🏨 AtliQ Hotels – Data Analysis & Business Insights Project

🚀 Executive Summary

AtliQ Hotels, a multi-city hospitality chain operating luxury and business properties, faced challenges in revenue optimization, occupancy performance, and booking efficiency.

This project delivers a comprehensive Exploratory Data Analysis (EDA) to uncover:

• 📉 Revenue leakage due to cancellations & no-shows
• 🏙️ City-level performance gaps
• 🛏️ High vs. low-performing room categories
• 📅 Seasonal revenue patterns
• 📊 Channel dependency and booking behavior

The analysis transforms raw booking data into clear, actionable business recommendations aimed at improving occupancy, revenue, and strategic decision-making.

A presentation deck summarizing the insights is provided alongside the notebook.

📊 Key Business Metrics

• Cancellation rate: ~24.8% of total bookings
• No-show rate: ~5%
• Average occupancy: 63% overall, 74% weekends vs 52% weekdays
• Top revenue city: Mumbai (≈35% of total)
• Peak season revenue: May–July

These KPIs highlight where the business is losing share and where focus should go.

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📌 Key Business Findings

1️⃣ Revenue Leakage Identified

• ~24.8% bookings were cancelled
• ~5% were no-shows
• Significant revenue loss due to booking inefficiencies

Impact: Stronger cancellation policies and direct booking incentives are needed.

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2️⃣ City-Level Revenue Leaders

• 🥇 Mumbai generated the highest revenue
• Followed by Bangalore and Hyderabad
• Delhi had high occupancy but comparatively lower revenue

Insight: Pricing strategy and room mix vary by city.

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3️⃣ Room Category Performance

• RT2 (Elite) is the most booked category across cities
• Presidential rooms generate highest revenue per booking but have lower demand
• Standard rooms consistently rank second

Opportunity: Reposition or bundle premium rooms to increase utilization.

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4️⃣ Weekend vs Weekday Behavior

• Weekend occupancy: ~74%
• Weekday occupancy: ~52%

Opportunity: Introduce weekday corporate packages to stabilize revenue.

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5️⃣ Seasonal Revenue Trends

• May–July: Peak revenue months
• December & January: Revenue dip

Opportunity: Launch off-season campaigns and dynamic pricing models.

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🎯 Business Recommendations

• Implement dynamic pricing strategies for low-demand periods
• Promote underutilized premium rooms via bundled offers
• Reduce reliance on third-party platforms by incentivizing direct bookings
• Improve cancellation policies to minimize revenue leakage
• Replicate strategies from high-performing properties (e.g., Exotica) across portfolio

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🛠️ Tools & Technologies

Category	Tools Used
Programming	Python
Data Manipulation	pandas, numpy
Visualization	matplotlib, seaborn
Environment	Jupyter Notebook
Version Control	Git & GitHub

🧮 Example Code Snippet

import pandas as pd

# load booking data
df = pd.read_csv('datasets/fact_bookings.csv', parse_dates=['booking_date', 'checkin_date'])

# compute cancellation rate
cancel_rate = df['status'].eq('Cancelled').mean()
print(f'Cancellation rate: {cancel_rate:.1%}')

# revenue by city
revenue_city = df.groupby('city')['revenue'].sum().sort_values(ascending=False)
print(revenue_city.head())

This snippet demonstrates typical data loading and KPI calculations using pandas.

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📂 Datasets Used

Dataset	Description
dim_date.csv	Date dimension data
dim_hotels.csv	Hotel metadata (city, type)
dim_rooms.csv	Room categories
fact_bookings.csv	Detailed booking transactions
fact_aggregated_bookings.csv	Pre-aggregated KPIs

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🧹 Data Preparation Process

• Standardized date formats
• Handled missing values using contextual logic
• Removed duplicate records
• Treated outliers using IQR method
• Merged dimension & fact tables ensuring referential integrity

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📊 Analytical Approach

The project followed a structured EDA framework:

1. Data Cleaning & Validation
2. KPI Calculation & Metric Analysis
3. City-wise & Property-wise Performance Evaluation
4. Room Category Performance Analysis
5. Revenue Trend & Seasonality Study
6. Occupancy Pattern Analysis
7. Business Recommendation Framework

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📈 Selected Visualizations & Strategic Insights

Below are a few of the most illustrative charts from the analysis:

Cancellation/no‑show rate (~30%) clearly exposes revenue leakage.
Strategic insight: Enforce stricter cancellation policies and promote non‑refundable rates.

Mumbai leads revenue; other cities underperform.
Recommendation: Tailor pricing and marketing by city to close the gap.

Elite rooms dominate bookings but bring less revenue per stay than presidential suites.
Strategy: Upsell premium rooms and create value packages.

Revenue peaks in May–July, dips in winter months.
Action: Launch off‑season campaigns and use dynamic pricing.

(Full visualizations available in the Jupyter Notebook and presentation deck.)

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💼 Business Impact & Strategic Value

This project simulates a real-world hospitality analytics scenario and demonstrates how operational booking data can be transformed into strategic decision-making tools.

🔍 What This Analysis Enables for Leadership:

• Clear visibility into revenue leakage from cancellations and no-shows
• City-level performance diagnostics to guide pricing and marketing strategy
• Room-category level demand analysis for better inventory allocation
• Seasonal revenue trend insights for dynamic pricing decisions
• Data-backed recommendations to improve occupancy and profitability

🎯 Why This Matters

In a competitive hospitality market, small inefficiencies compound into significant revenue loss.

By applying structured exploratory data analysis, KPI evaluation, and business reasoning, this project bridges the gap between raw booking data and executive-level strategy.

It showcases the ability to:

• Translate business problems into analytical questions
• Quantify operational inefficiencies
• Extract revenue-driving insights
• Communicate findings in a structured, executive-ready format

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🛠️ How to Run the Project

1. Clone the repository

   git clone https://github.com/ManoharManu1999/AtliQ-Hotels-EDA.git
   cd AtliQ-Hotels-EDA

2. Set up Python environment

   python -m venv venv
   venv\\Scripts\\activate  # Windows
   pip install -r requirements.txt  # create this file if needed

3. Launch the notebook

   jupyter notebook "AtliQ Hotels Data Analysis.ipynb"

4. View the presentation deck located at AtliQ Hospitality Project Presentation.pdf.

5. Explore the visuals under the images/ folder or regenerate using the notebook.

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📁 Project Structure

📦 AtliQ-Hotels-EDA
├── AtliQ Hotels Data Analysis.ipynb
├── README.md
├── AtliQ Hospitality Project Presentation.pdf
├── /data
│   ├── dim_date.csv
│   ├── dim_hotels.csv
│   ├── dim_rooms.csv
│   ├── fact_bookings.csv
│   └── fact_aggregated_bookings.csv
└── /images
    ├── booking_status_distribution.png
    ├── revenue_by_city.png
    ├── occupancy_by_room_category.png
    ├── monthly_revenue_trend.png
    └── ...

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📬 Contact & Professional Profile

If you would like to discuss this project or explore collaboration opportunities:

Thank you for exploring the AtliQ Hotels EDA project! I hope the insights and recommendations provide value to hospitality professionals and data enthusiasts alike.

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💼 Career Interest

I am currently seeking opportunities in Data Analyst / Business Analyst roles where I can apply data-driven decision-making to solve real-world business problems.

Open to full-time roles, collaborative projects, and analytics-driven initiatives.