Hotel Booking Demand
Project Overview:
This data set contains booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things.
Objective:
To analyze revenue and bookings data, identifying key revenue drivers, trends, and opportunities to optimize profitability. The analysis focused on understanding revenue patterns by room type, booking source, guest demographics, and seasonal variations.
Steps taken to complete the project:
Data Cleaning: Handled missing values, duplicates, and inconsistencies in the dataset.
Converted data types where necessary (e.g., dates to datetime format).
Descriptive Statistics: Conducted summary statistics on key variables such as hotel types, lead time, booking cancellations, and more.
Feature Engineering: Created new features like "length of stay" (check-out date minus check-in date) and seasonality (high or low season).
Visualization:
Visualized data trends using Tableau and Python’smatplotlibandseabornlibraries to analyze booking demand over time.
Distribution of bookings between city and resort hotels.
Relationship between lead time and cancellation rates.
Correlation Analysis: Checked correlations between numerical variables to understand relationships affecting booking behavior, such as the effect of lead time and market segments on cancellations.
Insights on Cancellation Rates: Investigated cancellation patterns to identify the causes behind high cancellation rates, especially focusing on specific customer segments, booking types, and lead times.
Outcomes:
Booking Demand Trends: Discovered significant seasonality trends, with resort hotels being in higher demand during the summer months, while city hotels experienced a steadier demand throughout the year.
Lead Time: Longer lead times (i.e., earlier bookings) were observed to have a higher probability of cancellation, indicating the need for a flexible pricing strategy.
Customer Segment Insights: Certain market segments like corporate bookings and direct bookings had lower cancellation rates, while bookings from online travel agencies (OTAs) had a higher chance of cancellations.
Average Length of Stay: Resort hotels had a longer average stay compared to city hotels.
Geographic Insights: Domestic bookings were less likely to be canceled than international bookings, highlighting different customer behaviors based on geography.
Technologies Used:
Python: For data cleaning, feature engineering, and initial analysis.
Key Libraries:pandas,numpy,matplotlib,seaborn
Tableau: For advanced data visualization, creating interactive dashboards and easily digestible insights.
GitHub: To upload the Python script for public access and version control.
Kaggle: The dataset source for hotel booking demand.
Project Results and Impact:
Cancellation Patterns: About 30% of bookings were canceled, with online travel agents (OTAs) contributing the most to cancellations
Lead Time Impact: Bookings made far in advance were more likely to be canceled, particularly for resort hotels, indicating the need for better cancellation policies or more flexible booking options
Revenue Insights: While resort hotels had higher booking demand during the summer, city hotels had a more consistent booking pattern throughout the year. This suggests different marketing strategies are needed for each hotel type
Guest Preferences: Guests preferred higher-tier rooms in resort hotels, while city hotels saw more demand for standard rooms, reflecting different customer expectations based on hotel type
What we learned from the project:
Seasonality Plays a Major Role: Resort hotel is highly seasonal, with peak demand in summer, while city hotel has a steadier flow of guests throughout the year
Lead Time Affects Cancellations: Bookings with a longer lead time have a higher risk of cancellation. Implementing flexible cancellation policies or promotions closer to the check-in date could mitigate this risk
Market Segments Matter: Direct bookings and corporate clients have lower cancellation rates, suggesting that hotels should encourage more direct bookings and cater to corporate guests with special offers
Booking Source Influences Cancellations: Online travel agencies (OTAs) contribute significantly to cancellations, highlighting the need for hotels to manage OTA relationships carefully while promoting direct bookings
Data-Driven Decision-Making: Understanding booking patterns can lead to more targeted marketing, better pricing strategies, and improved operational efficiency for hotels
Conclusion:
This project provided critical insights into the factors driving hotel booking demand and cancellations, with actionable findings that can help hotels optimize their pricing strategies, improve guest experience, and reduce cancellations. By using Python and Tableau, we transformed raw data into meaningful insights that can guide better business decisions in the hotel industry.
