This is the third blog in a series talking about how video streaming data, pulled from various parts of the workflow, can be used to support business goals. The next post will look at the relationship of streaming data to core business objectives of generating revenue.

There are a number of reasons why this is a critical business goal. First, if your streaming platform is ad-supported, it quite simply means more impressions. Second, though, it illustrates a healthy subscriber base. When your users are consuming more content, it means they perceive value with their monthly subscription payment and are, therefore, less likely to churn. Measuring this is relatively simple as it represents the total time viewed across the entire platform. But that is not as important as understanding how user behavior influences individual viewership numbers.

Which Users Are Watching The Most Content?

In the first blog post in this series, we learned about how data can reveal important information about viewers: geographic region, device type, etc. The results of this analysis can prompt more targeted marketing efforts to generate additional subscribers. But that data can also be a spotlight turned on existing subscribers. By understanding where users are watching the most content, and on what device, can lead to deep behavior analysis which can have long-term benefits to promoting content consumption.

Getting Users to Watch More

So how can you encourage individual subscribers to consume more content? What causes one to binge a series while another to watch it over a few months? Answering these, and other, questions, can help create profiles of users based on behavioral data which can be gleaned from within the video delivery platform. You can then employ these profiles in recommendation engines, or use data to build your own recommendations algorithms, to suggest content to viewers who share similar patterns. For example, you can identify users who watch content with a certain actor, from a particular director, or of a specific genre to recommend other content for them to watch. You can even modify the interface of the platform to reflect a user’s “favorites” category or these recommendations.

In short, you need to find desirable behavior (as it relates to increasing content consumption), identify users that exhibit this behavior, and then nudge other users in that direction.

Here are some other behaviors and actions which might be taken to increase individual user viewer hours:

  • Consider a viewer who watches a live stream of a football game. When it’s over, they often watch the highlights of another game they couldn’t watch. Rather than requiring them to take that action, you can automatically begin playback of those highlights for those users.
  • For users that don’t binge a series, you can send them a push notification (through the app or SMS) or an email when the new episode is available.
  • When users often watch a variety of content, perhaps sampling an episode of a lot of different series, you can email them a list of recommended TV shows that they haven’t yet sampled but which might be relevant to their likes.
  • You can also analyze which users read the synopsis before engaging with a piece of content and use that piece of content metadata to market to them in the future.

The Impact of Consumption on the Business

Understanding how much viewers are watching is critical to the business. The data that you pull from CDN logs and the player will reveal deep insight into viewer behavior that can have a meaningful impact on content acquisition decisions, ad placement, and even what advertising to display. Without this understanding, you are flying blind and just delivering content. You need to provide value to the viewer. Just how much value, such as simply providing a library vs. providing a library that is full of recommended content, largely depends on how deeply you are digging into viewer behaviors and consumption habits.

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