As machine learning is on its way to becoming a game changer in the global economy, we expect the media sector to accelerate its investment in machine learning solutions to become part of this technological disruption. Globally, it is already touching the media sector across its entire value chain—from content creation to content consumption to revenue generation.
Machine learning is already being actively used for targeted content generation—case in point being the success of “House of Cards”, which was extensively driven by machine learning and data science. In the Middle East, every year the Ramadan grid sees the production of over 30 new series, predominantly Arabic, as the programming reaches a peak of around 80% scripted content vs. less than 50% the rest of the year.
Considering the high competition for eyeballs between the main free-to-air channels, coupled with the increased monetization potential during this period (average ad length increases by over 30%, while the value of 30-second time slots more than doubles depending on the window during peak time), it is critical for channels and studios to get it right. Building on the “House of Cards” experience and considering the relatively standardized format of series and audiences, there is a strong case to use machine learning to support the production of Arabic dramas and comedies for the holy month.
Another early stage application of machine learning to media has been the creation of trailers. Shorter, more self-contained, and abiding to general rules (length, voiceover, etc), trailers are obvious candidates for “creativity automation”.
In 2016, 20th Century Fox released the trailer for “Morgan”, a horror movie developed using machine learning. The system was “trained” on scenes from over 100 horror movies and recommended 10 moments for the trailer. While the movie was a relative box office miss, the trailer was a successful proof of concept. Applying this concept to the way broadcasters currently operate in the Middle East, this is an interesting potential for strategic cost optimization.
Currently, broadcasters tend to still insource a large number of creative services staff dealing with on-screen graphic, visuals and fillers, including trailers. Taking the lessons learnt from trailer creation via machine learning, a creative process that used to take weeks could be reduced to less than 24 hours. The resources could then be reallocated to more value-adding tasks, including core content creation or programming.
Precisely, programming is another area where broadcasters should greatly benefit from machine learning. In the same way that Netflix uses machine learning and algorithms to push content recommendations to customers, or Spotify’s partnership with Accuweather aims to adapt music recommendations to local weather, we see the linear TV grid becoming much more dynamic and based on machine learning in the near future. Broadcasters have always tried to link their content to current events—a case in point is the release this Ramadan of MBC’s “Black Crow”. Now, the opportunity is to tailor dynamically the content on a daily basis, following a number of factors including current events or weather.
Machine learning could have its most potent application for regional broadcasters in the optimization of their ad inventory management. What is otherwise known as traffic management has been more of an art than a science for many Middle East broadcasters, with many of the activities and decisions left to manual interventions and excel spreadsheets. Yet, for an FTA broadcaster, effective traffic management can have a very significant impact on yield maximization.
Introducing machine learning in ad management would mean incorporating years of data and dozens of variables ranging from time of day, content type (potentially down to “moments”), social sentiment or financial positions with the media buying units. Extending the concept beyond ad management, machine learning and analytics could help broadcasters more accurately assess the real value of their ad slots for brands and media buying units.
Already, many broadcasters are using social sentiment analysis (e.g. tweets on specific programs) to gather feedback on the content they air. Going forward, with initiatives such as the private sector’s open data that some GCC governments are contemplating, you could think of brands in the region opening up their inventory data to broadcasters or media buying units to assess near real time the impact of ad-on buying behaviors. In a region where audience measurement remains a key pain point, the dialogue could shift from audience share to return on investment.
Overall, in a regional sector that has been battling with commercial viability issues for years, machine learning could play a critical role to help broadcasters both optimize their ad revenues and streamline their costs. Globally, machine learning is now moving out of the shadows of ideas and vision to the real world of application, usage and revenue generation, with some first successful use cases in media. Hopefully, the regional media industry will catch this boat in time.
Emmanuel Durou is Partner and Technology, Media and Telecommunications Leader at Deloitte Middle East.