Zain The AnalystAvailableCase Study
Data Analysis and Visualization
Hotel Bookings Analysis Dashboard
A Power BI dashboard that helps understand hotel bookings, cancellations, occupancy, lead time, guest behavior, and market segment trends.
Key Metrics Analyzed
Average Waiting Duration3 Days
Occupancy Rate62.32%
Average Lead Time104 Days
Booking Cancellation Rate37.04%
Average Daily Rate$102
Repeat Guest Rate3%
Overview & Objectives
This project analyzes hotel booking data using Power BI. It helps users understand booking behavior, cancellation patterns, occupancy rate, average daily rate, repeat guest rate, lead time, and market segment performance.
The dashboard includes two report pages. The first page focuses on monthly booking trends, cancellation behavior, lead time, booking changes, and key hotel KPIs. The second page focuses on market segment distribution, cancellation rate by hotel type, repeat guest rate, and relationships between lead time, average daily rate, booking cancellation, and occupancy rate.
Project Goal
Build a clear Power BI dashboard that helps users explore hotel booking performance and understand the main reasons behind cancellations, occupancy changes, and booking trends.
The Business Problem
Hotel booking data can be difficult to understand when bookings, cancellations, lead time, market segments, occupancy, and revenue metrics are analyzed separately. The business needed one clear dashboard to quickly compare hotel performance, customer behavior, and cancellation risks.
Methodology
My Approach
- 1I cleaned and prepared the hotel bookings data.
- 2I created Power BI measures for occupancy rate, cancellation rate, lead time, average daily rate, repeat guest rate, and booking totals.
- 3I designed dashboard pages for booking performance and market segment analysis.
- 4I added visual comparisons by month, day, hotel type, and market segment.
Implementation
Roadmap Execution
Data Preparation
Cleaned and prepared the hotel bookings dataset for analysis.
Data Modeling
Created the Power BI data model for bookings, hotel type, market segments, dates, and guest behavior.
DAX Development
Built measures for occupancy rate, cancellation rate, average daily rate, lead time, repeat guest rate, booking totals, and booking changes.
Dashboard Design
Designed booking performance and market segment analysis pages.
Trend Analysis
Created monthly and daily visuals to show booking, cancellation, and rate patterns.
Final Formatting
Formatted the dashboard with clear KPI cards, line charts, bar charts, pie charts, donut charts, and scatter plots.
Key Features
Hotel booking KPI overview
Average daily rate and cancellation trend by month
Average lead time by market segment
Booking cancellation by day
Previous booking and previous cancellation trend
Total bookings by month
Booking changes by month
Market segment distribution
Cancellation rate by hotel type
Repeat guest rate by market segment
Lead time relationship analysis
Occupancy rate relationship analysis
Business Impact
Helps monitor hotel booking performance in one place
Makes cancellation trends easier to understand
Shows which market segments drive bookings and repeat guests
Supports better pricing and occupancy decisions
Reduces manual analysis work by using one interactive Power BI dashboard
Challenges Overcome
- Combining booking, cancellation, hotel type, and market segment data into one dashboard
- Making cancellation behavior easy to understand
- Showing monthly and daily booking trends clearly
- Comparing market segments with different booking patterns
- Designing simple visuals for hotel performance metrics
Outcomes
Final Outcomes & Learnings
- The final dashboard gives a clear view of booking trends, cancellation patterns, market segment performance, occupancy rate, lead time, and average daily rate.
- Users can quickly identify high-cancellation periods, compare market segments, understand guest behavior, and monitor key hotel booking KPIs.
Project Gallery & Screenshots
Technologies Used
Power BIPower QueryDAXExcelData ModelingData Visualization
Data Sources
Hotel bookings datasetBooking cancellation dataMarket segment dataHotel type dataGuest behavior dataMonthly booking data
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