Lynn Spigel, Max Dawson, and Amanda Lotz are all interested in the 21st Century’s transitioning television to a new media space that emphasizes the ability of the individual to control what he watches, with niche markets developing that transcend both original network structures and even geographical barriers as more televised content becomes internationally distributed and targeted. An interesting factor of all of this is that with digital distribution expanding, DVR’s, web streaming, and On-Demand watching are all becoming more prevalent. Part of what is driving this is that user viewing behavior and preferences are much easier to track and target for advertisers and creators alike, as the choice to intentionally seek out specific content is a much different one than the traditional “tuning in to what was on” of the time of the big networks, and so creators and distributors are therefore encouraged to respond to what certain segments are interested in. Advanced demographic research is now available as the identities of those streaming on Hulu and making on-demand orders are much more accessible than what is achieved with the Nielsen family system.
My query is whether it will one day be possible to leverage this data to begin designing content that is truly ideal for certain demographics? If we can know exactly what the 16 year old males from South Florida, who from their internet history likely smoke marijuana, are interested in watching, will the perfect show for that demo one day be possible? It seems that if Spigel, Dawson, and Lotz are right about this trend being the future of the televised medium, then more and more data mining will be possible as internet and on-demand becomes the most dominant distributed form. Imagine the possibilities when the mass of collected demographic information has collected over enough years that definitive trends can be identified and experimental models developed depending on what factors are trying to be maximized.
Ryan Kavanuagh has already had some limited success leveraging data for the creation of fiction with his film production and acquisition studio Relativity Media, which uses statistics and modeling to attempt to predict which genre pictures featuring which actors released on which weekends will generate the most bang for their buck. What success he has had has come from finding a way to gain the most profit without needing to necessarily innovate within his media’s form, instead just knowing that a competent horror picture with an accessible hook will definitely yield a baseline profitability Kavanaugh currently assigns his own self-derived weights to variables, and the effectiveness of his intuitions is up to debate (the company made a series of serious missteps in 2010 and 2011). But that limitation is probably related more to a lack of competitors helping refine the method.
This is additionally interesting in the context of modern supercomputing. The fact is that the amount of raw processing power the average person can cheaply access has grown tremendously and still grows every year. The combination of tons of data and tons of processing could possibly allow us to design advanced simulations that can use factors such as which actors are involved and what the genre hook is to analyze what the chance of the show’s success is. Another potential metric would be to identify what the ceiling or floor of its appeal is, determining that it has a decent shot at being a moderate success, or no shot at being a minor success but enough of a chance at becoming a wide-spread sensation that it is still worth the investment.
Perhaps no amount of data can guide us to the ultimate iteration of different genre offerings. But hopefully the trends behind who makes what shows popular will be researched and acted upon in ways that inform the nature of reception and appeal, and can give us a greater insight in to how certain storytelling and televised forms contain their appeal. As the industry becomes more mechanized and individuated and profit from well-tailored offerings is easier and easier to achieve, these kinds of statistical tools will allow companies and even the average creator to know so much more about how their product really relates to the audiences that receives them.