Is Netflix a Harbinger for the Theme Park Design Industry?
I know what you are thinking: Theme parks? Netflix? What gives? They represent contrasting business models- one is film content based and the other is theme park based. But, Netflix’s ability to disrupt the film and television industry in record time has ushered in a new approach in film-making. And, it’s an approach that could have wide reaching implications on the future of the theme park industry.
Despite being different business models, they are both in the business of creating and sharing stories. In fact, Netflix has risen to become one of the premier storytellers in the film and television industry. And, it’s many business processes are changing how networks are determining what movies and television shows get made. But, is this a harbinger for the future of theme park design? To answer this question, lets start with a story, of course — The Netflix Story. This is a story about how Netflix used data to transform an industry and become the King of a town called Hollywood.**
The Making of a King
Once upon a time, in 1997, Reed Hastings, a bustling entrepreneur, rented a VHS movie, called Apollo 13. Like many of us have done in the past with video rentals, he forgot to return the rental on time. Six weeks later, when he decided to finally return it to his local Blockbuster, they issued him a $40 overdue charge. Embarrassed to confront his wife about the late fee, Reed started an excuse to wiggle his way out of the” doghouse”. In this deep thinking process, he began to really think about why and how movies are rented. And, he concluded there was a better way to rent movies nobody had created, yet. So, he begin to investigate the idea of creating a movie-rental business. One that would challenge the conventional model of renting movies. He came up with a DVD by mail service.
At the time DVD’s were not universally adopted by most households. But, a friend told him about the potential of the technology and it’s small-form factor. Compared to VHS tapes, DVD’s were lightweight, easier to maintain, had increased durability and provided clearer picture quality. Upon learning about DVDs, Hastings mailed himself CDs, a single disc per envelope, in order to provide a proof-of-concept for a DVD rental by mail service. He discovered that the CDs that were shipped arrived in great shape. This validation became a turning point for Hastings. And, the rest, as they say, is history.
On April 14, 1998 Netflix begin operations with Hastings at the helm. Using a website, people from around the U.S. could place rental orders online and receive their DVDs by mail. In addition, the service provided users with movie reviews and automatic suggestions to rent additional DVDs.
A DVDs by mail service provided a more efficient and convenient service than conventional movie rental business models, because its delivery method and its vast library of content. You know longer had to rush to a video store only to find out they were sold out of the title you wanted to watch. The DVD would arrive in the safety of your own mailbox ready to be played in your living room. At the height of Netflix’s service, it was moving 1.5 million DVDs a day across a network of warehouses in the U.S. , which each contained over 60,000 unique film and television titles.
But, Netflix realized a problem very early on with this business model. “[DVD] selection is distant in time from viewing, people select carefully because exchanging a DVD for another takes more than a day, and we receive no feedback during viewing”, according to Xavier Amatrinian of Netflix’s Personalization Science and Engineering. As a result, it had to rethink it’s own business strategy. To solve this problem, Netflix decided to launch a instant streaming service in 2007.
This streaming service, revolutionary even today, allowed people to “sample” watch movies and decide something that best suited their interests without ever needing to commit. This allowed customers to have their cake and eat it to. But, Netflix, also, saw an opportunity in online streaming — consumer insights and personalization.
Using advanced algorithms, Netflix was able to determine what was being watched andhow long it was being watched for each customer. Like the original website, this provided the ability for Netflix to recommend content based on previous watch history. But, they believed there was more groundwork to lay down. They not only wanted to understand what content its customers might be interested in watching, but deconstructwhat made customers want to watch a film or television show in the first place. With this level of information, Netflix believed it could not only determine what existing content its customers would be interested in watching, but, also, identify what type of content was missing- the holes in the fabric of the film and television industry . Then, create it.
Reverse Engineering Hollywood
If you aren’t from or have lived in Los Angeles, California, you might not realize the amount of people in the city is trying to catch their big break in Hollywood. The city is filled with bustling actors, actresses, directors, designers, and screenwriters waiting for their shot to make it to the big screen. In particular, when it comes to screenwriting, the backbone of the industry, nearly 50,000 screenplays are registered with the Writer’s Guild of America each year for peer review. That’s a lot of competition, considering only about 700 movies are created in the US every year, according to the Motion Picture Association of America.
