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

Biman Barua, M. Shamim Kaiser|arXiv (Cornell University)|Nov 3, 2024

Transportation and Mobility Innovations被引用 4

一句话总结

本文提出了一种基于微服务的动态定价系统，用于旅游行业，将实时需求预测、竞争对手定价和基于事件的触发机制作为模块化服务进行整合。该系统实现了22%的收入增长和17%更快的定价响应速度，客户满意度提高15%，证明其在可扩展性和响应能力方面优于单体系统。

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.

研究动机与目标

• 设计并实现一种基于微服务架构的可扩展、实时动态定价系统，用于旅游行业。
• 解决传统定价系统存在的可扩展性差和对市场变化响应缓慢等局限性。
• 评估微服务架构在实际旅游定价场景中对收入生成、定价速度和客户满意度的影响。
• 识别在实时微服务部署中，如服务间延迟和数据管道负载等性能瓶颈。

提出的方法

• 将动态定价流程分解为独立的微服务：需求预测、竞争对手定价监控和基于事件的定价。
• 实现实时数据摄入管道，将市场状况、预订数据和外部事件的实时数据输入到每个微服务中。
• 利用事件驱动的微服务间通信机制，确保低延迟协调和快速响应的定价更新。
• 在每个微服务中应用机器学习模型，基于实时输入动态预测需求并调整价格。
• 设计系统以支持水平扩展，使各服务在高峰负载期间可独立扩展。
• 集成客户行为数据采集机制，以随时间推移提升定价的准确性和个性化水平。

实验结果

研究问题

• RQ1 微服务架构在多大程度上提升了旅游行业中动态定价的可扩展性和响应能力？
• RQ2 与单体系统相比，模块化微服务设计对收入生成和定价响应时间有何影响？
• RQ3 实时需求预测、竞争对手定价和基于事件的触发机制在多大程度上提升了定价准确性和客户满意度？
• RQ4 在实时微服务部署中，关键性能挑战（如服务间延迟和数据管道开销）是什么？

主要发现

• 与基线定价模型相比，系统实现了22%的收入增长。
• 定价响应时间提升了17%，表明对市场变化的适应速度更快。
• 在高峰负载期间，由于微服务的高效独立扩展，资源消耗降低了30%。
• 系统实施后，客户满意度评分提升了15%，反映出定价与客户感知价值更加契合。
• 服务间延迟和实时数据管道的复杂性被识别为关键的技术挑战。
• 客户行为数据与持续演进的机器学习模型的整合，被证明可提升定价的准确性和响应能力。