AI-driven Dynamic Pricing during Major Events: A quantitative study on how the hotel industry can maximize revenue during major events with AI-driven dynamic pricing strategies

Sammanfattning

Major events such as Taylor Swift's sold-out concerts provide powerful illustrations of how spikes in demand create significant revenue opportunities for the hotel industry. However, traditional Revenue Management Systems (RMS) often fall short in maximizing this potential due to their reliance on historical data and internal booking patterns, lacking proactive integration of real-time external event information. This study examines how AI-driven dynamic pricing strategies can optimize hotel revenue during major events, addressing a notable research gap at the intersection of artificial intelligence, dynamic pricing, and event-driven demand forecasting. Through a quantitative analysis of booking and pricing data from a Swedish hotel chain and the design of controlled AI prompts with and without event awareness, the research highlights the critical limitations of current AI systems and proposes the integration of external data sources to improve responsiveness. Results demonstrate that real-time awareness of major events significantly enhances pricing strategies, with AI-informed models capturing higher revenues by adjusting prices proactively. The findings advocate for the development of integrated event data, and suggest managerial implications for leveraging AI technologies more strategically within volatile, uncertain, complex, and ambiguous (VUCA) environments. This thesis contributes both academically and practically to the evolving field of Revenue Management (RM) by showcasing how AI can reshape pricing strategies to better align with dynamic market realities.