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Hotel Gran Bilbao Forecasts Occupancy Rates Using AI

Delivering unique, highly personalized guest experiences drives Hotel Gran Bilbao (HGB) in Spain.  They know that no two guests are the same and therefore no two rooms are exactly alike. Artwork is paired bespoke to each room as an inspiration to guests. With such an acute focus for guest experience, HGB saw an opportunity to become even more proactive with preditions and services. 

The Challenge

HGB partnered with Avvale to assist with determining Revenue Per Available Room (REVPAR). This is the key metric that measures revenue per available room that is based on several factors including set time horizon, whether weekly, monthly, yearly, etc. To calculate this KPI it is necessary to have these two variables, percentage of occupancy on the total available rooms and the average daily rate (ADR).
 
To begin this project, Avvale interviewed Hotel Directors, Revenue Directors, Partners, and potential clients. It was concluded that there was no solution in the market that evaluated both external and internal indicators when predicting demand and hotel occupancy. It was clear a tailor-made solution was needed. Not only that, but a solution that could be applied to other smaller and larger businesses.  
 

After analyzing the independent business/hotel network in Spain, it was determined that the solution can replicate certain aspects of their business models. In the case of HGB, it was essential to strengthen direct sales to improve its profitability. A necessity of this tool is to allow for a more effective and profitable management of the channel strategy to increase profitability. 

 

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The Approach

Avvale co-developed a new solution with the Hotel Gran Bilbao and successfully implemented it with the intention of improving the revenue management strategy. 

To create stronger predictions, various algorithms and predictive models based on Deep Learning (neural networks) were combined while factoring variables such as past occupancy, average booking price, channel, segment, room types, or other data external to the hotel. All this connected to the data of the Property Management System (PMS) of the hotel to create a full view of current and future occupancy.
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The Impact

Leveraging real-time insights through data management, Hotel Gran Bilbao can predict demand and hotel occupancy with 90% accuracy over a one-month time horizon and almost 89% accuracy over a ninety-day time horizon. Because of this, the hotel has experienced an increase in REVPAR. Predictive scenarios manage potential challenges and hotel service consumption for accurate service and scheduling, further driving down operations costs. Most importantly, Hotel Gran Bilbao can focus on what they do best-- delivering exceptional service and hospitality. 

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