Practical Case of Airline Customer Churn Prediction: How the SkyInsight Project Achieved 99.5% ROC-AUC

In the highly competitive airline industry, customer loyalty directly determines the survival of enterprises. As an end-to-end machine learning solution, the SkyInsight project uses the XGBoost model to achieve 96.1% accuracy and 99.5% ROC-AUC, transforming passive satisfaction surveys into an active customer retention engine. It accurately identifies hidden churn risks and supports real-time interventions. This article will analyze the project's technical architecture, business insights, and implementation practices.

The airline industry faces a paradox: 82% of passengers are high-value loyal customers, but nearly 31% have silent dissatisfaction, posing hidden churn risks. From a financial perspective, the cost of retaining existing customers is only 1/5 to 1/7 of acquiring new ones, so precise intervention is a cost-effective investment. The core goal of the project is to shift from "post-hoc analysis" to "real-time intervention" and take action before customers leave.

The project was trained on over 130,000 historical passenger survey data, covering dimensions such as in-flight experience, digital experience, ground services, and flight reliability. Three baseline models were compared:

Model	Overall Accuracy	Precision	Recall	F1 Score	ROC-AUC
XGBoost (Champion)	96.1%	97.1%	95.7%	96.4%	99.5%
Random Forest	96.0%	96.9%	95.6%	96.3%	99.4%
Logistic Regression	83.5%	84.6%	85.0%	84.8%	90.9%

XGBoost won with high precision (reducing false positives) and high recall (capturing at-risk customers), becoming the production model.

Four Priority Findings:

1. In-flight comfort (54% weight): Entertainment systems and seats are essential for business travelers; malfunctions completely damage loyalty
2. Digital experience (25% weight): Seamless online experience is a basic expectation of passengers in the digital age
3. Airport and crew services (13% weight): Opportunities for brand differentiation
4. Flight reliability (8% weight): Affected by gate location and convenience of takeoff/landing

Threshold Effects:

• Four-star rule: 3-star service perception is as negative as 1-star; only 4-5 stars trigger retention
• Delay red line: 15 minutes is the psychological line; after 120 minutes, dissatisfaction rate reaches 63% and remains high

These findings provide clear intervention points for operational decisions.

Tech Stack: Python, Pandas, Scikit-learn, XGBoost (modeling); Joblib (model persistence); Streamlit (interactive web application); Pyngrok (secure remote access)

Production Deployment: The Streamlit application supports real-time inference; inputting passenger parameters outputs churn risk levels, facilitating immediate intervention

Model Reliability: A 99.5% ROC-AUC indicates excellent differentiation ability. The threshold can be flexibly adjusted to balance precision and recall, providing reliable confidence for decision-making.

Implementation Recommendations:

• Prioritize data quality: Ensure full coverage of the customer journey and avoid sampling bias
• Focus on silent dissatisfied customers: Optimize identification of customers who never complain but will leave
• Threshold intervention: Concentrate resources on nodes like 15-minute delays and 3-star experiences
• Dynamic threshold adjustment: Adjust classification thresholds based on business goals
• A/B test validation: Verify actual business value before promotion

Industry Insights:

• From description to prediction: Move beyond statistics to predictive models
• From average to individual: From group analysis to individual risk scoring
• From post-hoc to real-time: Shorten from quarterly cycles to event real-time
• From intuition to data: Replace subjective judgment with data-driven decisions

The methodology can be migrated to industries focusing on retention, such as hotels, banking, and telecommunications.

The SkyInsight project proves that machine learning can solve practical business problems, transforming abstract "customer satisfaction" into actionable retention strategies. Its achievements of 96.1% accuracy and 99.5% ROC-AUC provide a complete reference for similar systems from data preparation, model training to deployment. More importantly, the project translates technical results into business language such as the "Four-star Rule" and "Delay Red Line", helping non-technical decision-makers understand and support data-driven improvements.