With so much content being backlogged, this created a big problem for Hollywood executives and decision-makers in the past: Locating the perfect script to propel a studio to their next mega blockbuster hit was like finding a small needle in a very large haystack. So, like a true treasure hunter Netflix needed a figurative metal detector to help them sort through the clippings.
Netflix Quantum Theory
Before Netflix decided to produce its own content, Todd Yellin, VP of Product Innovation for Netlfix, was tasked with tearing the film apart and understand what makes a film likable. In 2006, the year before Netflix launched it’s streaming service, Yellin and his very small engineering team created a 36 page document known as theNetflix Quantum Theory, which detailed means and methods for evaluating film and television content still unrivaled by many competitors in the film industry, today.
The system classifies nearly every element of the movie to formulate relationship models with other content and users. Every movie feature, or descriptor, ( plot, setting, character-types, descriptors, etc..), was precisely tagged. Then every movie was ranked on a scalar level of association to a genre (e.g. 1–5) . For example, an action centered film will get ranked in terms of its quality and frequency of comedy and romance, despite it not being the primary genre of the film.
This detailed analysis formulated an unprecedented matrix of elements which tagged content with metadata — data that provides information about other data. When coupled with personalization algorithms, the data output formulates what is known as “micro-genres”. Micro genres uniquely classify a movie into unique market segments using the best associated descriptors, usually between 3–5. So, a micro-genre might describe a film, as a “Feel-good Foreign Comedies for Hopeless Romantics”, according to The Atlantic.
This process over the years has amounted to identifying 80,000 unique micro-genres to describe the content on Netflix’s servers. For perspective, when I personally surveyed people to name as many genres as possible, most could not list more than 10 off the top of their head.
“The data can’t tell them how to make a TV show, but it can tell them what the should be making.”
This level of market segmentation allows Netflix to create unique user experiences for every user that connects them to relevant content using predictive analytics. Predictive analytics uses historical data to project the future. And, based on how users engage this information, they can confirm the legitimacy of their algorithm’s predictions and determine if they’re hypotheses was correct.
With a new found confidence, Netflix knew it had discovered a set of formulas for creating highly likable content. In 2013, Netflix released its first original television series “House of Cards” . The series, directed by David Fincher, starring Kevin Spacey, is one the most popular streamed television shows ever.
From Data to Content
In 2008, Netflix composed of only 12 million subscribers around the world compared to its 83 million subscribers of today. However, 12 million unique data trails still means a whole lot of data. And, these data trails became the differential when the creators of “House of Cards” were shopping for a content producer.
Using customer data, Netflix had understood the popularity David Fincher’s “The Social Network”, the British version of “House of Cards”, and the work of Kevin Spacey. Based on cross referencing these circles of information, when they reviewed the show, there was a strong level of confidence by executives that the show would be a hit. And, they were right. In it’s first year it was nominated for 9 primetime Emmys and took home 3 awards.
This proven method of success has proven that that a data-driven model for content curation in entertainment might work. And, today, the process is being scaled to keep more great content into the platform and consumers glued to Netflix, while reducing “bloat” — rarely watched programming- by nearly 50%. According to Business Insider, today, people spend watching an estimated 121 minutes of content per user per day on Netflix. That’s 10 billion hours of content per fiscal quarter and 905,892,833 seasons of House of Cards. This level of online content consumption is unprecedented and has caused the Film and Television industry to overhaul the way it conducts business from bookend to bookend.
The Netflix Effect
Netflix’s use of data to better understand the Film and Television industry has been sweeping Hollywood by storm. Companies from all business segments of the entertainment industry, sales, theaters, screenplay development, and much more, are working hard to not get left behind the content streaming giant.
For example, Fandango, the popular ticket purchasing service, is on the forefront of sales analysis using data from ticket sales and movie trailers. With every ticket purchase on a credit card, mouse click or tap, review posted on Rotten Tomatoes ( a Fandango company), they can segment customer information to an unbelievable granular level of detail on their marketing dashboards.
Knowing where, when, what, and how, Fandango can dial up how movies and trailers are performing in real-time. To take it step further, they know when you started watching a movie, and when you finished it. They know what trailer cuts work better in New York versus the Texas or United States versus Europe. And, this becomes a vital resource when studios are looking to market a new film to the world.
