A satisfying customer experience is the driver of any business’s revenue growth. Disney Theme Parks is no exception. Disney is executing a guest personalization strategy leveraging wearable computing (and analytics) to track, measure and improve the overall park experience. The ultimate goal is increase sales, return visits, word of mouth recommendations, loyalty and brand engagement across channels, activities, and time.
Wearable computing seems to be the next big thing. Many believe a new crop of gadgets — mostly worn on the wrist or as eyewear — will become a “fifth screen,” after TVs, PCs, smartphones, and tablets.
Wearables are already being used to monitoring vital signs, wellness and health. Devices like Fitbit, UP, Fuelband, Gear2 track activity, sleep quality, steps taken during the day. Consumers of all sorts — fitness buffs, dieters, and the elderly — have come to rely on them to capture and aggregate data.
What most people don’t understand is how powerful wearables (coupled with analytics) can be in designing new user experiences. Businesses thrive when they engage customers by creating a longitudinal view of each customer’s behavior. To understand the wearables use cases and potential we did a deep dive into a real-world application at Disney Theme Parks.
Wearable Computing at Disney: MyMagic+
Disney has been rolling out a new guest experience called MyMagic+ to the 30 million guests per year at the Walt Disney World Resort in Orlando.
Realizing that guests were arriving with smartphones and tablets in hand and expecting access to more information, Disney started the MyMagic+ initiative to provide a next generation experience. The overarching goal of MyMagic+ is to provide a much more personalized friction-free vacation at various theme parks, even down to characters knowing your name.
Disney is following in the steps of Harrah’s (now Caesars Entertainment) Total Rewards program that provided an integrated experience for gamblers across nearly 40 resorts and casinos. Loyal spenders were rewarded with innumerable entertainment options, enticing special offers, free hotel rooms, and different ways to redeem credits.
How does MyMagic+ work?
A key element of MyMagic+ is MagicBand. MagicBands is a ultra-personalization experience. These brightly colored bands link with online profiles for each visiting family member, and can be scanned at park kiosks to access advance ride bookings, receive customer service, and pay for all the stuff your kids want to buy.
The key to a great experience is being predictive in terms of context. For instance, while wearing her MagicBand, a young lady who loves Disney princesses might be approached by her favorite of the park’s life-size characters and be greeted by name.
Disney extracts and integrates all the information about the guest from all the park siloed data systems. as well as from external sources. This allows them to create a longitudinal view of each guest’s behavior over channels, activities and time.
Sophisticated pattern-detection science is applied against the 360-degree view to extract each guest’s behavioral predictors – like early warning on guest/family fading, real-time park experience dynamics (via feedback), and each guest sensitivity to specific promotions. The objective is to turn these signals into individuated recommendations served via customer marketing systems.
Technology behind MagicBand
According to Disney, each waterproof MagicBand contains an HF Radio Frequency device and a transmitter which sends and receives RF signals through a small antenna inside the MagicBand and enables it to be detected at short-range touch points throughout Walt Disney World Resort. MagicBands can also be read by long-range readers and used to deliver personalized experiences, as well as provide information that helps us improve the overall experience.
The next version of MagicBand might have much more computing built into it. If they go the Android route…Google has announced an SDK aimed at making Android, more palatable for small devices. Android apparently was consuming more battery. Samsung tried using Android for the Galaxy Gear, its smart watch, and the results were not so great. It couldn’t last very long without a recharge. For the Gear 2 Samsung dropped Android in favor of Tizen, its own operating system. I won’t be surprised if Apple and Disney team up in a few years around this.
Another day, another data breach. Just received another “We’re sorry you got hacked”…letter.
This is the fifth letter I have received in the past 3 months: Forbes.com, Target, Neiman Marcus, credit card company and a previous employer. What is going on?
Why aren’t firms investing in beefing up their predictive ability to spot the cyber-security intrusion threats? What’s taking them so long to identify? Why is the attack signature - sophisticated, self-concealing malware - so difficult to spot? Do firms need to invest in NSA PRISM type threat monitoring capabilities?
