How are Media Businesses Guiding Eyeballs to Their Streaming Content with So Much to See?

83% of internet users in 2020 will watch digital video, making streaming the most popular outlet for video entertainment in North America, according to eMarketer. Many millions have been spent on an incredible array of original content available on a wide variety of platforms and brand names. Since viewers on many of these platforms control 100% of their viewing experience, how do you reach them with something new?
A recent Cisco report pointed out that it would take a single viewer 5 million years to watch the video that crosses global IP networks each month. This means media businesses are looking hard at the data associated with these programs to find ways to make sure both subscription and advertising-based revenue content is not lost in the sheer volume of available entertainment.
Media businesses know the vital importance and value of metadata in their production workflows, but many are still working on ways to keep useful data intact throughout production cycles as well as upon release into external distribution systems. Much of that content data can improve viewers’ experiences with recommendation engines, relevant advertising associations, search results, and allow easier direct connection to social media discussions.
Whether media businesses are primarily in the live streaming or the live linear business, leveraging machine learning against metadata helps content owners and distributors increase the likelihood of content being discovered and viewed. Zixi’s streaming technology is an important element of a distribution strategy focused on keeping accurate, error-free metadata intact across distribution workflows. Zixi’s five-nines of QoS means a near-perfect viewing experience, but it also means that metadata, closed captioning, multiple language audio tracks, and even cutting-edge tracking, rights, and transaction data, will remain error-free and useful throughout a streaming ecosystem.
Netflix has lead the entertainment industry toward more-specific search sub-genres increasing the likelihood that systems will improve relevant recommendations over time. All entertainment systems are working on recommendation schemas that help their customers wade through the epic quantity of content available, and that means delivery technologies must provide incredibly precise streaming methods to meet ever-evolving data delivery needs. Entertainment content makers will continue to get better at using the automation of machine-driven metadata combined with human curators, and then the IP-based delivery technology must be able to flawlessly keep that data intact as content moves throughout the ecosystem.
As the amount of content, and the number of viewers, continues to change and evolve, business growth will come to those who build workflows that include ever richer data that remains intact, precise, and accessible, throughout its distribution lifecycle.