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Demand Forecast for Hotels

Forecast Accuracy converted into increased Business Margins for Hotels.
Make decisions based on when you will have rooms available for customers and at what price, with the constraint of fixed room capacity.
The purpose is to improve Demand Prediction based on the available internal/external data.

Case Study - Hotel chain
50 hotels in Europe. 6,000 rooms. $150 annual revenue. Avg price per room: $75

Forecast error

+15% better

2018: 35%

2019: 20%

Revenue attributed to Demand Forecast

+$7m. increment

2018: $145

2019: $152 

Profit attributed to Demand Forecast

+$2m more

2018: $13m

2019: $15m


Tailored predictive algorithms

We blended human predictions with machine learning to achieve the best possible results. Beforecaster's algorithms are created under the principle that forecasting accuracy is a means to a business end.

Tapping into the right sources

Exploits data from a variety of sources that included historical demand, weather, events, epidemics, or transportation prices.

Avoiding Forecasting Waste

We never take for granted that all forecasters add value to a prediction. We eliminate any prediction elements that return poor results.

Permanent updating

Every time new data arrives in the system, Predictions are updated. It warns users whenever it detects changes worth communicating.

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