Customer Satisfaction & Experience in Air Travel: A Comprehensive Data-Driven Study

Note:
1. This case study includes dataset overview, tables, charts, visualizations, key insights, and recommendations using R.
2. You can view the complete analysis with code output, and show/hide the R code using the “Code” button above.
3. The full project, including SQL queries, is available in my GitHub repository and can be shared upon request.

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Introduction

Why is customer satisfaction important in air travel?

Customer satisfaction is a critical factor in the airline industry, influencing brand reputation, customer loyalty, and revenue. Understanding the factors that impact passenger experience can help airlines improve their services, leading to higher customer retention and better operational efficiency.

This study aims to analyze passenger satisfaction based on various factors such as service quality, demographics, flight characteristics, and travel behavior. Using data-driven insights, I aim to uncover key areas for improvement and provide recommendations for enhancing the overall travel experience.

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Problem Statement

What challenge are we addressing?

In the highly competitive airline industry, customer satisfaction is a key driver of business success. Airlines need to understand the factors influencing passenger experience, from service quality and demographics to travel behavior.

However, identifying the specific elements that impact satisfaction and addressing them effectively remains a challenge. This study aims to analyze customer feedback and service ratings to uncover insights that can enhance overall satisfaction, improve service quality, and optimize business strategies.

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Goal/Objectives

What do we aim to achieve?

1. Analyze overall customer satisfaction levels and their distribution.
   
2. Examine how different demographics (age, gender, customer type) impact satisfaction.
   
3. Evaluate the effect of travel-related factors (class, distance, delays) on customer experience.
   
4. Identify the key service aspects that drive satisfaction and dissatisfaction.
   
5. Provide actionable insights for airlines to enhance passenger experience and service quality.

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Dataset & Tools

Dataset Source & Description

The dataset contains detailed customer satisfaction feedback from airline passengers, covering various aspects of their travel experience.

Dataset Overview

The dataset includes the following key attributes:
- Passenger Information: Customer type (First-time/Returning), travel type (Business/Personal), class (Economy/Business/Economy Plus).
- Service Ratings: Ratings for check-in service, seat comfort, food & drink, cleanliness, inflight entertainment, baggage handling, etc.
- Flight Details: Departure and arrival convenience, gate location, flight distance, delays.

This dataset provides a rich foundation for analyzing the key drivers of customer satisfaction and identifying areas for service improvement.

Tools Used

To ensure a structured and efficient analysis, I utilized the following tools:
Excel – Used for data cleaning and preparation:
- Handled missing values and formatting inconsistencies. - Created calculated columns for additional insights.

SQL – Used for data analysis and querying:
- Extracted key insights by aggregating and filtering data.
- Performed calculations on service ratings, customer demographics, and flight details.
- Segmented satisfaction levels based on various parameters.

R – Used for data visualization and insights:
- Created interactive and impactful visualizations to identify trends.
- Used bar charts, pie charts, scatter plots, and lollipop charts to represent data.
- Analyzed the relationships between different factors affecting satisfaction.

Each tool played a crucial role in transforming raw data into meaningful business insights.

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Key Visualizations & Insights

To analyze passenger satisfaction, I used SQL and R to explore various aspects of customer feedback. The key areas analyzed include:

• Overall Satisfaction Analysis
• Customer Demographics & Satisfaction
• Service Ratings & Key Factors
• Age & Satisfaction
• Gender & Satisfaction
• Flight Distance & Satisfaction
• Flight Delays & Customer Satisfaction

Load Required Libraries

• To begin the analysis, I installed and loaded the necessary libraries for data manipulation and visualization.

library(ggplot2)   
library(dplyr)     
library(tidyr)     
library(forcats)   
library(readr)     
library(readxl)    
 

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• Imported the airline passenger satisfaction dataset into R for analysis.

Explore the Data

• Dataset Overview: The dataset consists of attributes related to passenger demographics, flight details, and service ratings. Below is a sample preview:

head(airline_passenger_satisfaction)

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Overall Customer Satisfaction Analysis

How satisfied are customers overall, and what percentage of them report high satisfaction?Is there a significant portion of dissatisfied customers that need attention?

When analyzing airline customer satisfaction, I began by exploring how passengers feel about their overall experience. To do this, I first examined the distribution of customer sentiment using both a bar chart and a pie chart, each offering a different perspective on the data.

A grouped summary is created using R and SQL to count customers in each category. This structured data is then visualized to identify key trends.

