Disney’s Big Data Strategy

Prioritization Matrix for Disney wristbands

This article uses the prioritization matrix to analyze Big Data use cases for Disney wristbands.

About Disney Magicbands (wristbands)

Disney wristbands are wearables that provide a one-stop-shop integrated experience inclusive of it serving as admission card, room key, cutting line (FastPass), photo pass card, and payment method.

Optimizing existing business practices with prioritization matrix:

1. Cost reduction: Analyze smartphone wallet vs smartbands use to determine which modes are most used. Promote smartphone integrated with digital wallets to reduce band mail-in costs weeks before guest arrival.
2. Product performance: Identity which rides are most popular, which days/timings are most preferred by looking at ride transaction usage and FastPass data. Determine factors for switching rides after the plans are made.
3. Seamless customer experience: Providing an omnichannel experience across apps and wearables by integrating customer data across channels.
4. Personalized Marketing & Services: Using data on which rides customers choose, a mix of rides/repeat rides, places they pick to eat, which characters they meet, etc. could help personalize marketing and sales efforts. This data could also be used to suggest rides customers may like as top choices.
5. Optimizing operations & business continuity: Determining how many times wristband reader machines fail by looking at failed transaction logs.
6. Optimizing staffing: Analyze real-time staffing needs based on geolocations of wristband use across the property.
7. Ease of doing business/Frictionless contacts: Reduce the number of steps (thumb strokes for apps) in purchasing bands and checking status, loyalty points by studying user experience and customer reviews/feedback.
8. Security of digital assets: Analyze data policy, data breaches, DDoS and hacking attacks to strengthen security policies.

MagicBands present a couple of monetization opportunities:

1. Customization via the third party: Based on customer profiles, ride history, age, recommend design patterns to 3rd party services that produce bands. Identify logo license use like Nike, Adidas, charms, etc. based on customer preferences/interests.
2. Strategic marketing: Determine which characters are popular based on RFIDs, wi-fi data and potentially placing them near food areas, retail stores, or rides that are not as popular. Based on choices, suggest movies on DisneyPlus.
3. Product & Service recommendation: Based on POS, social media, RFID, IMDB ratings, and customer journey data recommend across theme parks, cruises, resorts, spa, hotels operated by 3rd party, vacation club, guided tour packages, movie production, music, streaming services, merchandise, etc.
4. Ancillary offerings: Leverage social media, customer profile data to identify customers visiting properties to celebrate special life events and provide ancillary offerings. Eg. opportunity for frictionless in-experience purchases like special photos taken without commitment and automatically uploaded to myDisney app, all tied with wrist band customer IDs.
5. Revenue generation: Cashless, swipe transactions have a physiological influence on increasing customer spend. Identify locations where band readers could be installed like souvenirs shops in hotels, car rides, hotel-provided taxis and shuttles, tips for a cashless experience.
6. Partnering with retailers: Presently wristbands cannot be used at AMC theatres located in amusement parks is yet another area for expansion with the use of smartphone payments on Disney wi-fi. Data on which movies customers watch could be used for optimizing in-house streaming services.
7. Spend pattern analysis: Leverage transaction data from all other retail stores at parks could be used to further analyze spending patterns and offer real-time promotions.
8. Training recommendations: Determine which customer interactions/touchpoints are most productive, engaging, and revenue-generating to use as best practices for staff training/onboarding.

Prioritization Matrix explanation

The use cases have been ranked based on business value, implementation feasibility, and relative to each other.

High value, high feasibility (Sweet spot): #1, #2, #4, #6, #7, #8, #9, #11, #13, #15 are grouped here as most desirable and feasible use cases.

• #1 falls here assuming high adoption of smartphones. Assuming 20% of the customers stay at Disney resorts and that yields significant savings on wrist bands.
• #2 falls in this category as it is fundamental to optimizing operations.
• #4 Revenue generating use case.
• #6 is a good use case to optimize staffing needs and manage a major expense category.
• #7 User experience is key in the digital area. Investing here to make it easier for customers to do business with Disney will create long-term affinity.
• #8 Data breaches can risk customer trust, so important to invest here given the huge volumes of personal customer data being gathered.
• #11 use case while a heavy lift has long-term value. Many major players are leveraging data for product/service recommendations and cross-sell, thereby creating a holistic experience under one banner/brand.
• #12 use case provides leverages known customer data to delight customers.
• #13 use case taps into predictive analytics to increase revenue streams.

High value, low feasibility (Zone of mismanaged expectations):

• #3 falls here as chances of successful execution are low though the business value could be relatively high. It is assumed that with multiple products and service offerings across channels and countries full integration and 360-degree customer view is not immediately viable for execution.

Low value, low feasibility (Zone of career-limiting moves):

• #5 failure of machine readers is assumed to be <.5% worldwide and not a worthy investment. It is not a top 20% area of customer dissatisfaction.
• #16 fall in this category have little business value and a low probability of successful execution
• #9 use case it a huge undertaking given the partnerships needed and license costs but little business value and potential for successful execution. Some companies that have popular brands may not be averse to partnerships.
• #10 use case falls here as customer favorite character preferences may change over time, especially with younger audiences.
• #15 use case is may have constraints on monetizing retail data due to data privacy policy with existing customers on selling data to 3rd party. It could also take away business from wallet-share.

Low value, high feasibility (zone of user disillusionment):

• #14 use case may not be as feasible as AMC partnerships may not materialize and other data privacy constraints may apply. While executing this is easy, given existing playbooks, it doesn’t translate to high business value as a small portion of the overall business revenue.

References

Digital Lessons from Disney’s $1 billion experiment: Marketing and Operations intertwined. (2016). Big Data to Big Profits. http://bigdatatobigprofits.com/2016/03/30/digital-lessons-from-disneys-1-billion-experiment-marketing-and-operations-intertwined/

Lovejoy, B. (2021, March). iPhone and Apple Watch MagicBand option coming to Walt Disney World Resort. 9to5mac.com. https://9to5mac.com/2021/03/12/iphone-and-apple-watch-magicband/

Oskwarek, Z. (2021, March). What is the Future of Disney MagicBands?. Ziggyknowsdisney. https://www.idcband.com/en-us/blog-us/10-lessons-disneys-magicband-can-teach-theme-parks/