This is the sixth and final blog in a series talking about how video streaming data, pulled from various parts of the workflow, can be used to support business goals. This post will focus on increasing subscriber engagement and stickiness.
Do you know how much content your viewers are watching? On what device? At what time of day? If you don’t, then you are missing a critical puzzle of long-term success for your streaming platform: user engagement. Understanding how often your viewers watch, and from where, and on what device, is the fundamental data of your business. Not only can this data help shape advertising, it can also help you determine the long-term viability of your platform. If few users are watching little content, if only a small portion of your subscribers are logging in every day, it may signal troubling times for your business. Thankfully, with access to the data from your streaming workflow, you can take action to increase engagement and stickiness.
The Two Core Values of Engagement and Stickiness: DVU and MVU
Two key metrics, Daily Views per User (DVU) and Monthly Views Per User (MVU), tell you how often your users are returning to your platform to watch content. Industry statistics tell us that users who are more engaged and return more often, are five times more likely to continue paying for the service. In short, a retained user is far more valuable than a newly acquired user!
Measuring these values and using them to experiment with a variety of levers in the platform (i.e., ad placement, subscription tiers, content recommendations, etc.) can help ensure you drive up the level of repeat engagement. Most streaming platforms, for example, get only about 20% of their subscriber base to continually engage. In fact, studies have shown that 80% of users churn after three days of subscribing. Improving those numbers, which will have a demonstrable impact on sustainable revenue, ad impression sales, and ad impression value, involves continual monitoring.
So how do you get users to come back and consume content every day, week, and month? You utilize player and delivery data to build content journeys for different user personas. For example, some users like to binge on content. So you offer them that experience. Other users like the slow burn, such as releasing a single episode each week at the same time (similar to traditional linear television). By looking at the N-day retention of users who perform the playback start event for a series, you can see how the airing date affects their engagement, and can segment and adjust marketing campaigns to re-engage accordingly.
Cohort Analysis: An Example of Using Subscriber Data to Affect Meaningful Improvement in Your Streaming Platform
As defined by Bill Su in his Medium article, cohort analysis is, “…an analytical technique that focuses on analyzing the behavior of a group of users/customers over time, thereby uncovering insights about the experiences of those customers, and what companies can do to better those experiences.”
Capturing data about individual users, for instance, customizing content recommendations, is important but understanding how groups of similar, or dissimilar, users behave is critical to improving the overall experience. For example, employing cohort analysis with data gathered through Datazoom, could provide you a deep understanding of consumer behavior over the first 72 hours (a critical OTT platform timeframe when users generally make the decision to stay or churn; of course, this could be aligned with a free trial time frame as well).
Using this data, you could then make needed changes to the content recommendation engines, platform features, etc. to try and mitigate that early churn. Another example might be to understand how a decline in viewing minutes relates to churn. You could look at users who have a certain percentage of decline over a given time frame. Then you could reference that against churn rates for that group.
Ultimately, you could set system alerts, perhaps automated emails that hit employee inboxes every morning, when multiple users have hit the start of the declination threshold enabling marketers to target specific programs at those users in the hopes of driving their viewing numbers back up.
Although cohort analysis setup can be complicated to ensure its reporting the right insights you need, it is becoming a valuable tool in the OTT operator’s toolbox for preventing churn and increasing subscriber satisfaction.
Keep Them Coming Back For More
Of course, having a rich and popular content library is sure to bring users back to your platform day-after-day. But even if you don’t have a billion-dollar content budget, you can still utilize viewer data to not only understand user receptiveness to your content, but also a host of other behaviors which all relate to how often your users will return to your platform. Keeping them engaged with the content that is most relevant to their behaviors will ensure they return often and, hopefully, bring others with them.