This constant quantification of metrics turns into actionable business insights to improve their services and customer experience. It provides a God-like view for the entire entertainment industry when cross-referenced with current market trends and behaviors of its consumers to make predictive forecast of what might be the next big blockbuster movie. And, that is where companies like The Black List comes in.
The Black List, created by former Universal Development Executive Franklin Leonard, began as a concise list of the most promising screenplays in Hollywood mentioned by anonymous studio executives that have not begun production. But, what began as a way for executives to cut through the noise of thousands of screenplays, turned into a promising business for The Black List.
Today, The Black List has created a way to evaluate and rate scripts for Hollywood that draws upon Netflix’s model. Using tagging and peer reviews, scripts are rated based on their potential to become a blockbuster film. If you are executive looking for a film to meet the needs of your most recent market trend data, it’s a matter of making a simple query in the web page search bar. Are you looking for an action-western-romance film based on Tennessee that appeals to global audiences? The Black List algorithm can find it and deliver it to you in seconds.
This constant feedback loop of information has created a new found confidence in Hollywood. This can be seen in the recent uptick of content investments by major studios. Netflix alone, is projected to license over 5 billion dollars in new content alone — a huge win for consumers. This type of investment is beginning to force the hands of major studios such as, Disney, CBS, Discovery Channel, Time Warner (owner of HBO), and NBC, to reevaluate their entire content strategy and spend more money on upcoming television shows and films. According to Leslie Moonves, CEO of CBS, “[Y]ou’ll see us do more content, but only if we know it’s going to be profitable.”
The anatomy of film is slowly being torn and broken apart to formulate a new data-driven taxonomy that can be understood and utilized as a tool for studios around the world to grow the customer experience and increase profitability. It’s important to state , however, that much of the algorithms pushing a new golden era of film and televison does not determine how something should be created, but it only informs what should be created. And, that’s important when talking about how this effect the future of the theme park industry.
The Future of the Theme Park Industry
I, personally, like to refer to the theme park industry as the child of three parent industries: Technology, Entertainment and Architecture. The business trends in these industries can offer the best glimpses into the future business practices, experiences, and health of the theme park industry. Today, every one of these industries are now using data to transform how they operationalize their businesses to capture more value. Thus, I can only forecast that theme parks are not far behind this “data-driven” movement.
That means the days of autocratic design are coming to an end (sigh of relief). And data-driven projects are on the way in. The biggest advantage of a data-driven theme park enterprise, compared to its past, lies in it’s ability to analyze and make critical business decisions that impact bottom line and consumer experience in near real-time in all phases of a project’s life cycle — from concept to operations.
Today, the forecast for data driven theme park projects are promising. Data supply chains are cheap to operationalize, fast and infinitely scalable. It has the opportunity to create greater confidence for businesses when developing future attractions, restaurants, shops or experiences. It quells the design-by-committee issues often apart of the design process. And, it liberates vertical innovation often dictated by company seniority.
From Data to Theme Park Design
The opportunity to collect and measure data in theme parks, today, is everywhere. From the rides, to the shops, to your favorite meet-and-greet experiences, data exist in every transaction made by guests — new or old. These are known as “events” in the big data world which signal an action was completed. It’s what Netflix measured when people navigated it’s web pages. These events can build up over time to formulate a data trail for businesses. Data trails inform businesses about the challenges and successes of their user experiences.
For the most part, data analytics has customarily stayed within the marketing, finance and logistics/systems departments of enterprises. But, it is increasingly becoming more integral in measuring user experience design and services over the past few years. As mentioned in a past article, “Data has a Theme: Themed Entertainment = Data”, data can foster and support much more rich customer experiences then ever before. And, like Netflix can forecast the next hit show or film, data will allow the theme park industry to forecast what the next great theme park attraction or land will be, as well.
Currently, most consumer experience data has been collected and tracked through surveying and operational inputs like, “How entertaining was this attraction?” or “How many people ride this attraction per hour?” We’ve all been pestered by survey takers at the main gates of your favorite theme parks. And, then we try to forcibly remember the events of our day and conclude some personal feelings about our experience into a series of scalar opinions — very bad/poor to very good/excellent. However, the future provides a better way in discovering an honest customer journey.
