Hotel Harmony: Data Insights for Optimised Operations

Project Overview

This project analyses hotel booking data for Elite Hotels International to uncover insights that improve operational efficiency, customer satisfaction, and revenue management. Utilising Python-based data analysis, the project examines booking patterns, cancellation behaviour, guest preferences, and pricing trends to inform data-driven decision-making in the hospitality industry.

Business Problem

Elite Hotels International faces challenges with:

• High booking cancellations
• Seasonal demand fluctuations
• Pricing optimisation
• Understanding guest behaviour across booking channels The goal of this analysis is to identify patterns and actionable insights that enable management to optimise operations and enhance the guest experience.

Dataset Description

The dataset contains 119,390 hotel bookings with information on:

Booking details (lead time, arrival dates, stay duration)

• Guest demographics (adults, children, country)
• Booking channels (market segment, distribution channel, agent)
• Financial data (Average Daily Rate – ADR)
• Booking outcomes (cancellations, special requests, reservation status)

Tools & Technologies

Python

• Pandas & NumPy – data cleaning and manipulation
• Matplotlib & Seaborn – data visualisation
• Scikit-learn – regression and logistic regression analysis Power BI Excel

Data Cleaning & Preparation

Key preprocessing steps included:

• Handling missing values in country, children, agent, and company
• Converting date fields to datetime format
• Removing duplicate records
• Creating engineered features such as total stay nights

Key Metrics

• Average Booking Lead Time
• Cancellation Rate
• Average Stay Duration (Occupancy Proxy)
• Special Requests Frequency
• Revenue Metrics (ADR trends)

Key Insights

Booking Trends

• Peak bookings occur in August, July, and May
• City Hotels receive more bookings but also higher cancellations

Cancellations

• Longer lead times increase cancellation likelihood
• Non-refundable deposits significantly reduce cancellations
• Guests with special requests cancel less frequently

Revenue & Pricing

• City Hotels have higher ADR than Resort Hotels
• ADR increased steadily from 2015 to 2017
• Online and Direct channels generate higher ADR

Guest Behavior

• Guests with more special requests tend to pay higher ADR
• Repeated guests stay shorter on average than new guests

Business Recommendations

• Introduce stricter deposit policies for long lead-time bookings
• Use dynamic pricing during peak seasons
• Incentivise special requests and personalisation to reduce cancellations
• Focus marketing on high-value channels like Online TA and Direct bookings
• Strengthen loyalty programs based on repeat guest behaviour

Future Work

• Build a cancellation prediction model
• Develop an interactive Power BI / Tableau dashboard
• Perform customer segmentation for targeted marketing
• Forecast demand using time-series models

Dashboard View

Author

BuiltbyAmiee