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  1. Datatainment - Data collection from viewers
  2. Datatainment - The customization of the TV experience

Innovation is always unexpected. Most often, it stems from an expected point of origin. Let us consider television for example: social interaction is one of its main features since its creation, more than fifty years ago. This screen was conceived as an entertainment device but also as something that would gather up people in a single room for a friendly interaction moment.

However, the evolution of the digital environment over the last few years was a complete game-changer. Thanks to newly developed tools and devices, such as smartphones, we acquire new senses. These senses allow us to multiply ourselves in time and space. This is how the concept of the Augmented Man was born. In just a few years, these augmented men have moved from a society of information to a society of recommendation. These technological and social transformations could very well alter the entire ecosystem.


1Datatainment – From emotion to customization

The TV programs conceived to develop and improve were the most effective at catching and keeping the audience’s interest. Today, the best programs are those that are massively recommended by people to their family or friends. However, a recent survey from Nielsen shows that web users have greater trust in their relatives (83%) regarding the choice of a TV program and that a growing number of them tend to change channel in order to follow a show recommended by their friends on social networks. This shows the importance for TV channels or shows to expand their influence beyond the television screen by understanding viewer habits and offering new experiences within the digital sphere to retain their audience.

Contrary to popular belief, the so-called «second screen» does not isolate people and is gaining importance; it appears to encourage the symbiosis between people and their environment. In order to progress, TV channels must consider the digital world beyond the traditional vision of a web replay platform.

The idea is for TV channels to become major actors of the Social TV. It is thus crucial to observe understand new habits, which implies understanding the Data continuously generated by interactions and conversations. This «Big Data» is so large that it becomes difficult to process and only a methodical approach can yield added value from this entanglement of data.

Datatainment: covering an entire process from data collection to customized TV experience, it became the greatest challenge of the decade for TV channels. Whichever masters it will remain ahead of its competitors.

In this context, Big data have become a medium for a greater understanding and monetization of all the digital activities of consumers. The advent of multitasking created the dynamics for multi-device use, and the propensity to produce a constant amount of storable and readable data continues to grow: it is estimated that the current amount of data produced doubles every eighteen to twenty-four months.

French households have an average of 6.3 screens per household and spend about 30 hours on the Internet every month. Such a rise of new media can be comprehended through pervasive logic, which presumes that information aligns on the mobility of individuals: 7.7% of total internet browsing is now done from mobile phones or tablets. Each day, over 2.5 trillion data is generated: 90% of the world’s data was collected over the last two years through climate sensors, social networks messaging, shared content, GPS signals, bank transactions, etc. The issue of data and of their processing must be envisioned through the prism of mobility. Every emitted and received signal is conceived as a reading key defined by an instant and a location. The resulting scheme is unavoidably fragmented, frozen on a specific vision that must constantly be updated. Awareness of such a matter creates a peculiar relation with innovation, conditioned most especially by the recognition of its short-lived quality. The second advantage of this mobility lies in the notion of a systemic redundancy: any fixity imposes a static model based on non-updated observation.

On the contrary, considering data as an analytical material introduces a predictive dimension, through the clustering of data and the identification of repetitions. As for the topic at hand, namely Social TV, the notion of mobility is expressed in terms of expression channels through which current television practices can now be analyzed by studying a combination of reporting and generated information sources. Taking a glance at LiveTweets for each broadcast leads to the conclusion that the logic of motion exists even in their display methods, which are constantly re-actualized according to a specific moment, sometimes redundantly and inevitably obsolete because it only reproduces an outdated scheme.


The main pitfall of this trend is to conceive Big Data as a way to produce one-dimensional data by assimilating the description of this activity (especially the way it is relayed via social networks) as the absolute guarantee of its effectiveness. However, the Social TV system relies primarily on a culture of demonstration and declaration. Conventional watching practices for TV programs are limited to recording feedback flows reflecting only the electrical activity of the machine without taking into consideration the state of mind of the audience. Thus, confining this issue to the mere apprehension of unicentered Big Data -that is to say, constrained to a single channel analysis field- leads to a limited collection of quantitative information which is completely irrelevant for data treatment.

The issue at stake is to think beyond the Big Data logic toward Smart Data by taking into consideration cross-qualitative data analysis, which can be achieved by re-centering the focus on the content and not the channel it passes by. It then becomes possible to conceive any given program using an exhaustive viewpoint not solely based on an outdated model of audience metrics or supposed viewer involvement. Conceiving “social viewers” in the most precise way amounts to picturing them within the perpetual change.

From this point, it can be noted that the real stake in this information race emerges from two predominant ideas: an exit from collective models and an adaptation of the content to actual needs. There is a switch from an imposed content logic to that of a double co-creation, that would be both explicit and information (because it combines declarative and “captured” data). Viewers progressively become the concept designers, producers, and actors of their own television, which is generated by collecting the information they produce and the choices they make. The strongly-valued staging of the self conditions the progressive emergence of new television trends, just like the Stevie TV application: the program is created using the social data of web users, making it unique storytelling material that is both egocentric and reflective.

As a consequence, data is considered according to a double logic: it is at the same time a capture point of a past activity but mostly a predictive guide, which aims to rationalize creative processes. The worth of datatainment lies in the adaptability potential it offers, which goes much further beyond the algorithmic confinement of engines such as Netflix or Amazon.

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