Sun Country Airlines: Customer Segmentation and Marketing Strategy

This project leverages K-Means clustering to segment customers of Sun Country Airlines and generate targeted marketing strategies based on behavior, spending, booking channels, and loyalty program participation.

Team Contribution

Worked as part of a team to perform:

• Data preprocessing on over 15,000 rows and 90 features
• K-Means clustering using the Elbow method to find optimal segments
• Marketing recommendations tailored to each customer cluster

Tools & Libraries

• Python: pandas, scikit-learn, matplotlib
• Google Colab for collaborative model development
• K-Means for segmentation
• Data Visualization for cluster insight communication

Key Insights

• Identified 5 distinct market segments including "The Honeymooners", "Solo Adventurers", and "Last Minute Savers"
• Proposed personalized promotions, bundle strategies, loyalty incentives, and UX improvements
• Enhanced business understanding of price sensitivity and seasonal behavior

Files Included

• Project.ipynb: Full code notebook for preprocessing, clustering, and visualization
• Sun Country Airlines Report: Detailed business recommendations and strategic report

Impact

Helped define actionable loyalty and pricing strategies based on cluster-specific traits, providing a data-driven foundation for personalized marketing.