Hotel Bookings Project
I jumped on the data project challenge initiated by datafrik_o on X (Twitter). Our task was to generate actionable insights using some select variables.
Summary:
The dataset provided consists of 66,541 hotel bookings between January 2010 and December 2019, from a company that helps international travelers secure hotels in their destination countries. The following details made up the dataset - Bookings, Hotel names, origin countries, gender, destination countries, etc. These details helped generate actionable insights to enhance the company's growth.
Analysis:
I used Microsoft Excel for the data Preparation, cleaning, analysis, and visualization.
Data Preparation and Cleaning:
I removed 5 duplicate values, which resulted in the hotel bookings figure dropping from 66,541 to 66,536.
I also removed the "No of Days" column because it was empty and irrelevant to the analysis.
Findings:
Seasonal Analysis:
* The booking trend had a positive flow from 2010-2019. Bookings peaked in 2019 with a total booking of 9,502. The lowest booking was recorded in 2010 with a total booking of 4,797. The highest booking was recorded in August 2019 (851).
* In 2016, the booking pattern in all the origin countries dropped except in Indonesia (1,193)
Demographic Analysis:
* The age groups 25-34, 35-44, and 45-54 have the highest booking trend while age groups 15-24 and 55-64 have the least booking trend. This could be down to age constraints.
* Females slightly edged the Males in the percentage of total bookings, with both genders having a percentage of 50.18% and 49.82% respectively.
The Origin country with the highest booking rate is Thailand.
Hotel Ratings and Customer Satisfaction:
* The Grand Hyatt Hotel stood as the first in the top 10 frequently booked hotels, the most expensive hotel, and the most rated. This underlines its quality and good customer service.
* The Cottage Motel and Simply Charmed B&B are the least booked, expensive, and rated hotels.
Deliverables:
I visualized my results in a dashboard.
Recommendations:
* The reason(s) for the slump in booking patterns in 2016 should be carefully investigated to avoid future occurrences.
* To improve the origin countries with the low booking rates, the methods employed by Thailand as the origin country with the highest booking rate should be incorporated into them.
* The least booked hotels should improve their services to attract customers and get higher ratings.