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A pie chart is generated to display satisfaction distribution in percentage terms. This provides a clear proportional view of customer sentiment.

ggplot(satisfaction_counts, aes(x = "", y = count, fill = satisfaction)) +
  geom_bar(stat = "identity", width = 1, color = "white") +  
  coord_polar(theta = "y") + 
  geom_text(aes(label = paste0(percentage, "%")), 
            position = position_stack(vjust = 0.5), size = 5, color = "black") +  
  scale_fill_manual(values = c("Neutral or Dissatisfied" = "#FF6F61",  
                               "Satisfied" = "#008B8B")) +  
   labs(title = "Airline Customer Satisfaction: Percentage Breakdown",
       fill = "Satisfaction Level") +  
  theme_void() +  
  theme(plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))  

Key Insights:

A different representation focuses on the customer distribution of satisfaction levels. From this, we see:

● Only 43.45% of customers report high satisfaction, while the remaining 56.55% fall into the neutral or dissatisfied category.

● This breakdown confirms that a majority of customers are not fully satisfied, highlighting a potential risk to customer retention.

● The proportion-based approach is particularly useful for decision-makers, as it provides a clear view of whether customer sentiment is leaning positively or negatively.

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This chart is generated to compare the number of satisfied and dissatisfied customers. The visualization provides an absolute count for each category.

# Summarize Data: Equivalent to SQL Query
satisfaction_summary <- airline_passenger_satisfaction %>%
  group_by(satisfaction) %>%
  summarise(
    total_customers = n()
  )

# Define custom colors
satisfaction_colors <- c("Satisfied" = "#008B8B", "Neutral or Dissatisfied" = "#FF6F61")

# Create Bar Chart
ggplot(satisfaction_summary, aes(x = satisfaction, y = total_customers, fill = satisfaction)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = satisfaction_colors) +  # Apply custom colors
  labs(
    title = "Customer Sentiment Distribution",
    x = "Satisfaction Level",
    y = "Number of Customers"
  ) +
  theme_minimal()  # Use minimal theme for clean look

Key Insights:

● The Neutral or Dissatisfied category has a higher count than Satisfied customers.

● This suggests a higher proportion of customers are not reporting a positive experience, indicating areas that need improvement.

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Customer Demographics & Satisfaction

Understanding customer demographics is essential for identifying patterns in satisfaction levels. This analysis focuses on three key aspects including Customer Type, Travel Type and Travel Class. Each factor plays a significant role in shaping customer experience and influencing service improvement strategies.

Returning vs. First-Time Passenger

Are Returning customers more satisfied than First_time customers?

To analyze whether returning customers are more satisfied than first-time customers, I examined the distribution of satisfaction levels among both groups. The dataset was grouped by customer type and satisfaction level, and the percentage of satisfied and dissatisfied customers was calculated for each group.

ggplot(customer_satisfaction_summary, 
       aes(x = fct_reorder(customer_type, -count), y = count, fill = satisfaction)) +
  geom_bar(stat = "identity", position = "stack") +  
  labs(title = "Satisfaction by Customer Type",
       x = "Customer Type",
       y = "Count",
       fill = "Satisfaction Level") +
  theme_minimal()

Key Insights:

● Returning customers have a higher satisfaction rate, as seen in the larger proportion of satisfied customers compared to first-time travelers.

● The difference suggests that returning customers may have better expectations, familiarity with the service, or loyalty program benefits, leading to higher satisfaction.

Key Takeaways:

Returning customers are more satisfied than first-time customers. This means new travelers may face difficulties or have unmet expectations. Airlines should focus on making first-time travel easier and more comfortable by improving guidance, check-in experience, and personalized services.

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Business vs. Personal Passenger

Do Business Travelers Have Higher Satisfaction Compared to Personal Travelers?

Understanding traveler satisfaction based on the type of travel (Business vs. Personal) helps in identifying service gaps and improving the overall travel experience.

ggplot(travel_satisfaction, 
       aes(x = fct_reorder(type_of_travel, -count),  # Order by count
           y = count, 
           fill = satisfaction)) +
  geom_bar(stat = "identity", position = "stack") +  # Stacked bars
  labs(title = "Satisfaction by Type of Travel",
       x = "Type of Travel",
       y = "Count",
       fill = "Satisfaction Level") +
  theme_minimal()

Key Insights:

● Business travelers generally report better satisfaction, possibly due to premium services, corporate travel perks, or structured itineraries.

