Revenue Management and Dynamic Pricing in the Tourism Industry. Alpitour SpA Traineeship Experience.

Almost everyone has experienced the inconvenience of purchasing an airline ticket at a price disproportionately different to the one monitored short time before. We often wondered what led to incurring this inconvenience. The explanation for that is what this thesis work aims to offer. Revenue management (RM) and dynamic pricing are the terms to use to identify the practices that makes it all happen. RM is evolving and spreading more rapidly than ever before, driven as an important factor of the daily operation to keep prices competitive and to create real-time optimal pricing. The economic impact of RM is significant, with increases in revenue of 5% or more reported in several industry applications of RM systems. This research provides a literature-based study of revenue management and pricing with an emphasis given to the tourism industry. The aim of this thesis project is to highlight the crucial role that RM covers inside the tourism sector and how the implementation of dynamic pricing could impact both on the company revenues and on the customer perception. The viability of the RM techniques is evaluated taking into account the traineeship experience of the author inside the revenue management area of the Italian leader of tour operating: Alpitour World. Obstacles to the successful implementation are identified and potential solutions are named. Giving an accurate description of the segmentation of the tourism sector, the work elaborates on the historically grown research field of RM and puts emphasis on the discipline of revenue maximization. The RM literature landscape is analysed to point out the main topics around which the scientific literature has grown in the past years. This work covers developments in forecasting, demand management, capacity analysis, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. To implement the abovementioned practices, methodologies based on Machine Learning, Artificial Intelligence and Big Data are presented. The technique of dynamic pricing occupies a central role inside the dissertation, being presented as one of the most promising revenue maximization techniques to use in the tourism supply chain. In order to achieve an efficient implementation of RM and dynamic pricing the constant consideration of customers results essential. For this purpose, an in-depth argumentation on the proper management of customer relationships is developed.