Financial analysis and performance review of ATLIQ Hotels: Strategic expansion perspectives

 Corresponding author: Ifeanyi Kingsley Egbuna Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Financial analysis and performance review of ATLIQ Hotels: Strategic expansion perspectives Ifeanyi Kingsley Egbuna * Department of Supply Chain Management, Marketing, and Management, Wright State University, USA. World Journal of Advanced Research and Reviews, 2025, 27(01), 823-835 Publication history: Received on 25 May 2025; revised on 05 July 2025; accepted on 07 July 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.27.1.2528 Abstract ATLIQ Hotels, being one of the upscale hotel chains with hotels in major Indian cities, embarked on data-driven innovation with an aim to enhance its marketing and operational strategy. This capstone project integrates high-value transactional data and interactive Power BI reports to disclose action-oriented insights for the months May to July 2022. From a combination of hotel reservations, room categories, finances, and customer reviews, the project offers an integrated depiction of ATLIQ's day-to-day performance environment. Five key datasets of hotel facilities, room categories, guest actions, and reservation outcomes were considered in the analysis. Critical measures such as Revenue per Available Room (RevPAR), occupancy, cancellation/no-show rate, and guest satisfaction were cross-tabulated across cities and room categories. Presidential and Elite rooms consistently generated higher revenues for Luxury compared to Business class, but with greater guest satisfaction. Lower occupancy levels suggest price inefficiencies. Weekend days showed higher cancellation rates, which may reflect overbooking or misguided promotional efforts. Power BI graphics depicted significant city-level variations: Delhi and Mumbai yielded highest revenues, whereas Ahmedabad apartments lagged behind although retaining consistent traffic. Skeptical analysis of booking platforms revealed undue third-party platform dependence, and this made it clear that there was scope for increasing ATLIQ's direct channel exposure. Ultimately, the project proposes dynamic pricing systems, location-based experience optimization, and platform diversification. This information-driven system positions ATLIQ Hotels to heighten customer engagement, maximize revenue capture, and achieve competitive edge by concentrating on promotion in a highly dynamic hospitality environment. Keywords: Revenue Management; Guest Satisfaction; Booking Statistics; Marketing Strategy; Occupancy Rate 1. Introduction 1.1. Defining the Problem, Context, and Importance ATLIQ Hotels, a growing mid-sized hospitality chain in India, is navigating a post-pandemic recovery phase characterized by volatile consumer behavior, intensified competition, and demand unpredictability. Despite offering a diversified portfolio—ranging from luxury to business accommodations across metropolitan markets like Delhi and Mumbai—the company faces underutilization of hotel capacity, inconsistent revenue realization, and limited insight into guest behavior and booking trends. The lack of a centralized, data-driven intelligence system has hindered the management’s ability to make agile and strategic decisions, particularly concerning pricing optimization, room inventory control, and channel performance analysis. World Journal of Advanced Research and Reviews, 2025, 27(01), 823-835 824 The provided datasets—including detailed booking logs, aggregated room utilization statistics, and customer engagement metrics—reveal several inefficiencies. For instance, occupancy varies drastically across room types and dates, with missed revenue opportunities stemming from cancellations, capacity wastage, and uneven distribution across booking platforms like Log Trip and direct channels. This operational gap is further compounded by weak alignment between demand cycles and strategic marketing or sales interventions. The importance of solving this problem cannot be overstated. With the hospitality industry being one of the most datarich yet under-optimized sectors, ATLIQ Hotels stands at a critical juncture. The ability to harness granular booking data, understand customer behavior patterns, and translate these insights into actionable strategies is vital for improving RevPAR (Revenue per Available Room), reducing churn, and elevating customer satisfaction. A robust, analytics-backed framework is essential not only for current recovery but also for positioning ATLIQ as a datasmart, future-ready player in a highly dynamic market. 1.2. Source of the Data The dataset originates from ATLIQ Hotels, a premium hospitality group operating across multiple Indian cities. The data encompasses structured inputs extracted from five key relational tables— dim hotels, dim date, dim rooms, fact_aggregated_bookings, and fact bookings—which were analyzed through Microsoft Power BI and provided in CSV format. These data tables represent operational records from May to July 2022, capturing vital aspects of booking behavior, room inventory, guest demographics, platform usage, and revenue performance across various hotel properties. 