Test

Platform for Price Management Based on Machine Learning

Arteco - Information Technologies
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foto Ramón Arnau

Ramón Arnau

Gerente de Arteco Consulting SL

Advanced Platform for Dynamic Hotel Price Adjustment. Utilizes machine learning and statistical analysis to optimize revenue and occupancy

Project Information

Project Name: Advanced Hotel Demand Forecasting and Price Adjustment Platform

Industry/Sector: Hospitality / Predictive Analytics

Challenge: In a highly competitive sector like hospitality, adjusting room prices in real-time to maximize revenue without compromising occupancy rate is crucial. Accommodation demand can fluctuate significantly due to numerous external factors such as holidays, music, and sports events. There was an urgent need to develop a platform that could analyze and forecast accommodation demand, dynamically and strategically adjusting prices using statistical analysis and machine learning.

Implemented Solution

Overview: Arteco Consulting SL designed and executed a comprehensive technological solution using Spring Boot and React, with a dual database incorporating PostgreSQL for structured data management and MongoDB for efficient handling of unstructured data. The platform integrates advanced machine learning models and statistical analysis to forecast hotel demand, considering significant external variables. It utilizes Apache Spark for data processing in distributed clusters, allowing for quick and efficient calculations for recommending optimized price ranges.

Technologies Used: The solution architecture is based on Spring Boot for the backend, ensuring a solid and scalable foundation. React is employed for frontend development, creating intuitive and responsive user interfaces. The combination of PostgreSQL and MongoDB offers robust and flexible data management. Apache Spark is used for analyzing large volumes of data, facilitating fast processing and analysis through distributed computing.

Key Features: The platform stands out for its ability to integrate and analyze multiple data sources, including holidays and events, to accurately forecast demand. The applied machine learning models enable the generation of dynamic pricing recommendations, balancing revenue maximization with market competitiveness. The use of data flows and Apache Spark for data analysis ensures that the system can process and analyze large volumes of information quickly, adapting to real-time changes in market conditions.

Results: The implementation of this platform has transformed hotel pricing strategy, allowing them to dynamically and strategically adjust their rates in response to demand forecasting. This has resulted in significant optimization of occupancy rates and revenue, ensuring that prices are competitive and attractive to consumers while maximizing benefits for hotels.

Conclusions

This project illustrates Arteco Consulting SL's ability to combine advanced technologies and complex data analysis in solutions that drive strategic decision-making in the hotel industry. The demand forecasting and price adjustment platform demonstrates Arteco's commitment to innovation and continuous improvement, providing hotels with powerful tools to navigate a highly competitive market and maximize profitability.

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