The different holiday periods, Easter, summer, Christmas… are the star moments of the year for the tourism sector. Knowing the weather in advance in order to predict the level of occupation is one of the main concerns of tourism companies, especially at times of the year when the weather is variable. Putting advanced analytics and Big Data techniques into practice is allowing industry leaders to get ahead of the weather and predict their occupancy level with a success rate that can vary between 70 and 90%.

Advanced analytics technologies and Big Data make it possible to predict what the level of occupation will be through the historical analysis of data, such as temperatures, rainfall and hours of sunshine that were experienced on the same dates in previous years; information that must be crossed with the occupancy rates obtained in previous seasons.

In this way, if the meteorological indicators say that the temperature during this period is going to be below the average and that the precipitations are also going to be below the average with respect to previous years, it is possible to infer that the occupation will be of a similar percentage to other years in which the same conditions occurred. It will only be necessary to take into account some extra correction factor, such as the economic situation.

The prediction, therefore, is in line with reality and will allow companies to put in place the necessary mechanisms to optimize revenue. In this way, the response offered by the new occupational prediction techniques based on Big Data, when forecasting the occupation at Easter, increases the annual results through the reduction of costs and the adjustments of the market prices in a certain moment, which can fluctuate up to 300%. According to Cognodata, the percentage of cost reduction, for its part, is estimated between 5 and 20%, depending on the company’s billing and intermediaries.

Other specific aspects that tourism companies are considering to predict what will happen on these dates are the origin of the clients and the average purchasing power, the shows and events in the environment, the occupational information of the competition or the searches for accommodation over the Internet.