Overview:
This project analyzes travel booking data to uncover insights about family and non-family travelers. Using Python, I explored differences in booking behaviors, revenue patterns, and planning habits. The results highlight actionable strategies that could help travel companies, like Expedia, enhance customer experiences and drive growth.
Key Objectives:
- Understand booking patterns for family and non-family travelers.
- Explore monthly trends in net orders and gross booking amounts.
- Provide actionable recommendations to optimize marketing and loyalty strategies.
Tools Used:
Programming Languages:
- Python: Used for data manipulation, analysis, and visualization.
Libraries:
- Pandas: To clean and manipulate the data.
- Matplotlib: For creating detailed, visually appealing charts.
- NumPy: For numerical operations and calculations.
Analysis and Insights:
- Monthly Net Orders and Gross Booking Amount:
- What the chart shows: Monthly trends in bookings and revenue from June 2018 to June 2019.
- Key Insight: Net orders increased by 14.7%, and gross booking revenue rose by 35%. This indicates growing travel demand, particularly during mid-year months.
- Family vs Non-Family Travelers
- What the chart shows: Comparison of total bookings and revenue for family and non-family travelers.
- Key Insight: Non-family travelers contributed over 80% of bookings and generated three times the revenue of family travelers.
- Average Length of Stay and Booking Window What the chart shows: Differences in how far in advance family and non-family travelers book and how long they stay. Key Insight:
- Families book further in advance (26.8 days on average) compared to non-families (23.2 days).
- Both groups have similar lengths of stay (around 2.7 days).
Contact:
For any questions or feedback, feel free to reach out via LinkedIn:
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DATA SET: 2025 DSA Case Study Dataset.xlsx