Hotel Revenue Analysis
I will be performing a data analysis on the dataset provided by Absent Data. The Business questions I would like to help answer are the following:
- Is hotel revenue growing by year?
- Should hotels increase the amount of parking spaces?
The dataset includes years 2018, 2019 and 2020. The 2020 dataset was not complete so I decided to exclude it and compare the 2018 and 2019 datasets.
Dashboard
Total Revenue
Total revenue was calculated from the average daily rate, total stays per night, discount and meal cost. The total Revenue from 2018 was $1,959,872.27 and 2019 was $7,616,162.28. This shows a positive change in revenue from 2018 to 2019
Average Daily Rate
The average daily rate grew from $87.18 in 2018 to $95.00 in 2019. The overall average rate between the two years was $93.30.
Total Number of Stays
Total number of Stays calculated for was 74,588 and in 2019 it was 265,856.
Average Discount
Average discount in 2018 was 23.79% and in 2019 it was 24.37%.
Stays per Month
The most popular season to stay on hotels is autumn while the least popular was winter. The most popular month to book a hotel stay was September with 54,255 and the least popular was January with 6,535.
Revenue by Hotel Type
I felt it was also important to note the revenue difference between hotel types. In 2018 city and resort hotels made roughly the same however in 2019 city hotels’ revenue were approximately 48.2% higher than resort hotels.
Hotel Revenue(World Map)
There were several values that were on the extreme ends of the Revenue scale. I decided to only include values between 10,000 and 1.1 million as there were a few countries under $10,000 of revenue and only one country above $1.1 million (Portugal which was close to 4 times that amount). European countries were among the top earners by revenue, Portugal with a little over $3,962,888, Great Britain with a little over $1,009,754, France with $808,987 and Spain with $780,470.
Bookings by Day
The most popular day to stay at a hotel was the 28 of each month, however the most popular time of the month overall was at the beginning of the month. I also find it interesting that even though the most popular day of the month, the 28, was also grouped in the least popular time of the month.
Required Parking Spaces
There was no real way to see each individual hotels’ parking needs. By grouping each hotels parking needs together it would seem that they are accommodating more guests each year and I believe it would be safe to assume that certain hotels would in fact need to increase their parking capacity.
Summary
Based on the report I’ve made I feel that it would be accurate to state that the hotel company’s revenue has increased between the years 2018 and 2019. I would like to look at a more complete 2020 dataset to be able to analyze the trends between hotels in various countries and the types of hotels.
As for the question of requiring more parking spaces I feel like the answer would be yes however data specific to each hotel’s bookings would be required to properly prescribe which hotels would need more parking spaces and roughly how many.
What I learned
- Utilizing Created Fields in Tableau
- Exploratory Analysis of large datasets
- Identifying and handling outliers in graphic visualizations
