🏨 AtliQ Grands Hospitality Data Analysis

🗂️ Table of Contents

• Introduction
• Problem Statement
• Tools & Technologies Used
• Project Workflow
  • Data Cleaning and Transformation
  • Exploratory Data Analysis (EDA)
  • Key Insights
• Recommendations
• Link

📝 Introduction

This project analyzes the business performance of Atliq Grands, a hotel chain operating across major Indian cities such as Delhi, Mumbai, Bangalore, and Hyderabad. Despite its strong presence in the hospitality industry, the company has recently experienced a decline in revenue and market share.

The objective of this project is to leverage data analysis and visualization to uncover actionable insights, enabling data-driven decision-making and improving overall financial performance.

❓ Problem Statement

Atliq Grands is experiencing a decline in revenue and market share due to increasing competition in the hospitality industry. This project aims to analyze booking data from both direct and third-party platforms to identify key trends and generate actionable insights that can improve revenue and overall business performance.

🛠️ Tools & Technologies Used

• 🐍 Python (Jupyter Notebook) – Data analysis
• 📊 Pandas – Data manipulation
• 📉 Matplotlib – Data visualization
• 🎨 Canva – Presentation

🔄 Project Workflow

🔍 Data Cleaning and Transformation

Ensuring data quality was a critical step in the analysis. This phase included:

• Handling missing and inconsistent data

• Removing invalid records (e.g. negative guest counts)

• Detecting and eliminating outliers to improve accuracy

• Creating new columns such as occupancy percentage (ratio of successful bookings to total capacity)

📊 Exploratory Data Analysis (EDA)

• Analyzed booking trends across cities and platforms

• Compared hotel categories and room types

• Identified patterns impacting occupancy and revenue

💡 Key Insights

• Room Category Performance: Presidential rooms have the highest average occupancy rate (~59%), indicating strong demand for premium offerings.

• City Performance: Delhi leads in occupancy (~61%), followed by Hyderabad, while Bangalore shows relatively lower performance (~56%).

• Booking Platforms: Third-party platforms generate a higher share of bookings compared to direct channels, impacting profit margins due to commissions.

• Weekend Trends: Occupancy rates are consistently higher on weekends, highlighting opportunities for dynamic pricing and targeted promotions.

• Monthly Trends: Revenue shows fluctuations across months, indicating seasonality and demand variability.

📝 Recommendations

Based on the analysis, the following data-driven recommendations are proposed to improve revenue and overall performance:

• Increase Direct Bookings: Promote bookings through the official website to reduce commission costs and improve margins.

• Implement Dynamic Pricing: Adjust room prices based on demand, seasonality, and occupancy trends to maximize revenue.

• Leverage High-Demand Periods: Introduce targeted promotions during weekends and peak seasons to further boost revenue.

• Focus on Underperforming Areas: Strengthen marketing in low-performing cities and hotel categories.

• Expand in High-Performing Markets: Invest in cities like Delhi, which show consistently high occupancy, to capitalize on strong demand.

📎 Link

💼 Linkedin Post