Exploring Airline Passenger Satisfaction: A Data Analytics Project
Written on
This project centers around the evaluation of passenger satisfaction within the airline industry, an essential undertaking for those new to data analytics.
Let’s get started!
The dataset utilized for this analysis originates from Maven Analytics, which can be accessed via the following link:
Free Data Sets & Dataset Samples | Maven Analytics
Develop your data analytics skillset with our free data sets using real-world data, including flight delays and more. mavenanalytics.io
The dataset comprises satisfaction scores from over 120,000 airline passengers, alongside supplementary details regarding each traveler, their flight, and travel type. It also includes evaluations of various factors such as cleanliness, comfort, service, and overall experience.
The platform has suggested some analyses, and I intend to extract insights based on those recommendations. The data analysis process includes six key phases:
- Ask — Define the business task.
- Prepare — Collect and store the data.
- Process — Clean and preprocess the data.
- Analyze — Examine the data to identify trends and patterns.
- Share — Communicate findings to stakeholders.
- Act — Provide recommendations based on insights.
Ask
The insights can be derived from the suggested analysis questions:
- What percentage of airline passengers report satisfaction? Does this vary by customer type or travel type?
- What characterizes a frequent airline passenger?
- How does flight distance influence customer preferences or patterns?
- Which factors have the most significant impact on customer satisfaction? Conversely, what leads to dissatisfaction?
Prepare
I downloaded the Airline Passenger Satisfaction dataset from Maven Analytics in .csv format and imported it into Excel using Power Query.
Steps to Load Data with Power Query:
- Navigate to the “Data” tab on the Excel Ribbon.
- Select “From Text/CSV.”
- Choose the dataset and click “Import.”
- The Power Query editor will appear; click “Transform Data” if cleaning is needed. Otherwise, select “Load Data” to proceed.
Process
Cleaning and transforming data is crucial in the data analysis workflow. Working with unclean data can lead to misleading conclusions, so it's vital to ensure data integrity before analysis.
- Removed duplicates.
- Eliminated errors.
- Cleared null values from the “Arrival Delay” column.
- Introduced a conditional column for “Age Group.”
- Added a conditional column for “Flight Distance.”
- Verified the data type of each column.
- Finally, clicked “Close & Load” to transfer the data to a new worksheet in Excel.
Fortunately, the dataset required minimal cleaning, and all columns are relevant for analysis. The table now consists of 24 fields and 129,488 rows.
Analyze
Let’s explore our analysis questions!
1. What percentage of airline passengers are satisfied? Does it vary by customer type or travel type?
To answer this, we will create a pivot table in Excel:
- Click “Insert” > “Pivot Table” > Select Table/Range > OK. Choose New Worksheet for clarity.
After summarizing the data with a pivot table, we can create a visualization chart. Various chart options are available in the “Insert” tab.
The analysis reveals that 43.82% of passengers express satisfaction with the airline. Satisfaction levels differ among customer types, with returning business class passengers reporting a higher satisfaction rate of approximately 36.01%.
2. What characterizes a frequent airline passenger?
To identify the profile of returning passengers, we will focus on those labeled as “Returning” in the “Customer Type” column and analyze their demographics and satisfaction levels. This includes examining attributes such as Gender, Age, Class, and overall Satisfaction.
#### Gender Distribution of Returning Passengers
The data indicates that returning female passengers number around 52,899, slightly outpacing male passengers at 52,874.
#### Age Distribution of Returning Passengers
Individuals aged 30-60 are the most frequent flyers compared to other age groups.
#### Class Distribution of Returning Passengers
Returning passengers show a preference for Business Class over Economy and Economy Plus.
#### Overall Satisfaction of Returning Passengers
Approximately 55,199 returning passengers reported dissatisfaction, while 50,574 expressed satisfaction with their airline experience.
3. How does flight distance affect customer preferences or patterns?
Next, we will examine the impact of flight distance on customer preferences.
#### Flight Distance vs. Satisfaction
The data suggests a correlation between flight distance and satisfaction levels, indicating that satisfaction tends to increase with greater distances.
#### Flight Distance vs. Classes
This visualization shows significant variations in flight distance preferences across travel classes, indicating that customer choices may be influenced by their class of travel, particularly for longer distances where Business Class is favored.
These findings highlight the influence of flight distance on customer satisfaction and travel preferences.
4. What factors significantly impact customer satisfaction and dissatisfaction?
To determine the key contributors to satisfaction and dissatisfaction, we will evaluate various factors such as departure delays, arrival delays, and seat comfort, calculating the average for each factor and categorizing satisfaction levels into Satisfied and Neutral/Dissatisfied.
The primary factors affecting both satisfaction and dissatisfaction are departure and arrival delays. Passengers generally prefer timely travel, and delays lead to increased dissatisfaction, with delays averaging 16.34 and 17.06 for departure and arrival respectively, compared to 12.44 and 12.53 for satisfaction.
Act
In this phase, we provide actionable recommendations to stakeholders based on our analysis:
- Enhance the Long-Distance Travel Experience: With heightened dissatisfaction among long-distance travelers, identifying and addressing specific pain points is crucial. Improvements in seating, in-flight entertainment, and meal quality should be prioritized.
- Improve Services with Low Satisfaction Ratings: The dashboard highlights services that require attention. Conducting a detailed analysis of these areas can lead to significant improvements in overall passenger satisfaction.
- Strengthen Loyalty Programs for Returning Passengers: As returning passengers exhibit higher satisfaction levels, enhancing loyalty initiatives can boost retention and overall satisfaction.
- Cater to Key Age Groups: The majority of returning passengers are aged 30-49. Tailoring services and amenities to suit this demographic may enhance satisfaction and loyalty.
- Differentiate Services for Personal and Business Travelers: With a substantial portion of passengers traveling for business, developing specialized services like expedited check-in and lounge access could significantly improve their experience.
- Customize Offerings for Business Class Passengers: Given the significant number of long-distance travelers in Business Class, tailoring services to meet their needs can elevate their travel experience and satisfaction.
Voila!
Thank you for taking the time to read through this analysis! Your feedback and suggestions are welcome. Enjoy your data journey!
I am a freelance data analyst with expertise in Excel, SQL, Tableau, Power BI, and R, specializing in creating impactful dashboards and presentations. Feel free to reach out at [email protected].
The views expressed in external submissions do not necessarily reflect the opinions or work of Maven Analytics or its team members.
We promote lifelong learning and aim to provide a platform for the data community to share their work and receive feedback from the Maven Analytics family.
Interested in contributing? Submit your writing here!
Happy learning!
- Team Maven