Atliq Grands aimed to analyze revenue performance across its hotels and identify factors influencing revenue generation to optimize pricing and operational efficiency. The project utilized five datasets: dim_date (dates), dim_hotels (hotel properties), dim_rooms (room categories), fact_aggregated_bookings (aggregated bookings), and fact_bookings (individual bookings, including revenue and platforms).

The project involved data cleaning, merging datasets, and conducting exploratory data analysis (EDA) to identify trends and patterns. Data visualization techniques were used to present key findings, including total revenue per hotel and room class, average revenue per night, and revenue by booking platform.

Revenue performance varies across hotels and room classes. Presidential rooms generally have higher average revenue per night. Booking platforms like ‘makeyourtrip’ and ‘others’ contribute most to revenue. Promote Elite and Premium rooms in underperforming hotels. Strengthen partnerships with high-revenue platforms. Adjust pricing and promotions based on average revenue per night, especially for Presidential rooms. Evaluate underperforming hotels for improvement in offerings and pricing. Prioritize customer experience and service quality to enhance satisfaction and loyalty.