Disney has made its reputation for building its theme parks into “the happiest place on Earth.” Throughout the last six decades, park-goers have enjoyed the advances that Disney has incorporated such as the animatronic robots in Tomorrowland and the motion-simulated Star Tours. In turn, Disney has continued to invest in the customer experience.
With enormous volumes of visitors every year (Disney parks globally had ~150 million visitors in 2017)1, Disney faces a serious challenge in managing extensive back-end and customer-facing operations. At Walt Disney World (WDW) alone, this entails managing hotel accommodations, food service, and theme attractions for over 20 million annual visitors.1 Machine learning can help Disney simultaneously enhance the existing consumer experience, improve internal operations, and push the envelope on product development at a large scale. By integrating this technology, Disney has sought to make its parks among the most efficient places on the Earth.
Disney’s use of machine learning
In recent years, Disney rolled out the MagicBands wristbands and My Disney Experience platform to streamline the theme park experience. My Disney Experience helps WDW visitors plan itineraries, restaurant reservations and express passes. Visitors can then load their pertinent data (e.g. room access, ride passes and payment methods) onto the MagicBand, a sleek Disney-themed wristband. This integrated wearable not only simplifies things for consumers, but also gives Disney managers a massive trove of data on visitor behavior. The distributed network of sensors at entrances, food stands, and rides gives Disney an opportunity to use machine learning to drive efficiency and improve guest experience in its parks.
On the front end, Disney uses machine learning to help customers develop itineraries that minimize the time visitors are waiting in line.2 Complex algorithms also help predict key customer events like when and where a visiting family will sit for their dinner reservation. WDW control centers can then prompt kitchen staff to get a head start on the meals the family pre-ordered earlier in the day.2 By reducing idle time, machine learning helps to increase visitor satisfaction and to maximize the time visitors are free to make purchases around the parks.
On the back end, Disney uses advanced algorithms to better plan resource allocations. With continuous updates, park operators can better allocate the 80,000 employees across 240,000 shifts each week.3 Additionally, machine learning helps Disney actively predict and manage demands for costumes and laundry needed to make a magical experience for its guests.4 Lastly, Disney uses dynamic pricing models – a staple in other industries such as airlines – to match demand with park capacity throughout the year.5 Disney uses machine learning to drive operational improvements while delighting customers and maximizing revenue.
Disney also invests in machine learning with longer-term improvement in mind. For example, Disney recently filed a patent for a shoe recognition tool to track traffic patterns in the parks to finer resolution. This should improve the ability down the road to understand and manage visitor flow through the parks.6 Executives have also revealed investments in artificial intelligence to create robots that move around and interact with guests in the parks,7 driving visitor engagement.
Beyond the parks themselves, Disney’s massive investment in its own streaming platform Disney+ opens a new, massive frontier of consumer data that should reap major long-term benefits independently and with the parks. Development of facial recognition software will also help content creators better understand how viewers engage with film.8 Disney can use machine learning to capitalize on investments like these to provide meaningful insight into how to best connect with fans over the long term.
Disney is setting itself up well for the future with its advanced analytics, and it still has opportunity to get more out of machine learning. Disney should offer customers cross-portfolio targeting between content and experiences. For example, if a family loves Star Wars on Disney+, Disney could provide catered packages to its new Star Wars-themed additions to Disneyland and WDW.
In the parks themselves, Disney can seek further engagement with feedback centers at attractions that are integrated with the MagicBands. This could provide data to support real-time and future park management.
Finally, Disney should make sure to keep focus of what it does best: connecting to fans. Disney’s long history of creativity, whether from Imagineers or filmmakers, has distinguished it as a cultural force for the last century. Machine learning can help Disney understand what works and where opportunities lie, but it will not replace the leaders and creators that have made Disney what it is today.
As Disney continues innovating to develop its offerings, two questions highlight the opportunities it faces:
- How can Disney best use data sourced from the parks to inform content creation decisions across film and TV?
- What limits exist to using findings from machine learning between platforms (e.g. parks, film, TV)?
 John Robinett et al., Theme Index Museum Index 2017 (2018), Themed Entertainment Associates.
 Cliff Kuang. “Disney’s $1 Billion Bet on a Magical Wristband,” Wired, March 10, 2015, https://www.wired.com/2015/03/disney-magicband/, accessed November 2018
 The Leadership Network, “How Disney Creates Digital Magic with Big Data.” LeadershipNetwork.com, October 5, 2016. https://theleadershipnetwork.com/article/disney-digital-magic-big-data, accessed November 2018
 Pete Buczkowski and Hai Chu, “Corporate Profile: How analytics enhance the guest experience at Walt Disney World.” Analytics-magazine.org, October 2012. http://analytics-magazine.org/corporate-profile-how-analytics-enhance-the-guest-experience-at-walt-disney-world/, accessed November 2018
 Erich Shwartzel. “Disney Tests Pricing Power at Theme Parks,” Wall Street Journal, June 18, 2018, https://www.wsj.com/articles/disney-tests-pricing-power-at-theme-parks-1529331115, accessed November 2018
 Bernard Marr. “Disney Uses Big Data, IoT and Machine Learning to Boost Customer Experience,” Forbes, August 24, 2017, https://www.forbes.com/sites/bernardmarr/2017/08/24/disney-uses-big-data-iot-and-machine-learning-to-boost-customer-experience/#289bf6303387, accessed November 2018
 Dave Lee. “SXSW 2017: Disney ‘not in the business of scaring kids!” BBC, March 12, 2017 https://www.bbc.com/news/technology-39245946, accessed November 2018
 Zhiwei Dang et al., “Factorized Variational Autoencoders for Modeling Audience Reactions to Movies,” July 21, 2017, IEEE Conference on Computer Vision and Patter Recognition, “https://www.disneyresearch.com/publication/factorized-variational-autoencoder/