Leveraging AI For “Any Time, Anywhere, Any Device” Success
Faced with increasingly uncertain markets, fierce digital competitors and the ever-growing amounts of data generated by consumers every day, broadcasters and media organizations are relying on ever-smarter tools and automated workflows to stay competitive.
Data science, machine learning, and AI platforms offer a significant advantage as they allow users to understand their customers on a granular level, deliver personalized content, captivating user experiences and storytelling, to help develop and grow new business models. If the goal is to deliver unique, differentiated, one-on-one experiences, AI-assisted platforms are the key to accomplishing that.
As content consumption behaviors are becoming increasingly complex and evolving more rapidly than ever, media companies are challenged with reducing operating costs and simultaneously generating more revenue from delivering content. Companies, in turn, are tailoring their offerings and business models to revolve around personal preferences, leveraging data and usage patterns to pitch their products not at audiences of billions, but at billions of individuals.
Recommendation engines have been widely used to predict what kind of information or content customers would be interested in. Companies can combine structured and unstructured data and machine learning methods to match people and content, thus improving the relevance of content recommendations and efficiency of content distribution.
When it comes to AI and data science implementation, media companies have many of the same challenges as other industries: requiring effective data engineering, data quality and stewardship efforts, attention to data governance and data privacy, and usage of the most appropriate machine learning models to solve a given problem.
Data has always played an important role in the media and entertainment industry. TV and radio programming lives and dies based on ratings generated from viewers and listeners. Long before newspapers were monitoring clicks and social media shares, they were carefully tracking subscriptions and analyzing the demographics of their readers. With leading tech media players now venturing more and more into AI-based interactive and smart content, we’re likely to see a shift from simpler content recommendation systems to an entire AI-driven personalized content experience.
The possibility of combining data from different sources in one place can allow companies to look at their customers as a whole and deliver unique, hyper-targeted ads or marketing strategies. In TV and advertising, this includes the concept of addressability: the ability to interact with consumers based on what their specific choices reveal about their interests and preferences. Therefore, thanks to AI and ML, media companies can predict churn rates more accurately, place advertising at the right time and in the right place, and have more appropriate, personalized offers to increase conversion.
In addition, some streaming platforms and leading film studios are currently experimenting with ML-based personalization of movie trailers that emphasizes specific elements that they know a given target audience would like – delivered on the platform that they use most frequently. Last November, movie studio 20th Century Fox used AI to detect objects and scenes within a trailer and then predict which “micro-segment” of an audience would find the film most appealing.
In the constantly evolving media and entertainment sector, looking back at consumers’ past activity often isn’t a good indication of what they will do next. Instead, real-time prediction based on current trends and behaviors from all data sources is key. Predictive modelling helps aid media companies not just by allowing them to react to consumers in real time, but also to anticipate their behavior, influencing long-term investments – for example, what kinds of movies in which consumer micro-segments will be popular two years from now. In addition, companies can make predictions about which customers are more likely to view a given type of content, and what device they will be using when viewing it.The best example of the impact of AI in media is OTT services such as Disney+, Netflix, Hulu and Amazon Prime. These platforms have not only transformed the way people consume film and television through on-demand programming with significantly fewer advertisements, but they have transformed the way that programs are marketed. Every single one of their customers are providing a steady stream of data that paints a sophisticated portrait of their preferences and viewing habits.
Every time somebody logs into their Netflix account they are presented with a personalized set of programs based on their viewing history. The platform’s algorithms use a number of different data points to predict what a person will like. Not only will it identify patterns in your viewing history (that you have sought out multiple sports documentaries or French-language films, for instance), but it will recommend programs that that others with similar viewing histories have watched. The algorithm is constantly improving based on the data fed to it by millions of subscribers. Netflix has similarly employed AI to optimize the way that movies and shows are presented to users. Netflix tests different thumbnail displays for programs to understand which ones are most likely to attract viewers3. Again, pairing this with other viewership data may lead the platform to conclude that certain designs work better on certain viewers.
There are now more customer data sources and analytics tools available than ever before to the media industry, and that’s good news for everyone involved. The formula for “Any Time, Anywhere, Any Device” success is using ML and AI to turn massive amounts of data into accurate and comprehensive insights on your customer base. Collecting, analyzing and acting on usage data is the key.