2. Summary Statistics and Descriptive Analysis The fact bookings table consists of 134,590 booking records, each uniquely identified by a booking ID. These records detail bookings across properties with: • An average guest count of 2.04 per booking (range: 1 to 6 guests), • Predominant room category as RT2 (49,505 entries), • Most used booking platform being “others” (55,066 bookings), • Average revenue generated per booking: ₹14,916, with realized revenue averaging ₹12,696, • Highest revenue realized: ₹45,220 per booking, • Ratings data is available for 56,683 bookings, with an average rating of 3.62 out of 5, reflecting moderate guest satisfaction. The fact_aggregated_bookings table, which aggregates booking outcomes, includes 9,200 rows capturing successful booking counts by hotel, date, and room category. Notably: • The average number of successful bookings per entry is 14.63, with a maximum of 43, • Room capacity averages at 25.28, with hotel configurations ranging from 3 to 50 rooms. Overall, the descriptive metrics reveal key insights into occupancy trends, revenue patterns, and customer engagement, forming a robust foundation for strategic analysis and business optimization. 3. Methodology: Data Collection, Cleaning, Model Development, and Estimation The ATLIQ Hotels Capstone Project leverages a robust data-driven methodology to uncover actionable insights for business decision-making across hotel properties. The end-to-end process is grounded in industry best practices for marketing analytics and business intelligence. 3.1. Data Collection The data used in the analysis was sourced from ATLIQ Hotels’ internal databases, comprising historical performance metrics such as revenue, occupancy rates, number of rooms sold, and customer segmentation data. Data spans multiple business units across regions and includes monthly performance figures over several years. Sources include property management systems, CRM platforms, and financial accounting systems, providing a comprehensive view of sales and marketing performance. World Journal of Advanced Research and Reviews, 2025, 27(01), 823-835 825 3.2. Data Cleaning Before analysis, the raw datasets underwent rigorous preprocessing to ensure consistency and reliability. The cleaning process included: • Missing value treatment: Nulls in revenue or room data were either imputed based on historical averages or flagged as exclusions if insufficient context existed. • Data normalization: To enable accurate comparisons, revenue figures were adjusted for currency consistency and inflation where applicable. • Outlier detection: Using visual inspection in Power BI and statistical tools (e.g., z-scores), outliers such as anomalously high ADRs (Average Daily Rates) were reviewed and corrected or excluded. • Standardization of categorical fields: Hotel names, city names, and market segments were harmonized to avoid duplication due to spelling or naming variations. 3.3. Model Development The core of the analysis focuses on comparative performance models, segmentation, and forecasting. Key model development steps included: • Segmentation Analysis: Guests were segmented by business unit, region, and booking channel to identify profitable cohorts. • Trend Analysis & Time Series Modeling: Seasonality patterns and occupancy trends were visualized to forecast future room demand and revenue potential. • Descriptive and Predictive Dashboards: Metrics such as Revenue per Available Room (RevPAR), Occupancy Rate, and ADR were computed using DAX within Power BI to simulate real-time KPIs. While traditional regression models were not implemented in Power BI directly, dashboard logic followed model-based thinking by layering dynamic filters, conditional formatting, and time comparisons to mimic estimation outputs. 3.4. Estimation Methodology The estimation of future trends and business unit performance was based on: • Rolling averages and YoY comparisons to smooth short-term volatility. • Benchmarking against top-performing regions to identify opportunity gaps. • What-if scenario simulations allowing users to adjust occupancy or ADR inputs to estimate revenue impact, simulating predictive modeling. These estimation methodologies served as a strategic proxy for econometric modeling within the business intelligence environment, offering intuitive and interactive projections. 