From Data to Departure: Visualizing British Airways Reviews with Tableau

Traveling can be a joy or a hassle, depending on the experience. As a data enthusiast and a frequent flyer, I decided to delve into the reviews of British Airways to uncover trends, patterns, and insights. In this blog post, I will walk you through my journey of analyzing British Airways reviews using Tableau, a powerful data visualization tool.

I obtained British Airways review data spanning from 2016 to 2023 from Kaggle and conducted an analysis to extract actionable insights. The dataset includes details such as name, date, travel type, seat type, and route. The survey also evaluates various aspects like seat comfort, food quality, staff service, value for money, entertainment, and ground service.

I planned to create an interactive dashboard, incorporating metrics to display averages based on the reviews. First, I created parameters for overall rating, cabin staff service, entertainment, food, ground service, seat comfort, and value.

Map Chart:

To visualize the data effectively, I used a map chart in Tableau. This map chart showcases the average ratings for various parameters such as overall rating, cabin staff service, entertainment, food, ground service, seat comfort, and value. Additionally, it displays the number of reviews from different regions. customized the map with color gradients to indicate different average rating levels, making it easier to identify regions with higher or lower satisfaction.

Tooltips were enhanced to show detailed information when hovering over a region, including the average rating for each parameter and the total number of reviews.

Aircraft:

To clearly view customer feedback for each parameter, I used horizontal bar charts in Tableau. These charts display the average ratings for various parameters and additionally, they show the number of reviews for each aircraft type.

The charts made it easy to identify which aircraft types received the highest and lowest average ratings for each parameter. This helped highlight areas where British Airways excelled and where improvements were needed. The side-by-side arrangement of the bar charts allowed for a quick comparison across different parameters. End users could easily see if certain aircraft types consistently performed well or poorly across multiple aspects of the service.

Month Performance:

I used Line chart to display month wise performance. The line chart made it easy to identify trends and seasonal patterns in customer feedback.

This line chart displays monthly trends for overall rating, cabin staff service, entertainment, food, ground service, seat comfort, and value, providing a clear view of how these metrics have evolved from 2016 to 2023.

Summary:

Presenting KPIs for overall rating, cabin staff service, entertainment, food, ground service, seat comfort, and value in a single view consolidates critical information, making it easier for viewers to grasp the overall performance briefly. By summarizing the key metrics in one place, viewers can quickly identify strengths and areas for improvement across different parameters without needing to navigate multiple charts or dashboards.

By leveraging KPIs in this manner, the dashboard becomes a powerful tool for delivering actionable insights and enhancing the overall user experience.

Conclusion:

I extracted British Airways review data from Kaggle, covering the years 2016 to 2023, and analyzed it to derive actionable insights. I created an interactive dashboard using Tableau, featuring various visualizations to display the average ratings and number of reviews.

  Showcased regional variations in customer satisfaction with average ratings and review counts across different locations. Illustrated parameter-specific performance for different aircraft types, highlighting strengths and areas for improvement. Displayed monthly trends in average ratings for all parameters, helping to identify trends, patterns, and anomalies. Provided a comprehensive analysis of customer feedback, identifying areas for improvement and strengths to maintain.

I hope this blog post inspires you to embark on your own data analysis projects. Whether you’re analyzing customer reviews or exploring other datasets, the possibilities are endless with the right tools and techniques. Happy Reading.