Exploratory Data Analysis (EDA) in the Hospitality Domain

Table of Contents

• Project Overview
• Problem Statement
• Business Understanding
• Data Sources
• Methodology
• Key Insights
• Recommendations
• Tools and Technologies
• Let's Connect

Project Overview

This project demonstrates an end-to-end Exploratory Data Analysis (EDA) workflow in the hospitality domain using Python.

As a data analyst, I analyzed booking and revenue data for AtliQ Grands, a leading hotel chain in India, to identify:

• Revenue leakage
• Occupancy trends
• Strategic opportunities

The analysis uncovers actionable insights to help reverse declining market share and revenue, showcasing my ability to translate raw data into business recommendations.

The project leverages Python's data manipulation and visualization libraries to:

• Explore datasets
• Clean inconsistencies
• Derive metrics
• Generate visualizations

View the full analysis in the Jupyter Notebook: Exploratory Data Analysis in the Hospitality Domain using Python.ipynb

Problem Statement

AtliQ Grands, a 20+ year-old hotel chain operating in Delhi, Mumbai, Bangalore, and Hyderabad, has experienced a decline in market share and revenue due to competitive pressures. The goal was to perform a data-driven analysis to pinpoint problem areas such as underperforming channels and occupancy gaps, and to provide strategic recommendations for recovery, including optimizing booking platforms and enhancing direct sales.

Business Understanding

Hotel Portfolio: 7 brands (AtliQ Bay, AtliQ Blu, AtliQ City, AtliQ Grands, AtliQ Seasons, AtliQ Exotica, AtliQ Palace) across Luxury and Business segments.
Room Types: Standard, Elite, Premium, Presidential.
Booking Channels: AtliQ's website and third-party platforms (e.g., MakeMyTrip, LogTrip).
Key Metrics: Revenue generated/realized, occupancy rates, booking platforms, and guest ratings.

Understanding these elements allowed me to align the analysis with business priorities, focusing on revenue optimization and competitive recovery.

Data Sources

The analysis uses five datasets (dimension and fact tables) covering May-July 2022:

• dim_date.csv: Calendar details (dates, week numbers, day types).
• dim_hotels.csv: Hotel properties (name, category, city).
• dim_rooms.csv: Room types and classes.
• fact_aggregated_bookings.csv: Aggregated bookings (capacity, successful bookings).
• fact_bookings.csv: Detailed transactions (booking ID, dates, guests, revenue).

Methodology

To solve the problem, I followed a structured data analytics workflow in Python:

1. Data Exploration – Understood the structure, quality, and distribution of the data.
2. Data Cleaning – Handled missing values, corrected inconsistencies, and prepared data for analysis.
3. Data Transformation – Merged datasets, created calculated fields, and reformatted data for insights generation.
4. Insights Generation – Analyzed the transformed data to identify trends, patterns, and actionable recommendations.

View the full analysis in the Jupyter Notebook: Exploratory Data Analysis in the Hospitality Domain using Python.ipynb

Key Insights

• Occupancy Leaders: Presidential rooms topped with 59.28% average occupancy; Delhi led cities at 61.51%.
• Weekend Spike: Occupancy jumps from 50.88% (weekdays) to 72.34% (weekends), showing strong leisure demand.
• Revenue Hotspots: Mumbai leads at ₹668.57M, followed by Bangalore (₹420.38M) and Hyderabad (₹325.18M).
• Platform Trends: “Others” booking category drives ₹480.70M revenue, outpacing Makeyourtrip (₹233.13M) and Logtrip (₹129.04M).
• Ratings Gap: Delhi customers rate highest (3.78/5), while Bangalore scores lowest (3.40/5).

Recommendations

• Capture Leisure Market: Promote weekend packages and leisure experiences, especially in low-performing cities.
• Lift Weekday Occupancy: Offer corporate tie-ups and business traveler discounts to close the weekday gap.
• Upsell Premium Rooms: Highlight Presidential and Premium categories in marketing for higher margins.
• Channel Optimization: Invest in high-performing platforms while incentivizing direct online bookings to cut commissions.
• Target Service Improvements: Address rating gaps in Bangalore through service training and guest experience upgrades.

Tools and Technologies

• Python: Core scripting
• Libraries:
  • Pandas: Data manipulation and exploration
  • Matplotlib: Visualizations
• Environment: Jupyter Notebook for interactive analysis

Let's Connect

I’m passionate about leveraging data to solve business challenges in hospitality and beyond. Feel free to reach out for feedback, collaborations, or to connect:

• Email: bilalayub.connect@gmail.com
• LinkedIn: https://www.linkedin.com/in/muhammad-bilal-ayub/