ARTIFICIAL INTELLIGENCE
Data-driven improvements in hotel management
In Spain, ALTEN partnered with two leading operators in the hotel industry to maximize occupancy, enhance revenue management, and improve operational efficiency. They came up with data-driven solutions – using AI and machine learning – to enhance the ability to predict reservation cancellations, on the one hand, and to adjust room pricing on the other.
The hospitality sector depends on the ability to optimize critical decision-making. Two leaders in the Spanish hotel industry approached ALTEN in Spain to help them with key issues. The first sought to maximize occupancy rates and minimize risks of overbooking by improving their ability to predict cancellations of reservations. The other company wanted to create a system that would allow them to dynamically adjust room pricing based on occupancy for balanced profitability. ALTEN leveraged advanced machine learning models to analyze historical and contextual data, such as booking patterns and seasonal factors, for accurate predictions of occupancy and actionable insights into improve operational efficiency.
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
Accurate predictions
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 tools
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.