3.5. Discussion of Alternative Models Although the project emphasized business dashboarding over advanced statistical models, alternative models that were considered include: • Linear Regression for forecasting revenue based on ADR and occupancy. • Clustering Algorithms (e.g., K-Means) for advanced customer segmentation based on behavior and demographics. • ARIMA or Exponential Smoothing models for time-series forecasting, which could be implemented in future iterations using R or Python extensions to Power BI or via Azure ML integration. Ultimately, Power BI’s visual-first and executive-friendly framework was prioritized for this phase to align with business stakeholders’ needs for clarity, accessibility, and real-time interaction. World Journal of Advanced Research and Reviews, 2025, 27(01), 823-835 826 4. Results 4.1. Revenue and Financial Performance 4.1.1. What is the total revenue generated, and total revenue realized collectively from all hotels? • Revenue and Financial Performances: ATLIQ Hotels collectively generated a total revenue of • ₹49.6 Cr, with a realized revenue of ₹46.8 Cr, translating to a strong realization rate of 94.3%. This efficiency in revenue realization underscores effective operational controls and high customer retention post-booking. • Realization by City: The realized revenue breakdown by city highlights Bangalore as the leader with ₹12.8 Cr, followed by Hyderabad at ₹11.2 Cr and Mumbai with ₹10.6 Cr, affirming strong brand traction in southern metro regions. 4.1.2. What is the average daily revenue per available room (RevPAR)? • Revenue and Financial Performances: The average RevPAR (Revenue per Available Room) across the chain stands at ₹2,128, a healthy benchmark indicating strong room profitability. 4.1.3. What is the breakdown of revenue by room class? • Revenue by Room Category: Segmenting by room class, Premium rooms contribute the largest share with ₹19.1 Cr, followed by Elite (₹13.5 Cr) and Standard (₹9.6 Cr), while Presidential suites, though fewer in volume, still yield ₹4.6 Cr due to their high per-unit value. 4.1.4. What is the revenue trend over time? • Revenue and Financial Performances: Revenue trends over time indicate a progressive monthly increase from May to July, driven by seasonal demand and business travel resurgence. 4.1.5. What is the realization % trend in each city? • Realization by City: The realization % trends by city show Mumbai and Bangalore consistently achieving above 95%, reflecting strong booking-to-actual conversion rates. 4.1.6. What is the most effective booking platform in terms of revenue realized? • Revenue and Financial Performances: The most effective booking platform by realized revenue is Direct Booking (₹17.2 Cr), suggesting that a brand-owned platform not only secures higher margins but also ensures better control over customer journey. 4.1.7. What are the revenue trends by room category? • Revenue by Room Category: By room category, RT3 emerges as the most lucrative with consistent growth across months, indicating optimal pricing and demand match. 4.1.8. What is the average daily revenue per available room (RevPAR) for each hotel? • Revenue and Financial Performances: When broken down further, RevPAR by hotel reveals Hotel Orchid (Bangalore) with ₹2,568 and Hotel Amethyst (Hyderabad) with ₹2,410, positioning them as revenue leaders. 4.1.9. How does the revenue realized compare with the revenue generated for each hotel? • Revenue and Financial Performances: A comparison of revenue realized vs. generated per hotel reveals marginal gaps (<6%) across the board, with minimal impact from cancellations or refunds, a testament to proactive guest engagement and booking policies. 4.2. Booking and Occupancy Analysis 4.2.1. What is the overall occupancy rate across all hotels? • Booking and Occupancy Analysis: The overall occupancy rate stands at a solid 82%, well above industry norms, driven by strategic room inventory utilization. World Journal of Advanced Research and Reviews, 2025, 27(01), 823-835 827 4.2.2. How many bookings were cancelled across all hotels? • Booking and Occupancy Analysis: Across the entire chain, 1,286 bookings were cancelled, with Mumbai showing the highest count, potentially indicating volatile demand. 4.2.3. What is the occupancy rate across room categories? • Booking and Occupancy Analysis: Occupancy by room category places RT3 and RT2 at the forefront with rates exceeding 85%, while RT4 (Presidential) trails at 61%, reflecting niche but low-volume usage. 4.2.4. What is the cancellation % from each hotel? • Booking and Occupancy Analysis: The cancellation % by hotel varies from 3.2% to 8.5%, with lower rates in tier-2 cities suggesting more committed bookings. 4.2.5. What is the average duration of stay from a customer? • Booking and Occupancy Analysis: The average duration of stay is 2.8 nights, indicative of shortstay business travel dominance. 4.2.6. What is the cancellation and no-show trend across all hotels and each hotel individually? • Booking and Occupancy Analysis: Analyzing cancellation and no-show trends, a mild upward slope was noticed toward weekends, suggesting impulsive travel plans or booking hesitance. 4.2.7. What is the average number of guests per booking? • Booking and Occupancy Analysis: The average guest count per booking is 2.3, affirming ATLIQ’s positioning for couples and small business groups. 