The three impediments to discovering and following up on attacks are:
- Volume, velocity and variety – Not collecting appropriate security data
- Immaturity and not identifying relevent event context (event correlation)
- lack of system awareness and vulnerability awareness
Obviously… where there is pain…there is opportunity for entrepreneurs. There is a growing focus on big data use case for security analytics after all the breaches we are seeing.
Here are three recent examples that i was personally affected by – Forbes, Target, Neiman Marcus.
The cost of data generation is falling. The cost of collection and storage is also falling. The speed of insight-to-action is increasing. The bottleneck has clearly shifted from transaction processing to Analytics & Insight.
Here are just a few examples of analytics at work:
- Target predicts customer pregnancy from shopping behavior, thus identifying prospects to contact with offers related to the needs of a newborn’s parents.
- Tesco (UK) issues 100 million personalized coupons annually at grocery cash registers across 13 countries. Predictive analytics increased redemption rates by a factor of 3.6.
- Netflix (Cinematch, Max) predicts which movies you will like based on what you watched.
- Life insurance companies calculate the likelihood an elderly insurance policy holder will die within 18 months in order to trigger end-of-life counseling.
- Con Edison predicts energy distribution cable failure, updating risk levels that are displayed on operators’ screens three times an hour in New York City.
- Linkedin (People you may know, Jobs you may like, Groups you may be interested in)
Take for instance, how Lufthansa is innovating around revenue management – pricing and capacity management. It’s running a pilot project with SAP’s HANA. HANA’s in-memory technology allows Lufthansa to load more data and application functionality into memory—rather than from disk— dramatically improving application performance.
Revenue optimization calculations are now done in real time, rather than in batch mode. The application aggregates more data than previous models (e.g., passenger data over two years). The system calculates and optimizes the revenue for origin/destination (OnD) itineraries, and bases pricing on passenger profiles. It also estimates the likelihood of cancellation and no-shows on flights, and thus how much overbooking to allow.
To meet demand for faster/better/cheaper innovation around analytics, CFOs and CIOs are rethinking their silo’d sourcing strategies and looking at new way of doing things via out-tasking, IT outsourcing and business process outsourcing their Analytics and Data Science functions.
The “should we or shouldn’t we outsource data science” discussion is heating up in board-rooms and executive suites as analytics becomes core to the firm, C-level execs have to consolidate efforts for delivering the same services to different groups within an organization.
Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data (Cloud, On-Premise, Hybrid IT) is challenging.
It’s a paradox of the information age. The glut of information that bombards us daily too frequently obscures true insight.
And every customer interaction is generating a growing trail of data (“data exhaust”). Every machine that services the customer is generating data. Every conversation, transaction, engagement, touchpoint location, offer, response is a potential digital bread-crumb of opportunity.
Now let’s flip the context. A typical mobile user check their phone interface 150 times a day for updates. A Gen Y or Millenial user obviously much more than a Gen X user. The consumption patterns for information are changing continuously. Facebook style real-time updates which were revolutionary 5 years ago seem outdated in the mobile world. We live in an “attention deficit economy” where attention is the new basis for competition. The firms that create the evolving experience using data which can grab/hold your attention will attract marketing and ad $$s.
As a result, the buzz and hype around data…small data, big data, machine data, social data, mobile data….is relentless. As a result there are a lot of new initiatives and companies. I have been asked repeatedly by a lot of entrepreneurs and strategy teams about analytics market size and opportunity size. Product and services firms are also interested in opportunity sizing as they create new offerings in this area.
I thought i would share a mashup of industry and market sizing data i have collected so far.
- How big is the market for Analytics, Big Data?
- How big is the market for Digital Customer Interaction or Engagement?
- How big is the market for Mobile and Social Intelligence?
- How big is the market for Wearables?
- What is growing fast, faster and fastest?
All good questions as services firms think about digital strategy, analytics and future state. You always want to be in the “hot” area… selling is easier, valuations are richer, revenue growth percentages exponential.
Self-tracking, Seamless Engagement and Personal Efficiency improvement’s new frontier is Personalized Big Data. This is really becoming a viable idea around wearable and sensor computing and the basis for new data platform wars.
