Optimizing Hotel Booking Strategies with Advanced SQL Analytics

Project Overview

In today’s competitive hospitality industry, data-driven decision-making is crucial for maximizing revenue, reducing cancellations, and enhancing customer satisfaction. This project demonstrates the power of SQL in analyzing hotel booking data to uncover trends, predict cancellations, and optimize pricing strategies. By leveraging advanced SQL queries, this analysis provides actionable insights that can help hotels refine their operations and improve profitability.

Dataset Description

The dataset used in this analysis contains over 40,000 records of hotel bookings, capturing a wide range of information from customer demographics to booking details and financial metrics. Key fields in the dataset include:

hotel: The type of hotel (e.g., Resort Hotel, City Hotel)
is_canceled: A binary indicator of whether the booking was canceled
lead_time: The number of days between the booking date and the arrival date
arrival_date_year: The year of arrival
arrival_date_month: The month of arrival
adults: The number of adults in the booking
children: The number of children in the booking
adr: The average daily rate for the booking

These fields, along with others like meal, country, market_segment, and distribution_channel, form the basis for a comprehensive analysis of booking behaviors and outcomes.

Analytical Process

Step 1: Data Import and Preparation

The dataset was imported into PostgreSQL using the \copy command, carefully handling null values and ensuring data types were correctly mapped. The table structure was designed to optimize query performance, with key columns indexed to facilitate fast retrieval of booking and cancellation data.

CREATE TABLE bookings (
    id SERIAL PRIMARY KEY,
    hotel VARCHAR(100),
    is_canceled INTEGER,
    lead_time INTEGER,
    arrival_date_year INTEGER,
    arrival_date_month VARCHAR(20),
    arrival_date_week_number INTEGER,
    arrival_date_day_of_month INTEGER,
    stays_in_weekend_nights INTEGER,
    stays_in_week_nights INTEGER,
    adults INTEGER,
    children INTEGER,
    babies INTEGER,
    meal VARCHAR(20),
    country VARCHAR(50),
    market_segment VARCHAR(50),
    distribution_channel VARCHAR(50),
    is_repeated_guest INTEGER,
    previous_cancellations INTEGER,
    previous_bookings_not_canceled INTEGER,
    reserved_room_type VARCHAR(5),
    assigned_room_type VARCHAR(5),
    booking_changes INTEGER,
    deposit_type VARCHAR(20),
    agent VARCHAR(10),
    company VARCHAR(10),
    days_in_waiting_list INTEGER,
    customer_type VARCHAR(20),
    adr DECIMAL(10, 2),
    required_car_parking_spaces INTEGER,
    total_of_special_requests INTEGER,
    reservation_status VARCHAR(20),
    reservation_status_date DATE
);

Step 2: Data Exploration

Initial exploration involved querying the dataset to understand the distribution of bookings across different hotels, identify the most common booking channels, and assess the average lead time for bookings. These insights provided a foundation for deeper analysis.

SELECT hotel, COUNT(*) AS total_bookings
FROM bookings
GROUP BY hotel
ORDER BY total_bookings DESC;

This query revealed that the majority of bookings were made at Resort Hotels, prompting further investigation into customer preferences and booking behaviors.

Step 3: Advanced SQL Queries

Cancellation Analysis

Understanding cancellation patterns is crucial for improving booking policies and reducing revenue loss. By analyzing the distribution of cancellations across different booking channels and lead times, this analysis identified high-risk bookings that could be targeted with tailored interventions.

SELECT distribution_channel, 
       COUNT(*) AS total_bookings, 
       SUM(is_canceled) AS cancellations, 
       (SUM(is_canceled) * 100.0 / COUNT(*)) AS cancellation_rate
FROM bookings
GROUP BY distribution_channel
ORDER BY cancellation_rate DESC;

The analysis showed that bookings made through online travel agencies had the highest cancellation rates, particularly when booked far in advance. This insight suggests the need for stricter cancellation policies or targeted offers to secure these bookings.

Revenue Optimization

Another critical aspect of the analysis was understanding how lead times and booking changes affected revenue. By examining the relationship between lead_time, booking_changes, and adr, this analysis provided recommendations for optimizing pricing strategies.

SELECT lead_time, 
       AVG(adr) AS avg_revenue, 
       AVG(booking_changes) AS avg_changes
FROM bookings
WHERE is_canceled = 0
GROUP BY lead_time
ORDER BY lead_time;

This query revealed that bookings with shorter lead times tended to generate higher revenue and fewer changes, suggesting that last-minute pricing strategies could be beneficial.

Results and Insights

The analysis provided several key insights:

Cancellation Rates: Direct bookings had the lowest cancellation rates, while bookings through online travel agencies were more likely to be canceled, particularly with longer lead times.
Revenue Optimization: Shorter lead times were associated with higher average daily rates and fewer booking changes, suggesting that dynamic pricing strategies could be used to capitalize on last-minute bookings.

These findings can help hotels refine their booking policies, optimize pricing strategies, and ultimately improve profitability.

Conclusion and Next Steps

This project demonstrates the power of SQL in transforming raw data into actionable insights that drive business decisions. By understanding customer behavior and optimizing booking strategies, hotels can enhance their competitive edge in the market.

Future Work: Future analysis could involve integrating external data sources, such as weather patterns or local events, to further refine booking strategies. Additionally, machine learning models could be developed to predict cancellations and optimize pricing dynamically.

Code and Documentation

All the SQL queries, data import scripts, and related documentation are available in my GitHub repository.

Tim Robbins