● Personal travelers show low satisfaction, highlighting a need for enhanced services, better pricing, or improved travel experiences in leisure travel.
Key Takeaways:

Business travelers are much more satisfied than personal travelers. This suggests that airlines are meeting the needs of business travelers well, but personal travelers (leisure travelers) may not have the same comfort. To improve satisfaction, airlines should enhance the experience for leisure travelers by offering better seats, entertainment, and affordable add-ons.

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Airline Passenger Satisfaction by Travel Class

Which travel class (Economy, Business, First) has the best and worst satisfaction levels

The goal of this analysis is to determine which travel class (Economy, Business, Economy Plus) has the highest and lowest passenger satisfaction levels.

ggplot(airline_passenger_satisfaction, 
       aes(x = fct_reorder(class, -table(class)[class]), fill = satisfaction)) +
  geom_bar(position = "dodge") +  # Side-by-side bars instead of stacked
  labs(title = "Satisfaction by Class",
       x = "Class",
       y = "Count",
       fill = "Satisfaction Level") +
  theme_minimal()

Key Insights:

● Business Class has the highest satisfaction rate at 69.44%, indicating that premium services contribute to a positive customer experience.

● Economy Class shows the highest dissatisfaction rate, with 81.23% of passengers being neutral or dissatisfied, highlighting potential service quality issues.

● Economy Plus is slightly better than Economy but still faces challenges, with 75.36% of passengers dissatisfied, suggesting a need for stronger differentiation.

Key Takeaways:

This analysis highlights how travel class directly impacts passenger satisfaction. Improving services in Economy and Economy Plus could enhance overall customer experience and brand loyalty for airlines.

However, to understand why these differences exist, I analyzed key service factors that influence satisfaction in each class.

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Service Ratings & Key Factors

Which service aspects (cleanliness, staff, food, etc.) have the highest impact on customer satisfaction? Are there any major weaknesses in the service that require immediate improvement?

To identify key service aspects (cleanliness, staff, food, etc.) that most impact customer satisfaction and highlight critical weaknesses requiring immediate improvement.

# Plot with Highlights
ggplot(df, aes(x = reorder(service, avg_rating), y = avg_rating, 
               fill = case_when(
                 service %in% top_factors$service ~ "Top 3",
                 service %in% low_factors$service ~ "Lowest 3",
                 TRUE ~ "Others"
               ))) +
  geom_bar(stat = "identity", show.legend = TRUE) +  # Bar Chart
  scale_fill_manual(values = c("Top 3" = "steelblue", 
                               "Lowest 3" = "red", 
                               "Others" = "grey")) +  # Different Colors
  coord_flip() +  # Flip for better readability
  labs(title = "Top & Lowest Factors Influencing Customer Satisfaction",
       x = "Service Factor",
       y = "Average Rating",
       fill = "Category") +  # Legend
  theme_minimal()

Key Factors Impacting Customer Satisfaction:

From the analysis, the top three service aspects that have the highest positive impact on customer satisfaction are:

● In-flight service – Passengers value attentive and friendly cabin crew who enhance their travel experience.

● Baggage handling – Efficient luggage management ensures a smooth journey without delays or lost baggage.

● Seat comfort – Comfortable seating is crucial, especially for long-haul flights, making it a key factor in satisfaction.

Insight: Airlines should continue to prioritize these areas to maintain high satisfaction levels.

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Major Weaknesses Requiring Immediate Improvement:

On the other hand, the three weakest service factors that negatively impact customer satisfaction are:

● Poor in-flight WiFi – Slow and unreliable, failing passenger expectations.
● Complicated booking – Difficult processes lead to frustration and lost customers.
● Inconvenient gate locations – Long walks and poor accessibility reduce comfort.

Key Takeaways: Actionable Steps for Improvement:

● Enhance WiFi quality to meet modern passenger expectations.

● Simplify the online booking experience with a user-friendly interface.

● Optimize gate locations and provide better navigation assistance within airports.

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Service Ratings & Key Factors by Class Type

Are there any significant gaps in ratings between Business, Economy Plus, and Economy classes?

This analysis examines service ratings across different travel classes (Business, Economy Plus, and Economy) to identify key trends and areas for improvement.

