Academy blog


There are some things that can only be understood by breaking them into smaller parts. This is certainly true for audience. For an online video service to understand their audience and predict changes in users’ behavior, they need plenty of user data but also plenty of ways to segment that data.

With the big OTTs making massive investments in original content in the coming years-—a trend that will no doubt be followed by traditional broadcasters jumping into the online game—segmentation will be a critical tool of decision makers as they fine tune their offerings for maximal success.

Detailed segmentation of the fundamentals like user gender, age, country and city or prefered device has been around for some time. Combined with recent advances in segmentation based on users’ consumption, these metrics can be leveraged to predict and prevent churn, improve marketing campaigns and user engagement, predict shifts in demographics of an OTTs user base, and more.

Let’s consider a few use cases made possible with YOUBORA:

  • Compare the viewing habits of users who share their account with those who don’t as it relates to churn prediction in order to identify if sharers are more or less loyal. With loyal users being an OTT’s goal, knowing which users tends to stick around would allow them to be targeting in campaigns and with special offers.
  • Compare users based on their sign-up date, linking it with specific marketing (such as free-trial promotions) and content to determine variations in retention rates among the different groups. This offers marketing departments actionable insights into what has worked and what hasn’t. Similar marketing campaigns in different countries could be compared to determine what effect market maturation, among other factors, might be having on results.
  • Compare the degree of growth (exponential, low, stable) for different segments of viewers, like bingers versus light users. This is a great way for an OTT to see where its user base is headed, and adjust content offerings accordingly.
  • A marketing department could look at what percentage of accounts in a given country are being shared among several devices on weekends—which would suggest sharing—in order to calibrate pricing packages and product positioning.
  • Segmenting by user age could reveal that younger users prefer, for example, shorter videos. Going further, those younger users preferring short videos could be compared against the minority that prefer longer videos. This could be crossed against buffer times for both groups, thereby confirming (or eliminating) a possible technical explanation for video length preferences. In any case, it would be actionable insights for content and marketing departments.

Having the data on these segmentation examples informs and improves content decisions. If buffer times are the culprit, a quality assurance team can work to limit it. If short videos are prefered based on age alone, programing and marketing campaigns could be adjusted accordingly with an uptick of youth-oriented short programing and longer form productions for more older viewers.

In the end, the choices an OTT makes are entirely dependent on their specific needs and strategies. But in making those decisions they would be flying blind without the power to highly segment their users across multiple parameters, run A/B or multivariate testing, and set goals based on segments of users.

Research & Editorial Team on January 18th 2018

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