“Any sufficiently advanced technology is indistinguishable from magic”
– Arthur C. Clarke
Walt Disney was an innovator whose passion was creating magic – through his movies, his characters, and most of all through his immersive theme parks. Today, Disney World employs incredibly innovative data collection and machine learning techniques to continue to give its customers the magical experiences that Walt Disney envisioned.
The output of any machine learning algorithm is only as good as the data it utilizes, so Disney World spent $1 billion to roll out the battery powered, RFID-enabled MagicBands to its visitors beginning in 2013 and start accumulating its massive set of customer data (see Figure 1) . This wristband acts as a hotel key, allows guests to make food and memorabilia purchases within the parks, and interfaces with their Disney profile to manage restaurant bookings, park tickets, and FastPass reservations (a system that allows guests to book a spot in the “fast lane” for certain rides, bypassing longer lines and cutting down wait times). What results is an incredible wealth of data that records not only customer transactions, but also crowd patterns.
Disney World uses machine learning on this data to solve two main issues. First, it is important for them to ensure that their “product” – a visit to Disney World – continues to feel like a personal experience for each guest, tailored to each individual’s interests. Second, with crowd size continuing to grow each year, parks are getting more crowded. The increasing number of visitors not only tarnishes a guest’s experience (no one likes waiting in line for Space Mountain for two hours), but it also cuts into Disney’s profits. Larger crowds are harder for the Disney staff to control, and guests waiting in lines for rides are unable to spend money on food, refreshments, and souvenirs.
By being able to track guests’ locations around its parks, Disney not only analyzes but predicts the movement of crowds in order to best utilize its workers and maximize the customer experiences. It leverages patterns in the data to plan where to put its characters and food carts, and it aids in the scheduling of the 240,000 shifts for 80,000 different employees each week . During the 2013 Christmas season, armed with machine learning and the new data from MagicBands, they leveraged these operational efficiencies to accommodate 3,000 additional guests per day at their busiest park, the Magic Kingdom . In the coming years, Disney has plans to continue innovating both on what data is collected and how it is analyzed to change the customer experience. Their research team in Pittsburgh is working on developing methods for guests to interact with either robotic or virtual characters in the park. The goal is for these characters, using data from sensors, to learn to naturally respond to guests’ behavior . Additionally, Disney is working on an augmented-reality service agent named Tink – named after Peter Pan’s fairy friend – that can navigate you efficiently around the park and make suggestions based on your interests .
I think that Disney has just scratched the surface in terms of what patterns they will be able to find in our data and how they can use it to transform and individualize Disney experiences. In the short term, I think that they should use this data to develop personalized itineraries for their customers. One of the most stressful things about a Disney World experience is trying to plan your day at one of the parks to ensure that you’ll be able to go on all the rides you desire. Using the data they already collect on wait times and walking times, Disney should develop a program that proposes an optimized daily itinerary its guessed based on their ride preferences. Looking longer term, I think they can use various sensors to objectively track guest satisfaction on each ride (tracked by facial expressions or other biometric data). This will not only help track which rides are truly generating the most happiness, but it will allow the Imagineers to analyze each subcomponent of a ride and, using this data from existing attractions, create new rides that are perfectly engineered for rider enjoyment.
Wearables that collect our data are certainly on the rise, and Disney seems to be on the leading edge by using technology often indistinguishable from magic. In any situation involving leveraging customer data to drive revenues, the question of ethics is an important one. At what point does this become less about creating an experience for each visitor and more about simply maximizing profit? And, if the end goal is just to drive profits, is this necessarily a bad thing? Or do park guests still get a share of the value that Disney is creating with this technology?
 Cliff Kuang, “DISNEY’S $1 BILLION BET ON A MAGICAL WRISTBAND”. Wired.com, March 10, 2015. [https://www.wired.com/2015/03/disney-magicband/], accessed November 2018.
 Walt Disney Company, “Disney Parks MagicBand”, [https://www.wired.com/2015/03/disney-magicband/], accessed November 2018.
 Bernard Marr, “Disney Uses Big Data, IoT And Machine Learning To Boost Customer Experience”. Forbes, August 27, 2017. [https://www.forbes.com/sites/bernardmarr/2017/08/24/disney-uses-big-data-iot-and-machine-learning-to-boost-customer-experience/#297d1cbd3387], accessed November 2018.
 Alexia S. Quadrani, Julia Yue, David Karnovsky, James Kopelman. “Disney Parks & Resorts: It’s No Longer A Small World.” J.P. Morgan, North American Equity Research, April 29, 2016. Via ProQuest, accessed November 2018.
 ETH Zurich, “Disney Launches Global Research &Development Labs with Carnegie Mellon University in Pittsburgh and Swiss Federal Institute of Technology Zurich”. Business Wire, August 11, 2008. Via ProQuest, accessed November 2018.
 Kate Everson, “Learning Is All in the Wrist”. Chief Learning Officer, Special Technology Report, April 2015. [http://www.cedma-europe.org/newsletter%20articles/Clomedia/Learning%20Is%20All%20in%20the%20Wrist%20(Apr%2015).pdf], accessed November 2018.