This project was developed as part of the Codebasics Resume Challenge and centres on AtliQ Grands, a fictional chain of five-star hotels across India. The company has been experiencing a decline in market share and revenue in the luxury/business hotel segment, primarily due to inefficient management decisions. This project leverages business intelligence and data analytics to support strategic decision-making and business revitalization.
AtliQ Grands owns multiple five-star hotels across India. They have been in the hospitality industry for the past 20 years. Due to strategic moves from other competitors and ineffective decision-making in management, AtliQ Grands are losing its market share and revenue in the luxury/business hotels category. As a strategic move, the managing director of AtliQ Grands wanted to incorporate "Business and Data Intelligence" to regain their market share and revenue. However, they do not have an in-house data analytics team to provide them with these insights. Their revenue management team had decided to hire a 3rd party service provider to provide them with insights from their historical data.
To design a data-driven Power BI dashboard that helps AtliQ Grands identify revenue trends, optimize pricing strategies, and improve booking efficiency across properties by analyzing key hospitality metrics such as ADR, RevPAR, Occupancy %, and platform performance.
- Hotel Chain: AtliQ Grands
- Locations: 4 major cities across India
- Properties: 7 five-star hotels
- Room Categories: Standard, Elite, Premium, Presidential
- Booking Channels: 6 key online platforms plus other direct channels
- dim_date: Calendar data including week numbers, months, and day types
- dim_hotels: Property IDs, names, categories, and locations
- dim_rooms: Room class information
- fact_aggregated_bookings: Daily bookings, capacity, check-in dates by room category and hotel
- fact_bookings: Detailed booking information (ID, guest count, revenue, platform, status, and ratings)
- Total Bookings | Total Revenue | Total Capacity | Average Rating | Realisation %
- Total Successful Bookings | Occupancy % | Total Cancelled Bookings
- Cancellation Rate | Revenue Loss | ADR | RevPAR | No Show Rate % | DSRN
- Power BI – for interactive data visualization and dashboard development
- DAX – for creating dynamic measures and KPIs
- Excel / CSV – for importing and cleaning sample hospitality data
- Data Modeling – building relationships and optimizing the data model
- Mumbai leads in revenue generation, contributing 668.6M, followed by Bangalore (420.4M), Hyderabad (325.2M), and Delhi (294.5).
- Luxury rooms contributed the most to total revenue (61.61%), while the Business category contributed 38.39%.
- Weekends outperform weekdays in both RevPAR (₹7,927 vs ₹7,101) and occupancy (62.08% vs 56.0%) — suggesting pricing strategies can be optimized for weekends.
- AtliQ Exotica and AtliQ Palace are the top revenue-generating properties with over ₹300M each.
- AtliQ Bay (Mumbai) and AtliQ Seasons have the lowest guest ratings (2.3–2.4) and higher cancellation percentages.
- The “Others” booking platform has the highest share of bookings (40.91%) but also the most cancellations (~13.7K).
- Standard and Presidential rooms have the highest cancellation rates compared to other room types.
- Overall occupancy rate is 57.79% with an ADR of ₹12,695 and a RevPAR of ₹7,337.
- Guest satisfaction averages 3.62 out of 5 across all properties.
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Enhance Customer Ratings: Customer ratings can be enhanced by delivering exceptional service, maintaining high standards of cleanliness, and offering quality food, all of which directly impact bookings and overall revenue.
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Optimize Room Pricing Based on Occupancy: With an overall occupancy rate of 57.9%, the management should explore dynamic pricing strategies, particularly for properties with lower occupancy, to boost room utilization and increase revenue.
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Implement Dynamic Pricing: As the Average Daily Rate (ADR) is almost the same on weekdays and weekends, using dynamic pricing for all properties and platforms can help increase revenue.
Special thanks to Codebasics for hosting this insightful challenge and providing a real-world case study for hands-on analytics learning