Course Project MISM 6210: Information Visuals and Dashboards for Business
Hotel Booking and Cancellation Analysis
The purpose of this analysis is to explore the Hotel Booking Demand dataset and identify key factors contributing to cancellations in the hotel industry. Our objective is to gain insights into cancellation rates, revenue losses due to cancellations, and characteristics of guests more prone to canceling their bookings.
Background
The hotel experiences a high cancellation rate, leading to a potential revenue loss of $11.48M.
The majority of the canceled reservations did not require a deposit.
How to mitigate revenue losses due to cancellations?
How to identify guests more likely to cancel their bookings?
How to make recommendations based on the analysis and predictive models?
Business Question
Why this matter
Cancellations negatively affect hotel revenue and profitability.
Proactive steps can help hotels minimize cancellations and manage their inventory better.
Data-driven decisions improve revenue management, guest experience, and profitability.
Article Hotel Booking Demand Datasets
A collection of real hotels booking information from Resort and City hotels
31 variables
40060 observation representing hotel booking
Data
The dataset was processed to address issues like missing values and duplicate rows.
An exploratory data analysis was performed to identify patterns.
Correlation analysis and random forest classifier were used to select variables predicting cancellations.
The predictive model showed an accuracy rate of 73.8% and helped identify key features like lead time, special requests, average daily rate, and weekday stays.
Methodology
Key Findings
Create a clear cancellation policy.
Collect a deposit for confirmed bookings.
Offer discounts for confirmed bookings.
Implement length of stay restrictions.
Send reminders about bookings.
Provide incentives for certain room types.
Offer discounts or incentives during high cancellation months.
Limit online bookings.
Last-minute offer strategy.
Dynamic deposit strategy based on key features.
Promotions and discounts on special request services.
Recommendations
The analysis did not consider external factors affecting cancellations like travel regulations or economic conditions.
The impact of cancellations on guest satisfaction was not considered.
There is a lack of direct information on the revenue or profit of the company.
The data has limited information on bookings over 500 dollars.