Digital Video Needs to Adapt to Current Consumer Behavior

Digital Video Needs to Adapt to Current Consumer Behavior

Tell me if this sounds familiar: you’re sitting around with some friends and someone asks the group if they saw [insert television event]. Chances are someone hasn’t, so they search for the clip online and watch it.

There’s a Constant Increase in Digital Video Consumption

This scenario, or ones like it, has played out billions of times. And I literally mean billions. According to comScore, 89 million people in the U.S. watch 1.2 billion online videos per day. This explosion of digital video consumption can be directly correlated to the rise of digital device capabilities. Mobile and tablets are the fastest growing online video consumption devices, expected to account for 37 percent of digital consumption in 2015. By 2016, mobile is predicted to account for half of all online video consumption.

Consumer Demand for Recommendation Engines and Continuous Play

But with this growing digital video usage comes a new type of audience behavior. Just look at Pandora. Pandora allows users to choose their preferred stream of content – where the next song is recommended based on likes and dislikes. On top of recommendations, the songs are played in a continuous steam that eliminates the need for click-to-play. This type of media environment has created a shift in user behavior, making an active audience more passive. Now, users want to lean back to consume content, not lean forward to search for it.

Digital video viewers want that same passive experience when it comes to discovery and personalization. Instead of viewing one video and having that be the end of the experience, viewers are responding to a continuous stream of customized content. IRIS.TV, an in-player video recommendation engine, has found that this type of experience can increase video consumption by 50 percent. The company uses programmatic video delivery technology to provide its clients with relevant streams of video. They implement interactive buttons that capture real-time feedback to adapt to a user’s preferences. It’s through this type of user experience that IRIS.TV’s clients have increased viewer consumption by 50 percent. They also found that when users are presented with a stream of recommended videos, they tend to watch four to five times as much content than typically observed from loyal viewers.

Why Adapting is Necessary

But why is this so effective? Well, the digital video ecosystem is incredibly vast. How vast is actually difficult to truly comprehend. According to Cisco, it would take one person over 5 million years to watch the amount of videos that will cross global IP networks each month in 2018; every second, a million minutes of video content will cross the network by 2018. These types of numbers are unfathomable, and present an equally unfathomable amount of options for viewers. How can a viewer possibly find the content they want to watch? They can’t. That’s why there needs to be a recommendation engine in place. Viewers don’t want to be tasked with searching for their desired content; it needs to be brought to them. That’s why recommendation is key.

And the same goes for continuous play. Imagine if your television program just ended, with no content queued up. That is such a ridiculous notion, yet for so long it’s been the norm in digital video. Pandora revolutionized online music by supplying a constant stream of songs; something that hasn’t transferred to digital video until now. By combining recommendation engines with continuous content streams, viewers are getting that passive television experience they crave, but with a personalized twist that is crucial to the ever-growing digital video library.

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