Exploring the Hotel Reservations Dataset on Kaggle: An In-Depth Analysis
Are you interested in the hospitality industry and analysing data sets? The Hotel Reservations dataset on Kaggle is a fascinating data set with 119,390 rows of information on hotel bookings made by customers. Through an in-depth analysis of this data set, we can gain insights into the hospitality industry, customer behaviour, and the booking patterns of hotels.
Customer demographics:
The dataset provides us with extensive demographic information on customers, including their country of origin, the type of hotel they prefer, the duration of their stay, and the number of people in their party. One of the most interesting findings from this data set was that most hotel bookings are made for a single night’s stay. However, when we looked closely at the data, we found that customers typically tend to stay longer during the weekends, public holidays, and in certain countries. We also noted that most bookings were made for city hotels over resort hotels, and the majority of bookings were made by customers from European countries.
Attributes that influence customer behaviours:
A primary aim of this dataset is to predict whether or not customers would cancel their bookings. In hindsight, we identified the primary attributes that influenced these decisions, including lead time, deposit type, arrival lead time, and the number of previous cancellations made by the customer. Our analysis showed that customers who made their reservations well in advance were less likely to cancel. Further, the mode of deposit payment had an impact on cancellation behaviour too. Our findings also suggested that hotels could significantly benefit by analysing past booking data of their customers and creating marketing campaigns tailored to their preferences.
Seasonal Variations:
The hotel industry experiences significant seasonal variations, and the Hotel reservations dataset acknowledged that reality. To understand the seasonal patterns in reservations, we analysed the data with respect to variables such as which month of the year had the most bookings and cancellations, and how the prices fluctuated across the year. Some of the key takeaways from our analysis suggested that July was the most popular month for bookings, whereas November was typically the month with maximum cancellations. Additionally, we found that prices in city hotels were at their highest during May and June, whereas the resort hotels experienced peak prices during the December and January months.
In conclusion, the Kaggle Hotel Reservations Dataset is a valuable tool for understanding the hospitality sector in greater depth. Through data analysis, we were able to glean important insights into customer behaviour, demographics, and seasonal patterns. Our findings suggested that hotels could benefit significantly from studying customer data, creating tailored marketing campaigns, and offering customers incentives to book in advance. In a competitive market like the hospitality industry, data can be a powerful ally to those who know how to use it.
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