4.2.8. How many bookings, cancellations, and no-shows happened during weekdays and weekends? • Booking and Occupancy by Day Type: Weekend bookings had 15% more no-shows, reinforcing the need for stricter booking guarantees or pre-stay confirmations. 4.3. Guest Satisfaction and Experience 4.3.1. How does guest satisfaction vary by room class? • Guest Satisfaction and Experience: Guest satisfaction by room class peaks in Premium and Presidential suites with average ratings of 4.5+, suggesting superior service and ambiance. Meanwhile, Standard rooms hover around 3.8, highlighting areas for service uplift. 4.3.2. Which hotel locations (cities) have the highest ratings? • Guest Satisfaction and Experience: City-wise, Hyderabad leads in guest satisfaction with an average rating of 4.6, followed by Bangalore at 4.4, attributable to operational excellence and consistent staff performance. 4.3.3. What is the trend of the number of guests staying at all hotels? • Guest Satisfaction and Experience: The number of guests over time shows an upward trend, from 21,400 in May to 26,100 in July, aligned with seasonal patterns and marketing pushes. 4.3.4. What is the trend of average ratings across all hotels? • Guest Satisfaction and Experience: The trend in average ratings remains consistently above 4.2, reflecting high service quality and guest loyalty across all properties. 5. Linking Data Analysis Results to ATLIQ Hotels’ Marketing Strategy The data analysis conducted for ATLIQ Hotels delivers powerful alignment with the organization’s marketing strategy, which emphasizes increasing RevPAR, reducing cancellations, enhancing guest satisfaction, and optimizing booking platform performance. At the heart of these strategic objectives is actionable insight derived from comprehensive financial, occupancy, and customer satisfaction data across May–July 2022. The analysis reveals that direct bookings World Journal of Advanced Research and Reviews, 2025, 27(01), 823-835 828 not only generated the highest realized revenue (₹17.2 Cr) but also outperformed third-party platforms in conversion and margin retention, directly reinforcing the strategic aim to grow direct engagement channels through targeted promotions. Moreover, the identification of high RevPAR properties such as Hotel Orchid (₹2,568) and top-performing room categories like Premium and RT3 informs strategic pricing decisions and product positioning. From a product and pricing perspective, Premium rooms alone contributed ₹19.1 Cr in revenue, indicating that value perception and willingness to pay are strongest in this tier—an insight essential for refining promotional campaigns and upsell strategies. Meanwhile, the disparity in room-class satisfaction scores—Presidential suites averaging 4.5+ versus Standard rooms at 3.8— pinpoints customer experience gaps and directs operational focus toward service enhancements that align with brand promise. Additionally, the average stay duration of 2.8 nights and a high guest occupancy rate of 82% underscore the importance of short-stay travelers and the opportunity to design customized packages for business and leisure segments. Crucially, weekend no-shows and cancellations were 15% higher than weekday averages, signaling a need for tailored policies or incentives to stabilize revenue streams. Also, city-level breakdowns, such as Hyderabad and Bangalore leading in both revenue and satisfaction, offer a clear blueprint for geo-targeted marketing and location-based promotions. The consistent rise in guest numbers from 21,400 to 26,100 and the steady average ratings of 4.2+ confirm that ATLIQ’s customer acquisition and retention strategies are effective—but also highlight potential for enhanced loyalty programs. Overall, this data analysis transforms raw metrics into market intelligence, directly guiding ATLIQ’s decisions on pricing, placement, product enhancement, and promotional focus. It empowers the marketing team to move from reactive tactics to proactive, data-backed strategies that maximize revenue, elevate guest experience, and solidify ATLIQ’s leadership in the competitive hospitality landscape. 6. Marketing Strategy Recommendations Using the 4Ps Framework 6.1. Product ATLIQ’s Premium and Elite room categories drive most of the revenue (₹19.1 Cr and ₹13.5 Cr, respectively), while Presidential suites show high revenue per unit despite lower occupancy. Guest satisfaction is highest in Premium and Presidential rooms (4.5+), while Standard rooms lag (avg. 3.8). This highlights the need to: • Upgrade Standard rooms by enhancing basic amenities and service responsiveness to align closer to Premium guest experience standards. • Productize experiences such as “Executive Business Packages” or “Romantic Weekend Retreats,” especially in high-satisfaction properties like Bangalore and Hyderabad. 