Challenge: In the hotel industry, maximize occupancy rates, reduce risks of overbooking, and dynamically adjust room pricing

Solutions: Advanced predictive analytics of cancellations and occupancy. Data-driven decision making using historical data and predictive models

Benefits:

• High accuracy in predicting cancellations
• Real-time analysis of booking behaviors
• Improved occupancy rates
• Revenue maximization through effective cancellations management
• Optimized pricing strategies
• Targeted marketing and pricing strategies
• Improved customer experience

Key performance indicators

• Decision-making lead time reduced by approximately 20-30% thanks to real-time analytics
• Operational cost savings of roughly 10-15% thanks to minimization of cancellation ratio
• Approximately 10,000 lines of code developed
• Estimated revenue uplift of 12-15% during peak seasons with optimized pricing strategy

For a large hotel chain with more than 15 establishments in Spain’s most popular beach locations, the objective was to continue to provide clients with an excellent quality-price ratio to ensure that guests had the best experience possible during their stays. To achieve this, they needed to be able to proactively manage their bookings (e.g., by relisting high-probability cancellations, or offering them to other potential guests) while avoiding the risk of overbooking.

Another ALTEN client, specialized in technology solutions for the hotel industry, sought to help their clients optimize decision making and improve profits while ensuring the best possible experience for hotel guests.

ALTEN developed a dynamic pricing algorithm that analyzes future demand and determines the optimal room price based on historical occupancy data, pricing trends, and contextual factors (e.g., seasonality, holidays), maximizing profitability by suggesting the best price for optimal occupancy on any given day. The system also processes historical data (e.g., booking lead times, occupancy rates, room type, and customer profiles) to calculate the probability of cancellation for each reservation.

The technologies used to achieve these results included Python and BigML, for machine learning algorithms to process real-time booking data and predict future behaviors, deploying the models at scale. A MySQL database was implemented to store historical data and feed it into predictive models. Azure Web Services was chosen to host the web applications and APIs that power the predictive analytics solutions. Power BI enables data visualizations as well as the creation of interactive dashboards for decision making based on predictive analytics. TDSP, Agile and Scrum methodologies ensure efficient project management and development.

By combining advanced predictive analytics, real-time data processing, and interactive visualization tools like Power BI, the solution leverages machine learning models trained on historical data to make accurate predictions about cancellations and booking behaviors. In addition, Agile and Scrum project management methodologies contributed to a faster deployment cycle, which was a key technical advantage in this project.

This data-driven decision making allows hotels to make informed decisions in real-time, for improved profitability and resource utilization.

Looking ahead, the client is interested in expanding the use of AI to include reservation forecasting and the optimization of promotional campaigns for customers.