The new platforms for data driven life– that collect, aggregate and disseminate — will cover a wide range of new User Experience (UX) use cases and end-points… medical devices, sensor-enable wristwear, headset/glasses, tech-sensitive clothing. All of them are going to collect a lot of data, low latency analytics, and enable data visualization. Several new firms are entering the activity tracker market LG (Life Band Touch), Sony (the Core), Garmin (Vivofit), Glassup, Pebble, JayBird Reign etc.
Data collection is just one piece of the solution. The foundation for personalized big data is Descriptive and Predictive Analytics. Ok…What do i next? what is the suggestion? in the form of predictive search (automated deduction or augmented reality). A wide range of start-ups - Cue, reQall, Donna, Tempo AI, MindMeld, Evernote, Osito, and Dark Sky - and big companies like Apple, Google and Samsung are working on predictive apps — aimed at enabling new robo-assistants that act as personal valets, anticipating what you need before you ask for it.
The following eight secular disruptive themes are what Goldman Sachs believe have the potential to reshape their categories and command greater investor attention in the coming years.
The Eight Themes:
- E-cigarettes – The potential to transform the tobacco industry
- Cancer Immunotherapy – The future of cancer treatment?
- LED Lighting – A large, early-stage and multi-decade opportunity
- Alternative Capital – Rise of a new asset class means growing risk for reinsurers
- Natural Gas Engines – Attractive economics drive strong, long-term penetration
- Software Defined Networking (SDN) – Re-inventing networking for the cloud era
- 3D Printing – Disruption materializing
- Big Data – Solutions trying to keep up with explosive data growth and complexity (Industrial Big Data and Personalized Big Data)
These eight themes – through product or business innovation – Goldman claims are poised to transform addressable markets or open up entirely new ones, offering growth insulated from the broader macro environment and creating value for their stakeholders.
Goldman focuses on the impact of creative destruction – a term made famous by the Austrian economist Joseph Schumpeter, which emphasized the fact that innovation constantly drives breeding of new leaders and replacement of the old.
Health expenditures in the United States neared $3.0 trillion in 2013 which is over ten times the $256 billion spent in 1980. Almost 15% of U.S GDP is estimated to be spent on healthcare.
Approximately 149 M people in the U.S rely upon health care that is funded by an employer, according to Kaiser Family Foundation. In 2012, the average annual cost of health coverage per employee was $10,558, compared to $4,924 in 2001 – a 106% increase in 11 years. (Source: Mercer)
The focus in Healthcare today is to reduce the amount of waste in the health care system through the implementation of new forms of health IT… that reduces inefficiencies, redundancies and administrative costs. According the CEO of Aetna…”the health care system wastes more than $765 billion each year – that’s 30 percent of our health care spending.”
As a mega-vertical, healthcare covers several major segments (the 7 Ps)
- Payers (Health Insurance and Health Plans),
- Providers (Hospital Systems, Labs and IDNs),
- Pharmacy (retail distribution networks), and
- Pharmaceutical and medical equipment manufacturers,
- Prescribers (Physicians and clinics)
- Police (regulators)
While spending on health care is dominating headlines, the health care industry (7Ps) is in a state of flux. Stakeholders across the health care sector are running hard to reduce costs. The drivers impacting cost of healthcare include:
- Aging population – 100% are aging
- Rise in Chronic Disease – 75% of cost
- Demand for technology continues
- Drug cost – better, but still bad (Generics exchanged for biological drugs)
- Waste – estimated at 30%, but depends on definition
The healthcare ecosystem is being reshaped by two powerful counter economic forces at work: (1) Improve quality of care and (2) drive the cost of care down. Basically spend less and get more. As a result, the entire healthcare ecosystem is changing into a “information-driven”, “evidence-based” and “outcome-driven” model.
The target healthcare transformation goals are:
- align economic incentives between payers and providers,
- digital engagement…create a simpler, more transparent consumer experience, and
- connected health….technologies that seamlessly connect our healthcare system.
In this posting we look at Health Care use cases and how data and analytics are being slowly but sure being adopted in the form of informatics. All this change is being driven under the guise of Health Reform.