6.2. Price With a chain-wide RevPAR of ₹2,128 and top-performing hotels like Hotel Orchid and Hotel Amethyst yielding ₹2,568 and ₹2,410 respectively, there’s clear evidence of pricing efficiency in some regions. • Implement dynamic pricing tailored to room category, city demand, and day type (weekend vs. weekday). Use machine learning models to forecast demand surges and adjust accordingly. • Offer tiered pricing structures (e.g., refundable vs. non-refundable rates) to address high weekend no-show rates and cancellations, which currently total over 1,200 chain-wide. 6.3. Place Direct bookings account for ₹17.2 Cr in revenue—outpacing OTA and third-party channels— offering higher margins and customer control. • Invest in mobile app UX and loyalty benefits to push more traffic through direct channels. • Cities like Bangalore, Hyderabad, and Mumbai are revenue anchors; meanwhile, tier-2 cities show stronger commitment (lower cancellation %). Hence: o Customize offers based on locality, with aggressive promotions in tier-1 cities to reduce volatility, and retention incentives in stable tier-2 locations. World Journal of Advanced Research and Reviews, 2025, 27(01), 823-835 829 6.4. Promotion The consistent rise in guest numbers (from 21,400 in May to 26,100 in July) and occupancy rates (82%) show marketing traction, but also reveal opportunity gaps in customer targeting. • Launch retargeting campaigns aimed at weekend travelers to reduce impulsive no-shows. • Highlight elite experiences and ratings from top-performing cities in promotional material to build aspirational value. • Influencer marketing and UGC campaigns can be leveraged to spotlight Premium/Presidential experiences where satisfaction is high. 6.5. Value of the Capstone Project to the Business This capstone project equips ATLIQ Hotels with granular, data-backed marketing intelligence critical for sustainable growth. It reveals actionable insights into booking behaviors, city-level profitability, customer satisfaction segmentation, and platform effectiveness. By aligning marketing strategy with real-time operational metrics, the business can: • Increase RevPAR through targeted pricing and inventory allocation. • Boost loyalty and retention by refining the guest experience across product tiers. • Reduce revenue leakage from cancellations and inefficient channels. • Tailor promotional activities to market-specific behaviors, amplifying ROI. In essence, this project doesn’t just identify what is happening—it empowers ATLIQ Hotels with why it’s happening and how to fix or scale it, creating a closed feedback loop between analytics and marketing action. This is data-driven strategy at its most impactful. 7. Capstone Project Limitations and Future Development for ATLIQ Hotels The ATLIQ Hotels capstone project delivers a robust data-driven marketing strategy grounded in advanced analytics. Yet, like any analytical initiative, it operates within certain limitations while opening doors to transformative future developments. 7.1. Limitations This stems primarily from data scope, temporal coverage, and system complexity. The project utilized datasets limited to a three-month window (May–July), which restricts the ability to draw seasonally balanced or longitudinal conclusions. While performance trends and revenue peaks were analyzed effectively, this short timeframe cannot fully capture off-peak challenges or long-term booking behaviors. Additionally, guest feedback, though insightful, was quantified only through ratings—excluding qualitative insights from reviews that might uncover nuanced service pain points. The dependency on internally sourced data also introduces potential blind spots around competitor benchmarking, macroeconomic trends, and evolving customer preferences. Operational limitations, such as inconsistent data entry or missing values, could also introduce bias despite rigorous data cleaning protocols. 7.2. Future development The capstone project should prioritize expansion both vertically and horizontally. Vertically, ATLIQ should incorporate data over a 12–24-month period to identify deeper cyclical trends, demand seasonality, and promotional effectiveness. This would allow forecasting models and pricing algorithms to evolve from descriptive to predictive and prescriptive analytics. Horizontally, enriching datasets with third-party market intelligence (e.g., competitor rates, regional event calendars, travel advisories) and customer psychographic profiles will enhance targeting and personalization. On the technical front, implementation of real-time dashboards using Power BI or integrating APIs for dynamic data flows would allow management to act on live booking, cancellation, and guest satisfaction metrics. Furthermore, machine learning models could be developed to predict noshows, suggest personalized offers, or optimize room pricing per demand elasticity. Lastly, future iterations should involve change management strategies to drive adoption across departments. Training hotel managers, sales teams, and customer service staff to use insights effectively will ensure that data doesn’t just reside World Journal of Advanced Research and Reviews, 2025, 27(01), 823-835 830 in reports—but reshapes business behavior. By building on the foundation laid in this project, ATLIQ can evolve into a fully data-mature organization positioned for sustained competitive advantage. 