The core goals for a successful Disney vacation are to avoid crowds, save money, and maximize enjoyment by using a data-driven approach.
Manually planning a trip is nearly impossible due to the vast number of variables like park capacity, attendance, ticket costs, and attraction popularity.
Effective predictions rely on diverse data sources including school calendars, economic trends, scraped wait times, and user-submitted feedback.
A crowd calendar provides a high-level view, but the core predictions are granular wait time curves for every attraction on five-minute increments.
A live demo shows how to use a web tool to generate an optimized touring plan that minimizes waiting by sequencing attractions intelligently.
A static printed plan is fragile, so a mobile app is used to re-optimize the itinerary throughout the day using real-time wait time data.
By analyzing time saved versus cost, you can determine the actual value of upcharges like Genie+ and Individual Lightning Lane for different parks and crowd levels.
Scatter plots comparing user satisfaction ratings against average cost help identify the best value hotels and restaurants, avoiding expensive disappointments.
The presentation concludes by exploring future possibilities, such as a recommendation engine that learns user preferences in real-time to suggest the next attraction.
43:11Thomas Reiter
57:46Adrian Schmitt
58:21Julian Joseph
45:58Andreas Christian
22:41Christoph Fassbach & Daniel Rohr
37:21Mario Meir-Huber
49:45Kris Howard
22:06Lukas Kölbl
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Jobs that call for the skills explored in this talk.
August-Wilhelm Scheer Institut für digitale Produkte und Prozesse gGmbH
