This dataset is a synthetic representation of mobile and online booking trends at Marriott International. It includes 500 records with key attributes related to customer bookings, loyalty membership status, booking platforms, payment methods, and room types. This dataset is useful for data analysis, machine learning models, business intelligence, and hospitality industry insights.

Features Included: Booking ID: Unique identifier for each booking. Customer ID: Anonymized customer identifier. Booking Date: The date when the booking was made. Check-in Date & Check-out Date: Dates for the customer’s stay. Booking Platform: Source of booking (Mobile App, Website, Third-Party OTA). Payment Method: Method used for transaction (Credit Card, PayPal, Points Redemption, etc.). Loyalty Membership Status: Marriott Bonvoy membership tier (Non-Member, Silver, Gold, Platinum, Titanium, Ambassador). Room Type: The type of room booked (Standard, Deluxe, Suite, Presidential Suite). Booking Amount ($): Total cost of the stay. Discount Applied (%): Any promotional discount used during booking. Cancellation Status: Indicates whether the booking was canceled.

Potential Use Cases: Analyzing booking trends in the hospitality industry. Customer behavior analysis based on loyalty membership tiers. Predicting booking cancellations using machine learning. Revenue forecasting based on room types and booking platforms. Hotel marketing optimization using discount and pricing strategies.

In the increasingly competitive tourism industry, data analysis is crucial for understanding customer behavior and optimizing business operations. This dataset was created to serve as a practical resource for data scientists, analysts, and students.

Users can leverage this dataset to address real-world problems, such as:

Exploratory Data Analysis (EDA): Identify travel trends, the most popular destinations, or the most effective sales channels.

Customer Segmentation: Group customers with similar characteristics (e.g., students who prefer beach holidays, young professionals interested in adventure travel) to develop targeted marketing strategies.

Predictive Modeling: Build models to predict customer satisfaction, the likelihood of a customer returning, or even to forecast revenue.

Pricing & Discount Optimization: Analyze the impact of discount rates on revenue and booking decisions.

Data Visualization: Create interactive dashboards to monitor business performance. booking_id: String - A unique identifier for each booking transaction.

booking_date: Date - The date the customer made the booking.

travel_date: Date - The departure date of the tour.

customer_id: String - A unique identifier for each customer.

cust_segment: Categorical - The segment to which the customer belongs (e.g., 'Student', 'Young Professional', 'Family', 'Corporate').

tour_type: Categorical - The type of tour (e.g., 'Beach', 'Cultural', 'Adventure', 'City Break').

channel: Categorical - The channel through which the booking was made (e.g., 'Website', 'Mobile App', 'Call Center').

payment_method: Categorical - The payment method used by the customer (e.g., 'Card', 'Bank Transfer', 'E-wallet').

destination: Categorical - The primary destination city/province of the tour (e.g., 'Hue', 'Phu Quoc', 'Hoi An').

pax: Integer - The number of people in the booking.

base_price_vnd: Float - The listed price of the tour per person, in Vietnamese Dong (VND).

discount_rate: Float - The percentage discount applied to the booking, represented as a decimal (e.g., 0.1 for 10%).

booking_status: Categorical - The status of the booking (e.g., 'Completed', 'Cancelled').

revenue_vnd: Float - The actual revenue generated from the booking after the discount is applied. Calculated as: revenue_vnd = (base_price_vnd * pax) * (1 - discount_rate).

satisfaction: Float - The customer's satisfaction score after the trip, on a scale from 1 to 5.

is_returning: Boolean - Indicates whether the customer has booked a tour with the agency before (True/False).