Video Programming Insights: How Machine Learning Enables Creators to Thrive in a Shifting Digital Economy
Last week, I participated in a panel for the DTLA Tech Series, entitled “Video Programming Strategies to Thrive in an Evolving Digital Economy.” The event, based in Los Angeles, gave me the opportunity to discuss the nuanced and continuous evolutions in delivering video content with a variety of other professionals in the industry. Some of my co-panelists included Little Monster Media’s Matt Gielen, Spotify’s Lauren Jarvis and Fandango/NBCUniversal’s James Johenning, along with moderator James McCabe from ChallengeMaker.
Our focus for the evening: The practice of data-driven decision making to inform content creation, distribution, discovery, and monetization.
For IRIS.TV, data’s intrinsic to our business, and the value we deliver to clients. The information we passively collect during consumer video viewing experiences informs our machine learning, and helps populate more relevant videos going forward. Data creates our intelligent video distribution models, helps clients monetize their video archives, and helps set that content up for increased discovery within the publisher’s’ own platform.
The data used to keep our machine learning running smoothly also helps make tweaks in the process when we notice certain trends. Has an old video revealed a great amount of video lift because it was tagged well? Has a newer and relevant piece of content failed to resonate, in part because of a lack of meaningful tags applied on the backend? Constant communication with clients around this data and how it empowers better programming and improves content strategy is paramount to making the best decisions for growth.
The opportunities presented by artificial intelligence and machine learning technologies are making it possible for publishers to be more competitive while also dialing in the content that their audiences care about most. Content discovery is quickly growing into a must-have feature for any web experience — especially video. With the pure volume of options out there for viewers, recommendations (whether personally or AI-driven) are the currency that drives consumption. For IRIS.TV, artificial intelligence and machine learning allows us to deliver those recommendations swiftly and in the background, without disrupting any portion of the viewing experience.
Recommendation models like this are also the future of media consumption. In the digital age, the collection of available content cannot shrink. And that catalogue, bound to balloon even further in the coming years, has little chance at being delivered efficiently without AI. The cost of production and distribution can never be absolute zero. Quality content costs time and money to create and disseminate.
The future of media consumption will belong to the creators that are able to put a large collection of content in front of their viewers, provide them the tools to find what’s most relevant to their interests, and then walk away. Viewers and machine learning can steer the ship. But that journey will only be as good as the map — aka, the technology that delivers the content to viewers.