02.11.2019 | Ausgabe 11/2019

Mining Viewer Data For New Revenue Streams

From content repurposing to data analytics, broadcasters are quickly becoming adept at leveraging (and monetizing) viewer data in order to generate new revenue and keep audiences engaged. As TV becomes ever more immersive, the number of data points that can be collected has increased at an exponential rate. Studying content consumption data allows broadcasters to gain a unique insight into who its viewers are and how best to serve them.Talking lessons from their new competition (Amazon, Google, Facebook, et al) the broadcast industry, to varying degrees, has begun to deploy software-centric systems that measure audiences’ preferences and identify how to create content to support that usage.

This is especially true online, as viewers are harder to wrangle on their cellphones and portable digital devices.It’s now clear that today’s content providers have a treasure trove of data at their fingertips. By using it to generate insights into consumer purchasing behavior, and coupling these insights with an understanding of emerging macro trends, they can provide better service to customers—on all of the distribution platforms they use.

There is also the intriguing prospect of being able to infer much about a household’s composition purely from a top-level analysis of its viewing patterns. Managing that data and extracting information from it is going to necessitate leveraging some of the recent advancements in machine learning. The result will be that broadcasters can better understand their viewers, improve existing business processes, and open up new revenue sources such as targeted advertising. That’s why broadcasters are implementing new artificial intelligence (AI) technology into their production facilities that help streamline the process of ingesting, analyzing, managing and distributing content and related metadata. This improves productivity and goes a long way towards developing new types of revenue generating services.

Indeed, AI offers the opportunity to create and deliver more personalized video experiences, both for broadcast and targeted ad delivery to individuals and is having a significant impact on new types of video workflows.For broadcasters and production facilities specifically, the data monetization process often starts with on-premise media asset management (MAM) systems and continues into the cloud to enable collaborative workflows. The cloud is also instrumental in help to make more content for a wide variety of platforms. An AI-optimized MAM system offers users a better way to search and retrieve content across a vast archive of files using metadata.

This media industry-centric combination offers advanced search and retrieval tools tightly coupled with specialized AI algorithms included. Together they help to significantly enhance the creation, management, analysis, distribution and monetization of existing content across a vast archive of files.The ability to discover very specific content from an archive quickly – whether it was captured several years ago or two minutes ago – allows rights holders to tell more dynamic stories, either through the broadcaster televising a live event, or through their own digital distribution initiatives (including social, streaming, syndication or complementary channels.)

A live-event broadcast tells a linear story from beginning to end, but the goal now is to extend the story beyond the linear. Digital media channels allow rights holders or sports properties to broaden their reach – and their monetization opportunities – by going enhancing the story with alternative yet related content. For example, lots of things happen on a golf course or tennis court that are never shown in the main broadcast. Sometimes those moments are more compelling than the actual broadcast. And, when edited together, they become part of a larger developing story that could appeal to a broad audience of both hard-core fans and casual viewers.Whatever the case, rights holders now want to make use of all their content and take advantage of every possible opportunity to connect with viewers and monetize assets during and after the broadcast. To do it, they need an efficient process.

That efficiency comes from the ability to aggregate content in an active archive in the cloud, with rich metadata for accurate search. In combination with a cloud-based portal that enables easy download/distribution, such a solution provides invaluable management and monetization options with a very light footprint.In the U.S., many are promoting the emerging ATSC 3.0 broadcast standard as a new two-way wireless connection into a consumer’s home. Heretofore, terrestrial broadcasters haven’t had that type of addressable data. With it, they can deduce – from viewing figures alone – the composition of a household audience. [For example if a number of children’s programs have been watched it’s likely that there are children in the home.]

For advertisers, that’s powerful information that they are more than willing to pay for.The new TV standard will also facilitate new technology partnerships to efficiently aggregate and monetize this data. As one example, Sinclair Broadcast Group is working with Imagine Communications on a new, next-generation advertising management software platform that helps broadcasters exploit new monetization opportunities by using ATSC 3.0 digital television technology. Sinclair, the nation’s largest TV group in terms of stations, is now playing a critical role in the development of Imagine’s business process systems for traffic, ad sales and data analytics that allow for unit- and impression-based buys. Look for similar broadcast equipment vendor collaborations going forward.

In an industry facing cost pressures from many different sides, data monetization must be a key consideration when it comes to any deployment of new technologies. Fortunately, the correct use of TV data analytics holds out the promise not only of paying for itself quickly, but also facilitating the creation of new revenue streams.


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