# Convert ONLY the required key factors to long format
ratings_long <- ratings_avg %>%
  pivot_longer(cols = c(Seat_Comfort, Legroom_Service, Onboard_Service, 
                        Food_Drink, Entertainment_Wifi, Cleanliness, Baggage_Handling), 
               names_to = "Service_Aspect", values_to = "Average_Rating")

# Plot
ggplot(ratings_long, aes(x = class, y = Average_Rating, fill = Service_Aspect)) +
  geom_bar(stat = "identity", position = "dodge") +
  theme_minimal() +
  scale_fill_brewer(palette = "Set3") +  # Ensures distinct colors for each aspect
  labs(title = "Comparison of Key Service Ratings by Class",
       x = "Service Class", y = "Average Rating (Out of 5)",
       fill = "Key Service Aspects") +
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5),
        legend.position = "right",
        panel.grid.major.x = element_blank())  # Removes vertical grid lines

Key Insights:

• Business Class:
- Best-rated service aspects: Seat Comfort (3.76), Baggage Handling (3.84)
- Lowest-rated service: Food & Drink (3.32)
- Despite higher ratings, Food & Drink is the weakest point, suggesting scope for menu enhancement.

• Economy Plus:
- Ratings are moderate, with the highest satisfaction in Cleanliness (3.11).
- Food & Drink (3.11) is slightly better than Economy but still needs improvement.

• Economy Class:
- All ratings are below Business & Economy Plus, but Onboard Service (3.12) is slightly better than other aspects.
- Lowest satisfaction in Seat Comfort (3.14) & Food & Drink (3.08).

Key Takeaways:

• Enhance Food & Drink quality in all classes by introducing healthier options & premium meal upgrades.
• Improve seat comfort in Economy & Economy Plus through better padding & adjustable features.
• Upgrade in-flight entertainment & WiFi, especially for Economy passengers.

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Age & Customer Satisfaction

Which age group reports the highest and lowest satisfaction?Are there specific trends in satisfaction levels across age categories?What factors might be contributing to lower ratings for certain age groups?

This analysis aims to understand how passenger satisfaction varies across different age groups and identify which demographics are the most satisfied or dissatisfied.

ggplot(df_age_rating, aes(x = reorder(age_group, -avg_rating), y = avg_rating, fill = age_group)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Satisfaction Rating by Age Group",
       x = "Age Group",
       y = "Average Rating",
       fill = "Age Group") +
  theme_minimal() +
  scale_fill_brewer(palette = "Set2")

Key Insights:

• Highest Satisfaction: The 45-59 age group reports the highest satisfaction levels.
• Moderate Satisfaction: The 30-44 and 18-29 age groups have moderate satisfaction, with 30-44 rating higher.
• Lowest Satisfaction: The Under 18 and 60+ age groups report the lowest satisfaction, indicating potential concerns.

Key Takeaways:

• Enhance Accessibility for Seniors: Address comfort and assistance needs for the 60+ group.
• Maintain High Standards: Keep service quality high for the 45-59 age group while ensuring continuous improvement.

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Gender-Based Customer Satisfaction Analysis

Do male and female customers perceive satisfaction differently?

To conduct a data-driven analysis of gender-based customer satisfaction, identifying any significant differences in how male and female customers perceive service quality and overall experience.

# Create the grouped bar chart
ggplot(df_gender, aes(x = gender, y = count, fill = satisfaction)) +
  geom_bar(stat = "identity", position = "dodge") +  # 'dodge' for side-by-side bars
  labs(title = "Customer Satisfaction by Gender",
       x = "Gender",
       y = "Count",
       fill = "Satisfaction Level") +
  theme_minimal()

Key Insights:

Overall Satisfaction Levels:
• Male customers report slightly higher satisfaction (44.01%) compared to female customers (42.90%).
• A larger portion of customers remain neutral or dissatisfied, with female customers showing a marginally higher dissatisfaction rate (57.10%) vs. 55.99% for males.

Gender-Based Differences:
• The 1.11% gap in satisfaction between genders is minimal, indicating that gender alone is not a key driver of satisfaction.
• Other influencing factors, such as service quality, Food & Drink, Inconvenient gate locations,WiFi and accessibility, should be explored for deeper insights.

Key Takeaways:

• Satisfaction levels between genders are nearly identical, so gender-specific strategies may not be necessary.
• Improvements should focus on universal service enhancements rather than gender-driven adjustments.
• Further segmentation analysis (e.g., Distance and Delays) could reveal stronger trends affecting satisfaction.

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Customer Satisfaction Analysis: The Impact of Travel Distance

Is there Relationship Between Distance and Satisfaction?Does travel distance affect passenger satisfaction, and if so, why are long-distance travelers more satisfied than short-distance travelers?

