Introduction

In today's data-driven landscape, businesses across a variety of industries are acknowledging the transformative potential of data analytics. Here's an example where I leveraged data analytics to drive informed decision-making in the hospitability sector, centered around key areas such as pricing, marketing, and so forth.

In this case, I accomplished the following tasks:

• Scraping data from Booking.com with the Selenium package

• Performing data cleansing and analysis to extract actionable insights

• Presenting stakeholders with impactful visualizations of the findings

• Providing recommendations to improve strategic hotel management

For code reference, please visit my GitHub REPO.

Data Preparation

Sourced from Booking.com, the dataset used in this case consists of variables such as price, discount, rating, room type, distance to the city center, and review (see image). Consequently, it can be leveraged to drive strategic hotel management decisions in the next step.

Data Analysis

Let's venture into our dataset and focus on answering  two main questions :

• What is the optimal pricing strategy?

• Which marketing strategies can be conducted?

To begin, it's better to answer some  satellite questions  such as:

• What proportion of hotels offer discounts compared to those without discounts?

• What are the most common room types available? Are there any room types that are more expensive or more discounted on average?

• Is there a relationship between the distance to the city center and the price? Do accommodations closer to the center tend to be more expensive or have higher discounts?

Data Visualization

 Statistics approach  can help address the above questions. Meanwhile,  good visuals  will assist in presenting the insights to our stakeholders. Below are my chart samples. Kindly take a look!

For detailed analysis, you can refer to my version HERE. However, general insights that we can drive from these charts include the following:

• The majority of hotels do not offer discounts. 

• The dataset includes diverse room types, with double rooms being the most common. Superior rooms have fewer discounts but offer significant price reductions. Standard and deluxe rooms have a higher availability of discounts.

• Prices tend to decrease as the distance from the city center increases.