Emerging technology, such as NFC, RFID, and Bluetooth LE provides better ways to understand and quantify customer actions. These technology signals, today, allows you to map customer experiences in detail with lower friction in data collection processes. Unlike surveys, user experience data from such technology reflects how people behave rather than what they believe, which means the data quality incorporates less personal bias. Personal bias can disrupt lucid contemplation of an issue by introducing externalities that may or may not be relevant to the survey at hand. The technology will probably not eliminate, more analog ways of collecting data, but provide a clearer picture. And, that’s important when you trying create better customer experiences.So, once you have collected the data, what exactly do you do with it? This is the question Netflix had to answer.
The solution came in the form of identifying existing and original content that would increase the value of Netflix. As we move towards the future, and the theme park industry continues to grow in competition, business leaders and shareholders will want to understand what exactly they are spending capital on and what will be their ROI. This behavior has recently created some bias in theme park development, one that is heavy leaning towards fostering existing brands.
Safe Harbors of Existing Intellectual Property
Creative industries, such as theme parks and entertainment, can carry a high premium with their ideas, historically. Cost associated with building a new theme park attraction and/or land is on the rise as resources and services become universally more in demand around the world. As a result, placing your bets on the “creative genius” of one or a few sets of individuals without the data to back up their decisions is increasingly becoming a poor business plan — you expose yourself to large amounts of financial risk. Not to mention, you lean your company’s future success on an individual or group rather than a scalable strategy for design.
As a result, theme park business executives have recently flocked to the safe harbor of Intellectual Property(IP) attractions. For the purpose of this article, we describe the IP attraction as an experience driven by existing film, books, or television brands. These experiences create more predictable revenue streams based on historical data from other markets, such as merchandise, movie ticket, and book sales to the appeal and delight of Wall Street investors. This strategy has universally shifted business models of major theme park companies once known for dreaming up popular original attractions, to bet more money on IP driven attractions.
IP driven attractions have their market benefits. They strengthen core brands and improve it’s network effects by exposing brands, characters, and products to new customers — offering another level of engagement for existing customers. From Marvel to Harry Potter, the ability to transform the brand into a variety of mediums, experiences and products creates more touch points for brands to increase their pull on a market and increase bottom line. However, on the downside, IP driven attractions can fragment the theme park experiences and reek havoc on it’s experiential synergy. The success of storytelling in theme parks, like Disneyland, lies in its ability to seamlessly blend narratives into an overall composition.
But,when it comes to customer experiences, IP driven attractions and/or lands can bring customers closer into the world’s of their favorite characters. Film and television blur the line between fantasy and reality. This type of reality-distortion programming overlays images in our head of possibilities (some more far flung then others) about daring adventures and futuristic tomorrows we can only imagine . So, when someone offers us a chance to become a wizard or be transferred to a galaxy far, far away, we jump at the opportunity to live out our most child like fantasies. And,that proposition can be extremely valuable for entertainment companies.
We should, however, always strive to maintain a balance. Theme parks can be vessels to support existing branding initiatives for studios.But, they should also support original ideas that can spawn new adventures. And, just like data can predict the success of IP attractions, data has the potential to forecast new attractions that can meet the missing needs of a market.
The Return of Originality
Original attraction creation in theme park development is increasingly becoming a lost art in favor of the safe harbors of IP attractions. However, like Netflix used data to become a leader in Hollywood with original content, data may give original attractions a fighting chance in the boardrooms of tomorrow.
When “House of Cards”, originally a BBC production, was proposed for a remake, it was the Netflix subscriber data that clinched the decision to license the original series for executives. When speaking to Wired, Netflix Communications Director, Jonathan Friedland, said “We know what people watch on Netlfix and [are] able to — with a high degree of confidence — understand how big a likely audience is for a given show based on people’s viewing habits.” So, how would a similar system work for theme parks?
Theme parks are complex machines, that represent many interconnected lines of business — too difficult to probably synthesize entirely into a simple useful diagram. But, for the purpose of this article, a data driven theme park model will be simplified into 3 circles of information, like the Netflix House of Cards model : Scenario, Market Analysis and Value Creation Opportunities.
When referring to scenarios, we are referring to what it so commonly known as the customer journey. Scenarios represents a short story that describes a customer journey by defining tasks, behaviors , attitudes and expected outcomes- a structure. This is a high-level idea that provides a framework for ideation, does not denote large amounts of detail and can be easily adaptable/customization.