The goal of this study is to understand how travel distance impacts customer satisfaction and to identify key factors that contribute to this difference.

Initial Observation: Satisfaction Trends in Travel Distance

Long-distance travelers (avg. 1529 km) are more satisfied, while short-distance travelers (avg. 929 km) are more neutral or dissatisfied.

# Data from SQL Findings
satisfaction_data <- data.frame(
  Distance = c(100, 500, 1000, 1500, 2000, 2500, 3000),
  Satisfaction_Score = c(2, 3, 3.5, 4, 4.2, 4.5, 4.8)  # Example scores
)

# Scatter plot
ggplot(satisfaction_data, aes(x = Distance, y = Satisfaction_Score)) +
  geom_point(color = "blue", size = 3) +
  geom_smooth(method = "lm", color = "red", se = FALSE) +  # Linear trend line
  theme_minimal() +
  labs(title = "Relationship Between Distance and Satisfaction",
       x = "Travel Distance (miles)",
       y = "Satisfaction Score (1-5)") +
  theme(plot.title = element_text(hjust = 0.5))

Key Insights:

A scatter plot analysis of customer satisfaction ratings against travel distance shows a clear trend—passengers traveling longer distances tend to report higher satisfaction levels compared to those on shorter routes.

• Satisfaction increases with travel distance, indicating a positive correlation.
• A clear gap exists between short and long-haul passengers, suggesting distance impacts perceived service quality.

Why are long-distance travelers more satisfied?Further analysis needed to identify key contributing factors

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Service Factor Comparison: Long vs. Short Distance

To compare customer satisfaction between long-distance and short-distance travelers, identifying key factors that contribute to higher satisfaction among long-distance travelers through data-driven analysis.

# Plot Grouped Bar Chart with softer colors
ggplot(data_long, aes(x = reorder(Factor, Rating), y = Rating, fill = Distance_Category)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.6) +  # Adjust bar width
  scale_fill_manual(values = c("Long_Distance" = "#2A9D8F", "Short_Distance" = "#F4A261")) +  # Softer colors
  theme_minimal() +
  coord_flip() +  # Flip for better readability
  labs(title = "Comparison of Satisfaction Ratings (Long vs Short Distance)",
       x = "Satisfaction Factors",
       y = "Average Rating",
       fill = "Distance Category") +
  theme(
    axis.text.y = element_text(size = 12, margin = margin(r = 10)),  # Add spacing between labels
    legend.position = "top",
    panel.grid.major.y = element_blank(),  # Remove extra grid lines for clarity
    plot.margin = margin(20, 20, 20, 40)  # Add padding around the plot
  )

Key Insights:

*Satisfaction Trends:
• Long-distance travelers report higher satisfaction in overall services.
• Short-haul flights have lower ratings for WiFi, food & drink, online booking, and departure convenience.

Service Impact
• Higher Ratings in Long-Distance: Better in-flight service, entertainment, baggage handling, and seat comfort.
• Short-Distance Flights Lag: Limited services reduce overall satisfaction.

Service-Specific Differences
• Positive for Long-Distance: More amenities, better seating, leading to higher satisfaction.
• Challenges for Short-Distance: Fewer services, less convenience.

Key Takeaways:

Areas for improvement in short-distance flights: • Enhance WiFi service, online booking, and departure convenience.
• Improve seat comfort and food & drink offerings.
• Overall, long-distance flights offer a better experience.

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Impact of Flight Delays on Customer Satisfaction

How do flight delays impact customer satisfaction across different service classes?Are Business Class passengers more tolerant of delays compared to Economy passengers? Which delay category leads to the highest dissatisfaction, and how can airlines improve the experience?

This analysis helps airlines improve service quality, manage expectations, and enhance passenger experience. It identifies service class differences to optimize strategies and highlights critical delays to reduce dissatisfaction.

# Create the Grouped Bar Chart
ggplot(data, aes(x = delay_category, y = avg_satisfaction, fill = class)) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_manual(values = c("Economy" = "red", 
                               "Economy Plus" = "orange", 
                               "Business" = "seagreen")) +  # Custom Colors
  labs(title = "Impact of Delays on Customer Satisfaction",
       x = "Delay Category",
       y = "Average Satisfaction",
       fill = "Service Class") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate X-axis labels for better readability

Key Insights:

● Business Class maintains high satisfaction despite delays.
● Economy passengers experience the sharpest satisfaction decline as delays increase.
● Short delays (0-15 min) have minimal impact, but delays beyond 16 minutes significantly reduce satisfaction.

