Case: de Efteling
Developing a recommender app for De Efteling
A EngD trainee from JADS helped de Efteling, an amusement park in The Netherlands, develop a recommender app that was made available for the public in October 2020. Thanks to this app, Efteling will be able to better distribute its visitor flows throughout the park. Based on the stated preferences of the guest, the service gives advice about which attraction can best be visited at a specific time. This allows visitors to the theme parks to get more out of their day. The new app will eventually be integrated into the existing Efteling app. Efteling is the first theme park worldwide to use this functionality.
Using machine learning for better visitor distribution
Tests have shown that the system behind the service knows with 96% certainty what the waiting times will be for the rest of the day. By predicting waiting times and predicting catering transactions, the system also knows how busy it will be on the various squares in the park. In addition, the system teaches itself what different types of companies would like to visit. The combination of all these factors ensures that De Pennenveer can provide personal advice and ensures that Efteling visitors are better distributed throughout the park.
Quote from de Efteling
Quote from de Efteling
“Our EngD trainee excelled in initiative, self-reliance and deep data knowledge; and he surprised us in terms of creativity in solving data issues.”
– De Efteling
About the EngD program
The Engineering Doctorate (EngD; formerly PDEng) program Data Science is a two-year post-master’s program. It qualifies students with an MSc degree in mathematics, statistic and computer science to become top-level professionals. Candidates for the EngD Data Science are creative problem solvers with excellent technical skills. A major part of the EngD program Data Science is working on real-life projects with partners in the data science ecosystem. EngD trainees help industry and business with their decision making processes based on real, actionable data.