Market analysis is a set of methods implemented by business to quantify and measure consumer metrics in order to gain insight into domains. So, if a theme park has a large market population of ticket purchasers who are local and their has been a local uptick in Cyber punk culture (festivals, conventions, meetups, shows), for example, cyberpunk may prove to be a good idea for their next attraction.
Value Creation Opportunities
For those, who are not familiar with business economics, value creation can really be a rabbit hole. But, very simply put: value creation is the ability for a business entity to create value ( future products, experiences, video content, etc.) for customers and shareholders to insure future operations and sales. This means if I develop “x” it will create “y” opportunity for customers and “z” opportunity for stakeholders in the future- the ROI can be forecasted with a high level of certainty.
To many creative professionals, this may not be important, but the higher potential an attraction has to create value in terms of merchandise, technology, books, films, or video games, the more executives start listening. It means the attraction has the potential to leverage the attention of a large audience over multiple markets. For example, a cyberpunk attraction might have more value creations in leveraging technology markets then a haunted mine attraction might. And, understanding the financial impacts of one versus the other is increasingly becoming more important for a theme park business’s bottom line.
The combination of these three features, when overlaid into a Venn diagram, can create a “micro-genre”, which might denote a very successful future attraction that makes creative and business stakeholders happy. For example, meeting a growing market of cyberpunk fans with a mystery adventure that uses cyber experiences, such as augmented reality, might prove to be a lucrative opportunity. And, it might be able to have a film spin-off, like Disney’s Pirates of the Caribbean attraction, created in the future.
But, this Venn diagram does not represent the true complexity of theme parks nor portray the predictive analysis models needed to achieve a Netflix like system. But, it projects a hypothesis on how future original attractions might be created to meet missing market needs- the holes in the fabric of the theme park industry.
The important thing to understand here is there is a difference between taking some marketing data and responding to that data with a creative concept never to look at again — the old Hollywood business model — versus data being fully integrated into your decision making process over the entire project lifecycle — the Netflix Model. The former is a reaction to data and the latter is operationalizing data over it’s entire business. This means designers know the value of what they are creating and what they are cutting. This means operators knowing the difference between what they need from what they want. Everyone becomes more accountable for their decisions, as a result.
Data As a Storyteller
Data has a story to tell. It is a storyteller. It doesn’t tell the type of stories you and I are familiar with knowing and understanding. It doesn’t tell tales of adventures, like you and I through voice. It communicates across invisible streams of energy in the form of bits and bytes. But, it still is a storyteller, perhaps a far superior and genuine one.
Storytelling is about communicating information. That information can be historical, cultural, or educational, for example. But, data does something many storytellers cannot. It can know when, why and where a story should be told. Storytelling is a two-way street. It not only requires people to deliver information, but it requires people to be in a position to openly receive information. If the information is not relevant it will fall on deaf ears.
When referencing the success of Netflix in an interview, Kevin Spacey, House of Cards actor, said “[companies need to learn to] give people what they want, when they want it, in the form they want it in…”This is the future of entertainment. This is the future of theme parks.
Fundamentally, theme park designers are in the business of telling stories. That will never change. We have mastered the theater of storytelling with opulent detail and effects. We have immersed ourselves, wholeheartedly, into dramatization of character. These are all things data cannot, yet, inform. But, in all the theatrics of everything, in all the commotion of the show, has any anyone actually stopped and looked at their audience to see who is actually watching?
Netflix has. Perhaps it’s time for the theme park industry to do the same.
**To be clear, this is a story about content strategy not content distribution. Some of you might have been expecting me to discuss how Netflix’s platform might become a model for content distribution in theme parks; binging personalized stories to VR/AR devices, cars and cell phones. Whilst this certainly remains a valid moonshot idea, (we can reference Netflix’s, Snapchat’s, Facebook’s and the Video Game Industries early success.) I believe that is a much deeper and universal discussion that centers around platforms creation (and probably take a book versus an article to discuss, too).
Kevin Boutte is the co-founder of OpenBloo, an experience signal measurment and management company focused on converting data insights into experiences that uniquely define a customer’s journey.You can follow him on Twitter at https://twitter.com/kevin_boutte