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Leveraging Microservices Architecture for Dynamic Pricing in the Travel Industry: Algorithms, Scalability, and Impact on Revenue and Customer Satisfaction

2024/11/03 by Biman Barua, M. Shamim Kaiser, Barua, Biman +1 · 1 citation
Business, Management and Accounting · Engineering · Social Sciences · #Computation and Language (cs.CL) #Computational Engineering #Computer Science and Game Theory (cs.GT) #Digital Platforms and Economics #Distributed #FOS: Computer and information sciences #Finance #Parallel #Transportation Planning and Optimization #Transportation and Mobility Innovations #and Cluster Computing (cs.DC) #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2411.01636

openalex publication_date 2024/11/03 · openalex created_date 2024/11/14 · openalex updated_date 2026/07/28

Abstract

This research investigates the implementation of a real-time, microservices-oriented dynamic pricing system for the travel sector. The system is designed to address factors such as demand, competitor pricing, and other external circumstances in real-time. Both controlled simulation and real-life application showed a respectable gain of 22% in revenue generation and a 17% improvement in pricing response time which concern the issues of scaling and flexibility of classical pricing mechanisms. Demand forecasting, competitor pricing strategies, and event-based pricing were implemented as separate microservices to enhance their scalability and reduce resource consumption by 30% during peak loads. Customers were also more content as depicted by a 15% increase in satisfaction score post-implementation given the appreciation of more appropriate pricing. This research enhances the existing literature with practical illustrations of the possible application of microservices technology in developing dynamic pricing solutions in a complex and data-driven context. There exist however areas for improvement for instance inter-service latency and the need for extensive real-time data pipelines. The present research goes on to suggest combining these with direct data capture from customer behavior at the same time as machine learning capacity developments in pricing algorithms to assist in more accurate real time pricing. It is determined that the use of microservices is a reasonable and efficient model for dynamic pricing, allowing the tourism sector to employ evidence-based and customer centric pricing techniques, which ensures that their profits are not jeopardized because of the need for customers.

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