wadlzeno martynis janita
Lorrine Hatala
This comparative study of streaming services in different cultural and economic contexts shows how they optimize the user experience by improving recommendation algorithms, upgrading infrastructure, and developing global services, in responding to the crisis of losing subscribers.
There is already a similar trend among global streaming platforms to create a pan-media entertainment & cultural service by increasing revenue streams and merchandise offering categories, as well as keeping subscriptions and attracting more viewers in the long run.
The study displays how streaming platforms with SVOD, AVOD, and Mix-funded modes are changing their business strategies in the current hyperinflationary and increasingly competitive media market, updating how they create user-centered practices in search of ultimate commercial success.
The study is a comparative analysis of the streaming phenomenon as it happens in real-time, complementing the observation and evaluation of the latest updates in the streaming industry and predicting the future trends of global brands in the digital ecosystem under different commercial and cultural logics.
Streaming media is considered to be an updated generation of media forms (Herbert et al. 2018) and new inventions (Kaplan 2014) in the digital and internet era. By challenging the traditional distribution methods of television programs and films (Lotz 2021), streaming media uses a customized and personalized strategy (Noam 2021), applying recommendation systems to actively communicate directly with audiences and sell media viewing services and marketing brands. Currently, streaming is one of the most important forms of contemporary digital media (Lobato 2019).
The rise of streaming comes at the intersection of the digital transformation of media content production (Atkinson 2018) and the comprehensive platforming of the cultural industries (Nieborg and Poell 2018). Pan-cultural sectors, including music, film, news, radio, and other forms of communications, are being produced and stored as digital copies and distributed in national and international markets. They are strongly impacting the mass media system that has been established for decades (Murray 2016). Among the many forms, video streaming is seen as an essential example and has received a great deal of attention from academia (Dixon 2013; Flew et al. 2022; Hartley 2012).
Different business models have emerged from various video streaming media practices, including the subscriber-funded mode, also known as SVOD (subscription video-on-demand), which relies entirely on subscription revenue; the freemium/AVOD (ad-funded on-demand), which relies entirely on advertising revenue; and the Mixed-funded mode, which combines the above two features. These models highlight different levels of audience engagement, ranging from fully audience-funded to advertisers and viewers paying for their respective products to fully advertiser-funded. Thus, audience-generated data are applied for various purposes under different payment methods but are considered part of the production material in common.
This article takes a comparative approach, using the US-based streaming brand Netflix and the Chinese streaming brand Tencent Video, to emphasize the commonalities and differences between dissimilar cultural and economic circumstances of streaming video services as a common global phenomenon. The result aims to illustrate how different business practices influence and determine audience-centered financial decisions in streaming media, especially in content creation, algorithm, and infrastructure.
Netflix and Tencent Video are the top streaming service providers in terms of subscriptions in the US and China, respectively. Netflix has over 230 million subscribers worldwide (Statista 2023) and ranks No. 1 in the world in terms of subscriptions; meanwhile, Tencent Video obtains over 161 million subscribers worldwide (Tencent 2022c) and ranks No. 4. Defining themselves as transnational companies, Netflix and Tencent Video have launched their international-level business for the global market, partnering with overseas media production companies, creative industry practitioners, and local TV channels to commission and invest in the production of original titles and then distribute these TV shows and films in multiple countries with both local cultural styles and global-level production (Keane et al. 2022; Lotz 2022). Both Netflix and Tencent Video have developed distinctive streaming formats in doing business with their respective large numbers of subscribers. They value and actively exploit the impact of user interaction data on digital content production and distribution, algorithm adaptation, and industrial organization. Therefore, the practices of the two brands can serve as a reference for the diversification of streaming platforms in the international digital market.
