Predicting Customer Satisfaction in Airlines
Predicting airline customer satisfaction to enhance travel experiences and service quality.
1Overview & Strategic Importance
Problem Statement
Customer satisfaction is a critical measure of an airline's success, driving loyalty and maintaining a competitive edge. Predicting satisfaction is complex due to factors like service quality, flight experience, and passenger demographics. Solving this allows airlines to proactively address pain points and design targeted improvements for long-term growth.
Required Solutions
- Developing a classification model to predict passenger satisfaction.
- Identifying key drivers of satisfaction (e.g., Seat Comfort, On-board Service).
- Reducing dissatisfaction through data-driven service strategies.
Solution Objectives
- Uncover patterns in passenger feedback and travel data.
- Develop high-accuracy classification models.
- Conduct scenario analysis on flight experience variables.
- Optimize the model for operational airline application.
Understanding the Problem
Satisfaction is shaped by service quality and passenger expectations.
On-time performance and staff behavior are critical. AI models help identify these relationships to take proactive measures in service delivery and policy design.
2About the Data
Data Collection
Consolidated passenger feedback data from Invistico Airlines, including flight details and survey responses on various service contexts.
Major Parameters Description
Download Training DataGenderGender of the passenger (Male/Female).
Customer TypeType of customer (Loyal Customer/Disloyal Customer).
AgeAge of the passenger (years).
Type of TravelPurpose of travel (Business Travel/Personal Travel).
ClassTravel class (Business/Eco/Eco Plus).
Flight DistanceDistance of the flight in miles.
Seat comfortRating of seat comfort (0: Poor to 5: Excellent).
Departure/Arrival time convenientConvenience of departure and arrival times (0 to 5).
Food and drinkRating of food and drink quality (0 to 5).
3Using iDareAI
Guided Mode Initialization
AUploading Dataset
Click on the **'Upload CSV or Excel Data'** button → Select a source for the dataset → Upload `airline_customer_satisfaction_train.csv`. The system automatically analyzes the file, extracts column descriptions, and identifies the top value-adding targets for prediction.
BChoosing Analysis Mode
- What factors most influence customer satisfaction in airlines?
- How does seat comfort or service quality affect satisfaction predictions?
Operation Using Autonomous Guided Mode
AQuery Response
Key factors include 'Inflight entertainment', 'Seat comfort', and 'Ease of Online booking'. Improving these areas directly leads to higher satisfaction levels.
BAI Application
The system provides a dynamic interface to adjust variables like seat comfort and see their impact on passenger satisfaction predictions in real-time.
Model Fine-Tuning/Manual Model Building
ASelecting Prediction Target
'Satisfaction' column was selected as the target.
BSelecting Analysis Type
The analysis target is a categorical column. Hence, the 'Classification' analysis type is selected.
CSelecting Model Group/Item
DSelecting Features
Selected features including Gender, Customer Type, Age, Seat Comfort, and Cleanliness for the analysis.
ESelecting Training Level
The "Slow" training level with "High Performance" configuration was used for maximum accuracy in predicting passenger sentiment.
AI Modeling Details
Xtreme Gradient Boosting (XGB) performed best with a test accuracy of 95% and an F1 score of 92%.
Training Analysis Details
APredicted Target
BROC AUC
CError Trend
DFeature Importance
Finalize Models
Train until accuracy is optimal and then click 'Deploy' to start using your AI Application for passenger satisfaction monitoring.
4AI APPLICATION
Manual Model Building
In the manual application, users can modify sliders for variables like 'Seat Comfort' and 'Flight Distance' to see real-time impact on predicted passenger sentiment.
AI Application Demo
By increasing the 'Seat Comfort' slider, the prediction target dynamically shifts from 'Dissatisfied' to 'Satisfied', demonstrating the direct impact of service quality.
Saving the Project
Save your analysis project by clicking the icon at the bottom left corner of the interface.
Sharing the Project
Generate a shareable link to distribute your findings and application for single on-demand predictions.
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