Calculated and analyzed the key metrics of Hospitality data using My SQL.
Hospitality Bookings Data Analysis challenge from Code Basics.
📊 Comprehensive Metrics Calculations
Analyzed overall bookings, revenue generated, loss due to cancellations and no-shows, and successful check-ins.
Calculated average customer ratings across multiple criteria such as service, cleanliness, and amenities.
📈 Weekly Insights and Trends
Conducted Week-over-Week (WoW) analysis to identify booking trends, peak periods, and seasonality patterns.
Assessed customer behavior to uncover the impact of cancellations, no-shows, and refund policies on revenue.
🔍 Cancellation and No-Show Insights
Highlighted high cancellation rates (X%) and no-show percentages (Y%), identifying specific customer groups and booking sources with recurring patterns.
💡 Key Recommendations
📍Dynamic Pricing: Introduce flexible pricing models based on demand to attract more customers during off-peak periods.
📍Optimized Cancellation Policy: Implement partial refund options and stricter policies to reduce losses from cancellations.
📍Loyalty Programs: Develop rewards for frequent bookers and returning customers to improve retention.
📍Enhanced Customer Experience: Focus on improving key areas with low ratings, like check-in efficiency and room amenities.
📍Marketing Strategy: Use targeted campaigns on high-performing channels to boost bookings during slow weeks.