Optimizing Revenue Leakage and Profitability in the Hospitality Sector

1. Project Overview

In the highly competitive hospitality industry, understanding patterns of revenue leakage and opportunities to maximize profitability is critical. This project provides a data-driven approach to identify, analyze, and mitigate revenue losses while improving operational and financial efficiency.

Using Power BI for interactive visualization, alongside Python-based data analysis, we uncover insights from occupancy trends, booking behavior, customer segmentation, and pricing strategies to support informed decision-making.

2. Dashboard Link

Explore the live dashboard here:
👉 Power BI Dashboard

3. Files Included

• Dashboard.pbix – Power BI source file for the interactive dashboard.
• Dashboard.pdf – Snapshot of the dashboard.
• ppt.pdf – Presentation summarizing key findings and recommendations.
• trend_analysis.ipynb – Jupyter Notebook for analyzing trends in occupancy, revenue, and customer behavior.
• visualization_analysis.ipynb – Notebook with EDA and visual storytelling.
• README.md – Project documentation.

4. Key Objectives

• Identify revenue leakage areas in hotel operations.
• Analyze trends in booking patterns and cancellations.
• Evaluate room category performance and seasonal variations.
• Recommend pricing and booking strategy improvements.
• Visualize KPIs such as Average Daily Rate (ADR), Revenue Per Available Room (RevPAR), and Occupancy Rate.

5. Tools & Technologies

• Power BI – For building interactive dashboards and KPI tracking.
• Python (Pandas, Matplotlib, Seaborn) – For data preprocessing and analysis.
• Jupyter Notebook – For documenting the analytical workflow.
• PDF & PPT – For sharing insights and reports.