The industry’s data is locked in the 1950s, dragging quality down with it. Web3 holds a key.
Entertainment is suffering from bad data. Between an outdated data behemoth's $16B monopoly over the market and digital streamers' (Netflix, Hulu, etc.) refusal to transparently share their user's data, no one really knows what audiences actually want to watch.
Without a truly objective source of viewer preferences, digital streamers are suffering from audience capture and creating more and more bad content that fuels 'click-bait' incentives and attracts a vocal minority. If there's any hope of returning to a Golden Age of digital content, we need to find a way to democratize entertainment data and let viewers express what they actually want to watch.
Starting in 1950, Nielsen took over the television data market by measuring viewing behavior via physical devices installed in participants' homes. While novel at the time, the core collection methodology hasn't innovated in a meaningful way in over seventy years.
As the consumer shifted away from traditional television towards digital content, Nielsen's collection methods remained "derived from a subset of Streaming Meter-enabled TV households."[1] Because of this analogue collection method, the sample sizes have remained modest, with the Nielsen Ratings only based on a sample of 40,000 homes.[2] That means a country of more than 300 million are represented by a group of people who wouldn't even fill certain college football stadiums.
The industry’s frustration over the antiquated technology is growing. As the New York Times recently wrote,
"TV executives have been complaining about Nielsen for years…the 98-year-old research firm whose name is practically synonymous with TV ratings, uses antiquated technology that hasn't kept up with viewers who have moved away from cable and network TV. Now the television industry is looking for other options."[3]
Other strong arrivals into the marketplace have attempted to tap into these holes in viewership data through various indirect means such as monitoring search engines, wikis, rating sites…etc., however, no solutions have emerged that capture real-time viewing habits of computer-based streaming.[4]
In addition to the outdated collection methods, Digital Streamers keep their viewership metrics siloed behind their own corporate walls, leaving each unaware of what content is actually in demand by viewers outside their ecosystem.
By democratizing this data and making it available to everyone, we can make entertainment better:
‣ Viewers can find the best content across all platforms based on what people are actually watching (think leaderboards for shows and movies).
‣ Digital Streamers can create more in-demand programming based on objective cross-platform preferences.
‣ Creators can tap into their core audience base.
‣ Advertisers can act upon objective performance metrics and reward the best creative content in the market.
So, how can the principles of the Web3 movement address the enormous hole in the entertainment industry’s data? A clue can be found in the first paper to coin the term ‘Data as Labor’ where the authors argue,
“User data is typically treated as capital created by corporations observing willing individuals. This neglects users’ role in creating data, reducing incentives for users [and] distributing the gains from the data economy unequally… Instead, treating data (at least partially) as labor could help resolve these issues and restore a functioning market for user contributions.”[5]
Any workable solution to the entertainment industry’s data gap must be founded with user incentivization in mind by treating user contributions as a core component of the marketplace, not simply as capital to be exploited.
Building upon these principles, we are proposing a new community-owned DAO (decentralized autonomous organization) powered by multiple data-unions at its core called cineDAO.
The data-unions will allow the DAO to be self-sustaining with a marketable product of its own. Using the revenue generated by sales on third-party Web3 marketplaces like Streamr and Ocean, cineDAO can fund larger-scale community projects, support charities, and reward users.
The DAO’s community-owned dataset is powered by a free Chromium browser extension where users anonymously and passively contribute digital streaming data while streaming right on their favorite sites. User participation is rewarded via community tokens, allowing for decentralized governance in steering the DAO.
A decentralized approach not only allows data to be shared most freely, it also provides the best product on the market. Just by anonymously collecting Show Name and Minutes Watched, we are able to calculate a myriad of trends on a real-time basis, including the gold standard: Average-Minute-Audience. Considered by most advertisers to be the most important metric in determining advertising costs, our DAO is able to calculate AMA up to 900x more accurately than the industry average[6].
As individuals, our data is inherently valueless. While data-unions in their most basic form promise to reward users for their own data, these rewards are often too small to be meaningful on an individual basis. The real exciting opportunities of data-unions only fully emerge once the community focuses a large pool of small contributions towards a shared cause (e.g. focused through a DAO).
Some projects an entertainment-focused DAO could embark on include:
‣ Creation: A decentralized film festival where the community votes on the best short film and rewards the filmmaker with community funds to create a feature-length film.
‣ Democratization: An online leaderboard featuring the top shows based on real-time streaming data from the community’s dataset. The internet’s box office.
‣ Social Impact: Donating a percentage of data-union revenue towards community-voted causes.
‣ Independent Distribution: A crowd-sourced distribution model that allows independent films to reach a wider audience without passing through traditional gatekeepers.
‣ Viewing Rewards: An incentivized-viewing marketplace where small creators can offer tokenized rewards for community members to view their content and grow their audience.
By decentralizing entertainment data, democratizing it for public markets, and organizing a community around shared causes, Web3 has the potential to bring the entertainment industry out of the 1950s and allow artists to make entertainment that takes risks, that’s dangerous to corporate profit structures…entertainment that we actually want to watch.
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Nielsen. "Streaming hits all-time weekly high in December, according to The Gauge." Nielsen, 13 January 2022, https://www.nielsen.com/us/en/insights/article/2022/streaming-hits-all-time-weekly-high-in-december-according-to-the-gauge/. Accessed 17 March 2022.
Porter, Rick. "TV Long View: A Guide to the Ever-Expanding World of Ratings Data." The Hollywood Reporter, 2019, https://www.hollywoodreporter.com/tv/tv-news/tv-ratings-explained-a-guide-what-data-all-means-1245591/amp/. Accessed 10 March 2022.
Hsu, Tiffany. "Now TV Wants Nielsen to Measure Up." The New York Times, 12 November 2021, https://www.nytimes.com/2021/11/12/business/media/nbcuniversal-nielsen-tv-alternatives.html. Accessed 13 March 2022.
Parrot, Rebekah. "What is Demand?" Parrot Analytics Help Center, 2022, https://helpcenter.parrotanalytics.com/en/articles/3787220-what-is-demand. Accessed 13 March 2022.
Arrieta Ibarra, Imanol and Goff, Leonard and Jiménez Hernández, Diego and Lanier, Jaron and Weyl, Eric Glen, Should We Treat Data as Labor? Moving Beyond 'Free' (December 27, 2017). American Economic Association Papers & Proceedings, Vol. 1, №1, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3093683
Nielsen. "NIELSEN LOCAL TV VIEW." Nielsen, 2019, https://www.nielsen.com/wp-content/uploads/sites/3/2019/04/NLTV-Quarter-Hour-Flow-HTR.pdf. Accessed 14 March 2022.