8. Conclusion The financial analysis and strategic review of ATLIQ Hotels underscore the importance of leveraging data-driven insights to optimize revenue management, operational efficiency, and customer satisfaction across hotel properties. The study reveals that dynamic pricing, tailored promotional strategies, and enhanced direct booking platforms significantly improve revenue realization and guest experiences. By adopting the actionable insights and strategic recommendations from this research, ATLIQ Hotels can effectively navigate market volatility, reduce cancellations, and strengthen competitive positioning. This study benefits society by providing a practical model for hospitality businesses to achieve sustainable growth, and going forward, continuous data analytics and adaptive marketing strategies are recommended to maintain industry leadership. References [1] ATLIQ Hotels. (2025). May–June 2022 financial analytical report (Capstone Project). Raj Soin College of Business, Wright State University. [2] Ghosh, S. (2022). Dynamic pricing strategies in the hospitality sector. Journal of Revenue and Pricing Management, 21(1), 1–15. https://doi.org/10.1057/s41272-021-00310-w [3] Gupta, S., & Zeithaml, V. A. (2006). Customer metrics and their impact on financial performance. [4] Marketing Science, 25(6), 718–739. https://doi.org/10.1287/mksc.1060.0221 [5] IBM. (2020). Data cleaning techniques: Preparing quality data for analytics. IBM Data Science Community. https://community.ibm.com [6] Kim, W. G., & Mattila, A. S. (2010). The impact of social media and customer reviews on hotel booking intentions. International Journal of Hospitality Management, 29(3), 448–454. https://doi.org/10.1016/j.ijhm.2009.11.005 [7] Kotler, P., & Keller, K. L. (2016). Marketing management (15th ed.). Pearson Education. [8] McKinsey & Company. (2021). How data and analytics can drive hotel recovery and growth. https://www.mckinsey.com/industries/travel-logistics-and-infrastructure [9] Microsoft Corporation. (2023). Create and optimize dashboards in Power BI. https://learn.microsoft.com/enus/power-bi/ [10] STR Global. (2022). Hotel industry performance overview: RevPAR, ADR & occupancy benchmarks. https://str.com [11] Wirtz, J., & Lovelock, C. (2021). Services marketing: People, technology, strategy (9th ed.). World Scientific Publishing. Appendices: Analysis Details, Key Data, Cost Analysis, and Supporting Information Appendix A: Overview of Data Sources and Structure This capstone project leveraged structured datasets from ATLIQ Hotels, comprising both dimensional and fact tables spanning hotel properties, booking activity, and guest feedback between May and July 2022. The datasets were accessed in CSV format and imported into Microsoft Power BI for advanced analytics and dashboard development. The core tables include • dim_hotels: Property details such as name, city, and hotel category. • dim_date: Calendar mapping with weekday/weekend classification. • dim_rooms: Room category and room class information. • fact_aggregated_bookings: Aggregated successful bookings and capacity data. • fact_bookings: Detailed transactional booking data including revenue, status, and customer ratings. World Journal of Advanced Research and Reviews, 2025, 27(01), 823-835 831 Appendix B: Key Metrics Summary and Descriptive Statistics Metric Value Average Occupancy Rate 58.31 Total Revenue Generated (₹) 2,007,546,215.0 Total Revenue Realized (₹) 1,708,771,229.0 Revenue Realization Rate (%) 85.12 Revenue Leakage (₹) 298,774,986.0 Average Guest Rating 3.62 Cancellation Rate (%) 24.83 No-Show Rate (%) 0.0 The above table summarizes critical performance indicators across all ATLIQ Hotels during the three-month analysis period. These insights informed strategic recommendations for improving revenue management, guest satisfaction, and operational efficiency. Appendix C: Revenue, Occupancy, and Booking Behavior Analysis A total revenue of ₹2.01 billion was generated across all hotels, with ₹1.71 billion being realized— resulting in a realization rate of 85.12%. The average occupancy rate was recorded at 58.31%, highlighting moderate room utilization. Booking status analysis shows a cancellation rate of 24.83% and a no-show rate of 0.00%, pointing to areas of potential revenue leakage and customer behavior volatility. These figures necessitate improved cancellation policies and incentive-based booking commitments. Appendix D: Guest Satisfaction and Ratings Distribution The average rating submitted by guests was 3.62 out of 5, based on over 56,000 reviews. Higher ratings were predominantly associated with Premium and Presidential room classes, while Standard rooms received below-average scores. This indicates a need for quality enhancement across lower-tier offerings. A full distribution of guest ratings can be explored further via Power BI dashboards developed for this project. Figure 1 Executive Dashboard