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Development of Scalable Machine Learning Infrastructure for Dynamic Pricing in Tourism

stxnext.com

eCommerce

Hospitality & leisure

Logistics

Challenges in Dynamic Pricing for Tourism

Need to optimize dynamic pricing for ski passes in unpredictable environments through accurate demand forecasting, real-time price adjustments based on demand, weather, and external factors, and simplified onboarding for new ski resorts.

About the Client

Company specializing in decision-making tools and software solutions for tourism, logistics, and leisure sectors

Objectives for Dynamic Pricing System

Develop a scalable Machine Learning infrastructure for demand forecasting
Improve accuracy of demand prediction models using diverse data sources
Enable dynamic pricing adjustments based on real-time demand and external factors
Simplify client onboarding for new ski resorts
Ensure cloud-based scalability and operational efficiency

Core System Functionalities

Optimized forecasting module with multi-source data analysis
Real-time pricing adjustments using prebooking data and weather APIs
Automated client onboarding workflow for ski resorts
Cloud-native architecture with Kubernetes and Docker
Performance metrics dashboard for revenue yield tracking

Technology Stack

Machine Learning

Kubernetes

Docker

Cloud infrastructure

External System Integrations

Weather data APIs
Existing booking systems
Resort management databases

System Requirements

Scalability for seasonal demand spikes
High availability during peak periods
Data security for customer information
Low-latency real-time pricing updates

Expected Business Impact

Anticipated outcomes include increased revenue per ski pass through data-driven pricing, optimized occupancy rates via demand forecasting, stabilized cash flow during low-demand periods, and measurable improvements in key performance metrics like revenue yield and average pass price.