“What really gets us excited about machine learning’s ability, is to deepen the connection between consumers and our stories through personalization, optimization, and enhanced experience…Instead of using time-consuming, error-prone methods, we’re leveraging machine learning to analyze data, provide real-time recognition and experiences that increase engagement and satisfaction.”
– Michael White, Senior VP of Technology at Disney Consumer Products and Interactive Media
For over 95 years, Disney has differentiated itself in the entertainment industry through unique customer experiences and delightful content. Its marquee brands and world-renowned theme parks generated over $55 billion in revenue last year, representing a steady 4.5% CAGR over the last decade. But as competition for customer eyes and wallets has intensified, Disney has recognized the need to innovate to stay competitive – and they are leaning on machine learning to serve those customer needs.
History of Machine Learning at Disney
Disney approaches technological development using both empirical and practical methods. Since 2008, the Company has supported its own in-house research lab (Disney Research) that develops scientific and technological innovations to drive value across the company’s portfolio. The initiative is a collaboration between Disney and the Swiss Federal Institute of Technology Zurich and publishes on a number of computational fields including machine learning.
One popular example of the implementation of Disney’s machine learning capabilities is the development of MagicBand – a park-wide pass that tracks customers’ movements, analyzes purchasing habits, and reports the real-time data to Disney. The purpose of this innovation was to streamline the customer experience by helping Disney ‘cast members’ anticipate customer behavior and make quick decisions around adding staff or incentivizing guests to head to another ride or attraction. This investment has been highly accretive for Disney: efficiency and capacity of the parks has improved, operating margins have increased by over 450 bps, and traffic trends continue to be strong.
Machine Learning: Reinforcing Disney’s Competitive Advantages
Going forward, Disney will face two key competitive problems: The first is content creation: the proliferation and saturation of content means that Disney must become experts at predicting and adapting to consumer tastes and preferences. The second is content delivery: adoption of new technologies is shifting patterns of content consumption, so Disney must ensure they are delivering content through the most relevant channels and delighting users with every interaction.
Disney is working to extend its technological capabilities further by applying its expertise to content generation. In 2017 Disney Research published a study exploring computational models for narrative building. Using the ‘Q&A’ social media site Quora, Disney collected over 50,000 answers and used machine learning to build models for ‘good’ answers. Using good answers as a proxy for good narratives, Disney has effectively created a program that recognizes patterns of good stories and predicts what will resonate with consumers. The Company is also developing a variety of machine learning-enabled content projects: research on dynamic storytelling between robots and children, programs that can analyze viewer facial expressions to predict engagement with content, and deep learning speech animation processes that can be integrated into existing production pipelines.
Disney is also striving to optimize content delivery using machine learning. Over the next few years, Disney will launch two direct-to-consumer streaming services (multi-sports and Disney-branded film/TV content) with machine learning-driven recommendation engines to thoughtfully push content to consumers. Additionally, the Company invests in smaller projects to enhance and learn from customer interactions, including chatbots for characters such as Miss Piggy, Judy Hopps of Zootopia, and Guardians of the Galaxy.
In addition to the projects Disney has already undertaken, there are several competencies that Disney should build using their machine learning capabilities. One is a command of demographic segmenting and positioning – understanding how to best develop content that serves a spectrum of consumers. Alternatively, given the new focus on digital content Disney should develop innovative testing procedures for their DTC platform to make sure they are consistently producing and pushing the best products.
Looking to the Future
It is imperative that Disney innovate to stay ahead of their competition. However, the Company’s shifting competitive strategy may inhibit their technological progress. Disney recently received approval to consummate the $71 billion acquisition of Twenty First Century Fox. This merger may prove to be a distraction, as it will dramatically increase Disney’s international footprint, expand the content and distribution for its DTC offerings, and increase its ownership stake in Hulu. Furthermore, the Company is undergoing a significant reorganization to prepare for the integration and strategic realignment.
These developments raise several key questions: How should the company allocate its time and resources between the Fox integration and R&D? And if there are dollars available for R&D, which areas of technological exploration and development are most relevant to Disney today? (780 words)
 Magento. (2018). What can Disney teach us about Artificial Intelligence? | Magento. [online] Available at: https://magento.com/blog/events/what-can-disney-teach-us-about-artificial-intelligence [Accessed 8 Nov. 2018].
 The Walt Disney Company. (2017). Form 10-K 2017. Retrieved from SEC EDGAR website http://www.sec.gov/edgar.shtml.
 Disney Research. (2018). Research Areas. [online] Available at: https://www.disneyresearch.com/.
 The Walt Disney Company. (2018). Disney’s Q2 FY15 Earnings Results Webcast – The Walt Disney Company. [online] Available at: https://www.thewaltdisneycompany.com/disneys-q2-fy15-earnings-results-webcast/.
 Wang, T; Chen, P.; Li, B. (2017). Predicting the Quality of Short Narratives from Social Media. working paper, University of Massachusetts Boston, https://s3-us-west-1.amazonaws.com/disneyresearch/wp-content/uploads/20170816094024/Predicting-the-Quality-of-Short-Narratives-from-Social-Media-Paper.pdf.
 Disney Research. (2018). Machine Learning Research Areas. [online] Available at: https://www.disneyresearch.com/.
 Woodie, A. (2018). How Disney Built a Pipeline for Streaming Analytics. [online] Datanami. Available at: https://www.datanami.com/2018/05/14/how-disney-built-a-pipeline-for-streaming-analytics/.
 Rayo, E. (2018). AI at Disney, Viacom, and Other Entertainment Giants. [online] TechEmergence. Available at: https://www.techemergence.com/ai-at-disney-viacom-and-other-entertainment-giants/.