British Airways Customer Booking Prediction: Practical Application of Machine Learning in the Aviation Industry

Project Source: Original author Nasim Ansari, published on GitHub (Link: https://github.com/NasimAnsari06/British-Airways-Customer-Booking-Prediction) Core Content: Based on real customer data from British Airways, this project uses machine learning techniques like Random Forest to build a customer booking behavior prediction model, identify high-intent customers, and provide actionable business optimization recommendations through feature importance analysis. Value: Helps airlines improve booking conversion rates, optimize marketing resource allocation, and achieve precision marketing and personalized services.

The aviation industry is highly competitive with high customer acquisition costs. Identifying high-intent customers and improving booking conversion rates are core challenges. Traditional analysis relies on empirical rules and struggles to capture complex customer behavior patterns. With the massive customer interaction data accumulated through digitalization (browsing, search, membership information, etc.), machine learning techniques can predict customer intent by learning historical patterns, providing solutions for precision marketing.

Prediction Task: Based on customer search and interaction behavior, predict whether a flight booking will be completed (binary classification problem: 1 = booked, 0 = not booked). Business Value:

• Targeted marketing resource allocation to improve ad ROI;
• Provide personalized offers to high-intent customers to accelerate conversion;
• Optimize customer service resource allocation to prioritize high-value potential customers;
• Identify churn risks and retain customers in a timely manner.

Dataset Overview: Includes features such as customer profile (age, membership level), search behavior (route, date, cabin class), interaction behavior (number of visits, stay time), and historical behavior (past bookings, cancellation records). EDA Insights:

• Booking behavior has seasonal fluctuations, with higher conversion rates in peak seasons;
• Conversion rates vary significantly across routes, with different patterns for business/holiday routes;
• There is an 'optimal window' for advance booking days (2-4 weeks);
• Membership level is positively correlated with conversion rate. Preprocessing Strategy: Fill missing values with median/mode + missing indicator; use target encoding + one-hot encoding for categorical features; construct derived features (decision speed, consumption consistency, etc.); handle outliers.

Reasons for Choosing Random Forest:

• Strong interpretability; feature importance can be converted into business insights;
• Natively supports categorical features without complex encoding;
• Robust to outliers and noise;
• Can capture non-linear relationships and interaction effects. Training and Tuning:
• Split training/test sets by time to avoid data leakage;
• Grid search + cross-validation to tune hyperparameters;
• SMOTE oversampling + class weight adjustment to handle imbalanced data.

Key Driving Factors:

1. Advance booking days (golden window: 2-4 weeks);
2. Membership level (higher conversion rate for premium members);
3. Search route features (strong intent for business/popular tourist routes);
4. Historical interaction behavior (past bookings, visit frequency);
5. Price sensitivity indicators. Business Recommendations:

• Dynamic marketing: Offer discounts to customers in the golden window;
• Membership benefit optimization: Enrich benefits to improve conversion rates;
• Route differential pricing: Adjust strategies based on conversion features;
• Customer tiered service: Provide differentiated experiences based on predicted probabilities;
• Retention strategy: Follow up with high-intent non-converted customers.

Evaluation Metrics:

• High accuracy and recall on the test set, balancing the identification of potential customers;
• AUC score shows good discrimination ability;
• Calculate business value (save advertising costs, improve conversion rates);
• Calibration analysis ensures that predicted probabilities truly reflect conversion possibilities.

Project Summary: This project demonstrates the end-to-end application of machine learning in customer analysis for the aviation industry, emphasizing business interpretability and practical value, and is an excellent reference case in the field of customer analysis. Limitations: Data timeliness needs regular updates; lacks external features such as competitor prices and social media sentiment; currently an offline analysis, real-time deployment needs to be considered. Improvement Directions: Explore XGBoost/LightGBM and neural networks; sequence modeling (RNN/Transformer) to capture behavior evolution; causal inference to analyze strategy effects; establish an A/B testing framework to optimize strategies.