Navigating the Turbulent Waters of Hotel Booking Cancellations: The Menace Solution (Phase 1)
The Menace
Once upon a time in the city of London, there was a renowed hotel called “Radisson Blu”. It stood tall and elegant, welcoming guests from all around the world.
Now, that would be a fantastic story but you see, the hotel faced a persistent problem that plagued its operations — numerous last-minute cancellations. These cancellations caused financial losses and left rooms unoccupied, leading to inefficiency and discontent among the staff.
Determined to find a solution, the hotel manager, assembled a team of experts to tackle the issue.
Before this, let’s build a common understanding of recent information.
Radisson Blue Brand Palette
The rise of hotel Reservation Cancellations
The rise of reservation channels has revolutionized the way customers book their stays, resulting in a shift in their behaviour. Unfortunately, this has also led to a surge in the number of cancellations and no-shows, posing a significant challenge for hotels like Radisson. Customers cancel for a variety of reasons, including changes in plans and scheduling conflicts, often taking advantage of free or low-cost cancellation policies.
Cancellations by Sectors
In 2017 and 2018, All Complementary guests cancelled their booking.
All sectors experienced an increase in cancellations in 2018.
INSIGHTS
According to the article “European cities hotel forecast for 2017 and 2018” by PWC (https://www.pwc.com/gx/en/hospitality-leisure/assets/european-hotels-forecast-report-2017-2018-web.pdf),
Hotel revenues are still not back to pre-recession levels. Overall, Europe is expected to continue to see relatively low levels of new hotel supply growth despite some hotspots such as Berlin and London.
Over one third of people now use a mobile device to book a hotel room. That’s according to the latest Travel Flash Report by Criteo.
The shift to mobile continues to rise, and in 2018, hotels have an opportunity to capitalize on changing consumer habits.
As it turns out, the mobile booking landscape varies considerably between regional preferences, booking platforms, and demographics.
But how do we solve this Menace?
As we now fully understand which sectors of customers cancel, and when this all begun, I intrigue you, that by further knowing crucial data such as how much guests are paying, which months are busiest, we will be able to conclude on a solution to control and end this online cancellation scare.
Let’s go on.
How much do guests pay for a room per night?
The figure shows that the average price per room depends on its type and the standard deviation.
Which are the most busy months?
The graph clearly shows that the hotel is busiest during autumn.
How long do people stay at the hotels?
The longest number of nights reserved is between 10 and 13.
People typically spend one to three days for Room 1.
Room 1 is mostly preferred and is highly booked, followed by Room 4.
How does the price vary per night over the year?
This plot clearly shows that:
Room 1 varies and is most expensive during Spring and Autumn.
Room 2 peaks once during Spring.
Room 3 is seasonal and is booked only during Winter.
Price for room 4 is higher during Autumn.
Price for room 5 increases twice over the year during Spring and Autumn.
Room 6 is the highest priced room and is mostly booked during Spring and Autumn.
Room 7 varies a lot and is most expensive during Autumn.
Recommendation:
A pricing strategy in light of this feedback, to consider which months are busiest and using price reductions and segment-focused offers to attract more online customers, reduce complementary cancellations and increase the number of aviation customers.
Total Market Breakdown
The hotel has more guests booking reservations online than offline, explaining why most of the common complaints include
Mishandled Reservations and Double Bookings, Incorrect Guest Preferences, Overpricing by third-parties and Third-Party Scams.
Recommendations:
Mishandled Reservations and Double Bookings :
To proactively prevent these types of administrative errors, you’ll want to invest in a robust property management system (PMS).
Incorrect Guest Preferences :
To handle these mistakes, all managers and staff should be well-trained in conflict resolution and customer communication.
Overpricing by third-parties :
Give Travelers a Reason to Book Direct by giving importance of the hotel’s website, improve its usability and make it easy to access while offering visitors an unforgettable Booking Journey Experience!
Third-Party Scams :
As a regular habit, monitor news and industry sites for word of these scams.
References:
“European cities hotel forecast for 2017 and 2018” by PWC (https://www.pwc.com/gx/en/hospitality-leisure/assets/european-hotels-forecast-report-2017-2018-web.pdf)
“Common Complaints About Online Hotel Bookings “ by RoomKeyPMS (https://roomkeypms.com/blog/common-complaints-online-hotel-bookings-fix/)
“Hotel Booking Prediction (99.5% acc)” by NITESH YADAV https://www.kaggle.com/code/niteshyadav3103/hotel-booking-prediction-99-5-acc
Notes:
I leverage a comprehensive dataset obtained from Kaggle. This data has 36275 rows with 19 columns. For this analysis, I used Python packages such as seaborn, pandas, matplotlib, and numpy along with the Python environment.
This dataset encompasses a trove of information, including the following columns:
Column Dtype
--- ------ -----
0 Booking_ID object
1 no_of_adults int64
2 no_of_children int64
3 no_of_weekend_nigts int64
4 no_of_week_nights int64
5 type_of_meal_plan object
6 required_car_parking_space int64
7 room_type_reserved object
8 lead_time int64
9 arrival_year int64
10 arrival_month int64
11 arrival_day int64
12 market_segment_type object
13 repeated_guest int64
14 no_of_previous_cancellations int64
15 no_of_previous_bookings_not_canceled int64
16 avg_price_per_room float64
17 no_of_special_requests int64
18 booking_status object