ADVANCED ANALYTICS AND MACHINE LEARNING FOR REVENUE OPTIMIZATION IN THE HOSPITALITY INDUSTRY: A COMPREHENSIVE REVIEW OF FRAMEWORKS

Abstract

Revenue management in the hospitality industry has undergone a profound transformation with the integration of advanced data analytics, machine learning (ML), and artificial intelligence (AI)-driven optimization strategies. Traditional revenue management relied heavily on historical booking patterns, seasonal trends, and manual adjustments, which often lacked the flexibility to respond to sudden market fluctuations and evolving customer preferences. In contrast, modern revenue management incorporates real-time data processing, predictive analytics, and AI-powered decision-making to dynamically optimize pricing, demand forecasting, and customer segmentation. This study employs a case study approach, analyzing seven hospitality businesses, including hotel chains, boutique hotels, and resorts, to examine the effectiveness of ML-driven dynamic pricing, big data-enhanced demand forecasting, natural language processing (NLP)-based sentiment analysis, and cloud-integrated revenue management systems. The findings reveal that businesses leveraging AI-powered pricing models and big data analytics achieved an average revenue increase of 20%, with 22% improvement in profit margins, and a 16% reduction in operational costs through the integration of IoT-based predictive maintenance and resource optimization. NLP-powered sentiment analysis played a crucial role in refining revenue strategies by analyzing customer feedback and online reviews, resulting in a 14% increase in occupancy rates, as businesses adjusted pricing and promotional efforts based on guest sentiment. The adoption of cloud computing and edge analytics significantly enhanced real-time decision-making, allowing hotels to integrate data from multiple sources, process large volumes of information efficiently, and implement dynamic pricing strategies based on live market trends, leading to a 28% increase in direct bookings. These findings align with and extend existing research by demonstrating that AI, big data, and real-time analytics provide a measurable competitive advantage in modern hospitality revenue management. The study concludes that hospitality businesses that transition from traditional, static revenue management models to AI-powered, data-driven frameworks achieve greater financial sustainability, improved operational efficiency, enhanced guest experiences, and increased profitability, reinforcing the necessity for continuous investment in AI and big data analytics for long-term revenue growth and market competitiveness.