A significant number of hotel bookings are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.
The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.
The cancellation of bookings impact a hotel on various fronts:
- Loss of resources (revenue) when the hotel cannot resell the room.
- Additional costs of distribution channels by increasing commissions or paying for publicity to help sell these rooms.
- Lowering prices last minute, so the hotel can resell a room, resulting in reducing the profit margin.
- Human resources to make arrangements for the guests.
The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.
The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.
Data Dictionary
- Booking_ID: unique identifier of each booking
- no_of_adults: Number of adults
- no_of_children: Number of Children
- no_of_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
- no_of_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel
- type_of_meal_plan: Type of meal plan booked by the customer:
- Not Selected – No meal plan selected
- Meal Plan 1 – Breakfast
- Meal Plan 2 – Half board (breakfast and one other meal)
- Meal Plan 3 – Full board (breakfast, lunch, and dinner)
- required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes)
- room_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.
- lead_time: Number of days between the date of booking and the arrival date
- arrival_year: Year of arrival date
- arrival_month: Month of arrival date
- arrival_date: Date of the month
- market_segment_type: Market segment designation.
- repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes)
- no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking
- no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the current booking
- avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic. (in euros)
- no_of_special_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc)
- booking_status: Flag indicating if the booking was canceled or not.
Profitable Policies for Cancellations and Refunds:
- Lead Time Optimization: Encourage guests to book well in advance by offering attractive discounts for bookings made more than 90 days before check-in. This can help reduce last-minute cancellations and increase revenue stability.
- Online Booking Channel Promotion: Focus on optimizing the hotel's online presence and attract more online bookings by offering promotions, exclusive discounts, and loyalty programs.
- Special Request Incentives: Offer incentives for guests who make special requests, such as complimentary room upgrades or welcome amenities, to reduce the cancellation rate for guests with special requests.
- Cancellation Policy Review: Revise the hotel's cancellation policy to make it more guest-friendly while protecting the hotel's interests, including flexible cancellation options for guests who book well in advance.
- Market Segment Focus: Consider targeting marketing efforts toward market segments with lower cancellation rates, such as corporate guests or aviation-related bookings, to reduce the overall cancellation rate.
Additional Recommendations:
- Customer Segmentation: Segment guests based on their preferences, behavior, and likelihood of cancellation. This can help in customizing marketing efforts and service offerings.
- Booking Channels: Analyze the performance of different booking channels (online, offline, corporate) and allocate resources accordingly. Focus on channels with a lower cancellation rate.
- Special Requests Management: Streamline the process of managing special requests to ensure guest satisfaction and minimize cancellations.
- Lead Time Analysis: Further analyze the lead time data to understand booking patterns and optimize pricing and promotions.
- Continual Monitoring: Implement regular monitoring of booking trends, customer feedback, and competitive analysis to adapt policies and strategies in real-time.
- Data-Driven Decision-Making: Continue to leverage data analytics and machine learning to refine booking and pricing strategies.
Model Insights:
- Model Selection: The choice of the model should align with the hotel's objectives and trade-offs between precision and recall. If the priority is to minimize both false positives and false negatives, the Logistic Regression model with a threshold of 0.315 achieves an F1 score of 0.69835, indicating a well-balanced performance. Alternatively, the Decision Tree model (Post-Pruning) excels with a higher F1 score of 0.80367, highlighting its ability to optimize the trade-off between precision and recall.
- Consider Business Objectives: The hotel's model selection should be driven by its specific business goals and the associated costs of prediction errors. Whether the focus is on maximizing precision, recall, or a balance between both, the model choice should reflect these priorities.
- Regular Monitoring and Fine-Tuning: Continuous monitoring and refinement of models is essential. Regularly assess the model's performance and adapt it to changing business needs and customer behaviors.