scale. For the sake of convenience I decided to use Z-scores.¹
I find that the correlation is high at 0.77. It’s also statistically significant with a p-value equal to 0.016. However, notice that we have only 9 observations, which means that the standard error is likely to be high. Actually the 95% confidence interval for the correlation coefficient is pretty wide [0.21, 0.95] but the lower level is still comfortably far from 0.
I think that ACSI is doing a fair job of capturing customer satisfaction of American air travellers. It corresponds to the Twitter sentiment quite well. It’s worth noting that I am comparing the survey results, which were collected over 1-2 months period in 2015 with tweets that were sent on or slightly before 2 April 2016. It would be worth studying how Twitter sentiment fluctuates over a period of time. This is my next assignment once I am done with the sentiment analysis of top ranked airlines.
In case you are interested in individual airlines sentiment charts, you can view them here:
¹ Z-score of any variable has 0 average and 1 standard deviation.