Today’s data-driven video players are more than just a screen and a Play button. Here’s how they can anticipate viewer demand, keeping people watching longer.
Here are three ways today’s advanced digital video players will make it happen:
Data is the engine that drives any advanced video player platform. By analyzing data, you can learn about everything from viewer engagement across devices to the health of video streams and metrics on ads served. To make the most of data, you need a 360-degree view of video performance and the ability to collate multiple analytics sources. And, ultimately, you should be able to quickly take action based on data-driven insights.
For example, side-by-side access to audience and quality-of-service (QoS) metrics helps you monitor video performance and viewing patterns and respond in real time to any signs of poor stream quality or viewer drop-off during a live broadcast.
Also important: integrating the performance of multiple players across your network or website. In doing so you’ll have detail on how players may perform differently across various pages or formats. A/B tests can be run on a variety of player elements, such as autoplay vs. non-autoplay, player skin type, or the location of the player on a page, to see how each impacts video activity and revenue. Most importantly, pulling performance data from all of the players into one centralized dashboard with a video-centric view helps you make faster and more fully informed business decisions instead of siloed ones.
Today, any video player has to provide a rich set of data that can help build a deeper relationship between content providers and audiences. Keep viewers engaged with your site (and not a competitor’s) by offering relevant content in an easy flow that keeps them watching “just one more” video.
The traditional approach to helping viewers find content was to offer recommendations based on editorial content selections, such as most popular videos by region, through a manual fixed playlist. Then, basic discovery engines came along to recommend videos based on simple content-based selections: If a viewer watched a tennis video, offer them tennis videos or videos from closely-related topics. Smarter discovery engines today make one-to-one recommendations, offering up videos that a viewer is likely to enjoy watching based on what other viewers with similar consumption patterns actually watched, such as travel or news videos, helping the viewer explore the
content library in new ways.
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