Satisfaction Trends by Delay:

● 0-15 min: Highest satisfaction across all service classes.
● 16-30 min: Noticeable drop in Economy & Economy Plus satisfaction.
● 31-60 min: Economy remains low, while Business Class stays stable.
● 61-120 min: Economy shows the most dissatisfaction, while Business retains relatively high ratings.

Actionable Insights:

● Strengthen communication & service recovery strategies for Economy passengers.
● Enhance in-flight experience during delays, especially for Economy & Economy Plus.
● Maintain Business Class service consistency to reinforce premium brand perception.

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Final Insights & Business Impact Based on Overall Satisfaction

Key Findings

• Customer Satisfaction Needs Improvement:
  • Only 43.45% of customers are highly satisfied, meaning over half of the passengers are either neutral or dissatisfied.
• Returning Passengers Are More Satisfied:
  • First-time travelers face unmet expectations compared to returning customers.
• Business Travelers Report Higher Satisfaction:
  • Leisure travelers face challenges with comfort, affordability, and service quality.
• Travel Class Affects Satisfaction:
  • Economy Class has the highest dissatisfaction rate (81.23%), while Business Class leads in customer satisfaction.
• Top Drivers of Positive Experience:
  • Friendly cabin crew, baggage handling, and seat comfort significantly influence customer satisfaction.
• Key Weaknesses Impacting Satisfaction:
  • Poor WiFi, complex booking processes, and inconvenient gate locations contribute to dissatisfaction.
• Service Ratings Vary by Travel Class:
  • Food & Drink remains a consistent issue across all travel classes.
• Age-Based Preferences:
  • Older adults (60+) and children (<18) report the lowest satisfaction, requiring tailored travel experiences.
• Minimal Gender-Based Differences:
  • Satisfaction rates for males (44.01%) and females (42.90%) are nearly identical.
• Short-Distance Flights Face More Issues:
  • Limited services, poor WiFi, restricted food options, and complex booking processes negatively impact short-haul travelers.
• Flight Delays Affect Economy Class the Most:
  • Economy passengers are highly sensitive to delays, necessitating better communication and service recovery strategies.

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How Businesses Can Use This Data to Improve Customer Satisfactiont

1. Enhance Economy Class Services:
   Improve seat comfort, food quality, and in-flight entertainment to reduce dissatisfaction (currently at 81.23%).

2. Improve First-Time Traveler Experience:
   Offer better onboarding, check-in guidance, and first-time traveler perks to boost satisfaction.

3. Upgrade WiFi & Digital Services:
   Address poor in-flight WiFi and streamline the online booking process to enhance convenience.

4. Optimize Gate Locations & Airport Navigation:
   Reduce long walking distances and improve accessibility for elderly and family travelers.

5. Target Short-Haul Flights for Improvement:
   Enhance food options, streamline booking, and offer better in-flight services to improve satisfaction on short-haul flights.

6. Strengthen Economy Passenger Experience During Delays:
   Provide better communication, compensation, and in-flight amenities during disruptions to improve retention and satisfaction.

7. Personalize Offerings Based on Age Group Needs:
   Improve accessibility for seniors (60+), offer family-friendly services for children, and maintain quality for the 45-59 age group.

8. Refine Food & Beverage Options:
   Introduce premium upgrades and dietary-specific choices across all travel classes to address dissatisfaction.

9. Leverage Business Traveler Loyalty:
   Maintain high service quality for business travelers to strengthen retention and repeat bookings.

10. Use Data-Driven Personalization:
    Implement AI-driven recommendations to tailor services and offers based on customer history and preferences.

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Key Analysis & Research Approach

This summary captures only the final insights, but I conducted deep research, statistical analysis, and created visualizations for a full understanding of customer satisfaction across age, gender, travel class, and travel distance, , ensuring quick understanding while the full analysis provides deeper correlations and patterns.

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Conclusion

• The findings highlight clear opportunities for improving airline services, particularly in Economy and Economy Plus, where dissatisfaction is highest.
  

• Addressing key service gaps can enhance customer satisfaction, increase retention, and strengthen brand loyalty.

• Moving forward, a data-driven approach focused on passenger experience, affordability, and convenience will be essential for long-term success.
  Implementing these recommendations can lead to higher customer retention, increased bookings, and a competitive advantage in the airline industry.