https://github.com/codeofrahul/hotel_booking_analysis_powerbi
In recent years, City hotel and Resort hotel have seen high cancellation rates and as a result each hotel is now dealing with a number of issues. I have analyzed this data set and have presented my findings in a report format. I have also given my suggestion to those hotels.
https://github.com/codeofrahul/hotel_booking_analysis_powerbi
dataanalysis datavisualization hotel-booking powerbi
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In recent years, City hotel and Resort hotel have seen high cancellation rates and as a result each hotel is now dealing with a number of issues. I have analyzed this data set and have presented my findings in a report format. I have also given my suggestion to those hotels.
- Host: GitHub
- URL: https://github.com/codeofrahul/hotel_booking_analysis_powerbi
- Owner: CodeofRahul
- Created: 2023-10-15T18:05:46.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-22T16:31:55.000Z (over 2 years ago)
- Last Synced: 2025-01-30T03:19:00.510Z (12 months ago)
- Topics: dataanalysis, datavisualization, hotel-booking, powerbi
- Homepage: https://www.novypro.com/project/hotel-booking-analysis-18
- Size: 436 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Hotel_Booking_Analysis
## Business Problem
In recent years, City hotel and Resort hotel have seen high cancellation rates and as a result each
hotel is now dealing with a number of issues, including fewer revenues and less than ideal hotel
room use. Consequently, lowering cancellation rates is both hotel’s primary goal in order to
increase their efficiency in generating revenue and for us to offer thorough business advice to
address this problem.
## Assumptions
1. No unusual incident happen between 2015 and 2017.
2. The information is still current and can be used to analyze a hotel's possible plans in an efficient
manner.
3. The hotels are not currently using any of the suggested solutions.
4. The biggest reason for reduced income generation is booking cancellation.
## Research Question
1. What are the variables that affect hotel reservation cancellations?
2. How can we reduce the number of hotel reservation cancellations?
3. How can hotels be assisted in making pricing and promotional decisions?
## Hypothesis
1. More cancellations occur when prices are higher.
2. When there is a longer waiting list, customers tend to cancel more frequently.
3. The majority of clients are coming from online to make their reservations.
## Analysis and Findings
The bar graph shows the Count of Reservations that are cancelled and that those are not. The “not
cancelled” bar is taller than the “cancelled” bar, indicating that there are more reservations that
were not cancelled than those that were cancelled. There are still approximately 36.44% of client
who cancelled their reservation, which has a significant impact on the hotel’s earnings.
In this bar graph we can see that In comparison to resort hotels, city hotels have more
bookings. It’s possible that resort hotels are expensive in those cities than others.
The line chart shows the average daily rate in city and resort hotels from July 2015 to July 2017.
The highest average daily rate for both city and resort hotels occurred in July 2017.
I have developed the grouped bar chart to analyze highest and lowest level of reservation status
by month. As we can see , the highest confirmed reservation in August and lowest cancellation
in September. Whereas January is the month with the most cancelled reservation.
This bar graph confirmed that cancellations are more common when price are higher and least
common when price are lower. Therefore , the cost of accommodation is solely responsible for
the cancellation.
The pie chart shows the top 10 countries with reservations cancelation. Portugal has the highest
number of cancellations, with approximately 70.28% of all reservations being cancelled.
If we analysis the area from where guests are visiting the hotels and making reservations. So, we
can see that around 47% reservation come from online travel agents, 20.32% come from offline
travel agents. 10.47% clients make their reservation offline by visiting hotels.
As seen in the chart, reservations are cancelled when average daily rate is higher than when it is
not cancelled. It clearly proves all the above analysis, that the higher price leads to higher
cancellation.
## Suggestions
1. Cancellation rates increase as the price increases. To prevent reservation cancellations,
hotels can consider adjusting their prices for specific hotels based on locations. They can also
offer discounts to attract more customers.
2. Since the resort hotel has a higher ratio of cancellations compared to city hotels, they should
consider offering reasonable discounts on room prices during weekends or holidays.
3. In the month of January, hotels can start campaigns or marketing with a reasonable amount
to increase their revenue as the cancellation is the highest in this month.
4. They can also enhance the quality of their hotels and services, primarily in Portugal to reduce
the cancellation rate.
**Dashboard link :-** https://www.novypro.com/project/hotel-booking-analysis-18