Viewing the internet as a democratic space in which all people can participate, Keltie (2017) illustrates how the twin conditions of digital technology and media convergence have brought about changes in the foundation of media production and consumption: audiences (whether conscious or unconscious) are involved in contributing to digital content and transforming parts of their behavior into labor values that are integrated as part of the commodity. Based on these characteristics, consumers in the platform era are described as interactive audiences (Jenkins 2006), empowered audiences (Hesmondhalgh 2010), and democratic audiences (de Beus 2011). These definitions demonstrate the importance of audiences regarding digital goods in Internet commerce and highlight research on the importance of audiences as a digital workforce.
Referring to the classic discussion of the cultural industries, Adorno and Horkheimer (1993) give a more homogeneous assessment of the audience: they see the audience as passive consumers of products and texts. This historical perspective is based on the recognition of the identity and creative contribution of the producer, and more credit goes to the production team, the platform, and the investor. However, as the platform economy and business of attraction (Lpez Garca et al. 2019) increasingly rely on big data and the recommender system (Marciano et al. 2020), subscribers to streaming video services should not simply be defined as recipients of culture and objects of exchange of goods. Because audiences participate in the exchange of products and services, they are one generator of big data, and thus become part of the production.
This procedure of audience interaction in the production of data can be discussed in two parts: active data production, i.e., data that are added and recorded through the spontaneous actions of users such as searching, subscribing, commenting, etc.; and passive data production, i.e., data that viewers produce through feedback with the system, including pausing viewing, leaving the page, accepting recommendations, etc. Both active and passive data production is relevant to the commercial success of streaming media (Marr 2015). Platforms want to make the most efficient use of data and keep subscribers, while subscribers expect a better media experience and initiative in data production and content production in return (Plothe and Buck 2019).
Based on these theories, this article examines the impact of audience activity and the data they actively/passively generate on the content production and distribution strategies of on-demand media and subsequently predicts the possible involvement of audiences in the future by combining the real-time practices of Netflix and Tencent Video, brands from the Global North and South. This comparative study collects and analyses data from varied business models and cultural markets. The result is a complement to Western-centric perspectives on the business practices of digital media. It enriches the application of political economy and media industry studies to critique the business models of streaming media, enhancing the understanding of audience-generated data as an important contributor to digital content production.
We propose a critical media-industry analysis structure to illustrate that the use of big data and the reconceptualization of the power relationship with the audience are rooted similarly across the different business models of streaming media. This method is highly practical for tracking and updating market data and audience feedback in real-time. Additionally, the analytical structure is a cross-comparative model that can help assess the sources of difference in business strategies between the two brands and provide text on the conditions of implementation, effectiveness, and prospects for possible future changes. The comparative study findings will be an important tool in explaining streaming media business strategies.
The digital transition of the media industry and the rising popularity of online video viewing have witnessed a change in the relationship between content providers and content consumers. Streaming media has long been seen as a competitor and successor to cable TV (Shattuc 2020). Still, the only thing it inherits from TV is the form of content, i.e., TV shows, reality shows, and documentaries. The merchandise streaming peddles is distinctly different from traditional television or cinema: Television sells content, such as an episode of a TV series; however, streaming sells subscription service (Lotz 2022). Streaming as a platform no longer worries about the success of a single show, but about retaining the maximum number of subscribers and attracting new ones.
Netflix has replicated the success of House of Cards several times in the years since, using similar data analysis to invest in popular titles. After collecting, calculating, and applying viewer behavioral data and gaining positive economic results, Netflix has become more focused on the importance of large amounts of user data to its business success and building a user-centric experience on all fronts. This included upgrading its infrastructure. In the pre-streaming era, Netflix stored its initial data on several large Oracle servers with Java front ends in California. However, in 2008, Netflix experienced significant database damage, leading to a three-day suspension of operations. This loss made Netflix executives realize that a reliable, horizontally scalable system was required to protect the long-term storage and efficient use of data (Farrow 2011). Accordingly, Netflix chose AWS, a cloud storage service owned by its competitor Amazon, as its new data center. This data migration has allowed Netflix to retain existing subscribers and has led to an exponential increase in good viewing interactions for users, thus meeting the need for more data production and storage (Amazon Web Services 2016).
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