Analyzing Viewer Data to Improve OTT Monetization Strategies

It starts when you gather detailed viewer data using analytics tools to track things like what content your viewers watch, how long they stream, which devices they use, when they click away, and what time they're doing all this.

Look for patterns. You might have users who prefer binge-watching dramas on smart TVs and others that are constantly looking at short clips on their phones. Collect demographic data, like age and location, as well as the behavioral data about genres watched or search terms used. This lets you build a solid foundation for monetization strategies.

Next, divide your viewers into segments based on their data. Use this to analyze viewing patterns to identify potential high-value segments, like frequent streamers who might be willing to pay for premium plans to avoid ads or access better content. A segment like “urban adults with limited time for TV” could be targeted with ad-supported plans, for instance, while “casual viewers who watch constantly" are likely more interested in free, ad-heavy tiers.

The goal is to maximize revenue from each group, understanding that each group comes with different revenue potential. You simply can't get as much from some groups as others: but that doesn't mean you should ignore them entirely!

Looking at your viewer data also tells you where your ads will earn the most. Start by identifying your peak viewing times and schedule high-value ads then. Use data on ad engagement, such as skip rates, to further refine what ad types you're going to show to which groups, and when.

For example, if short, 15-second ads perform better with teens, prioritize those over longer ones. Focus on segments with high completion rates, like “sports fans during live streams,” to charge advertisers premium rates for those slots.

Using viewer data can also allow you to design subscription plans that increase your sign-ups. Start by looking at what content best drives those long viewing sessions, then bundle these into premium tiers.

Test pricing by tracking churn rates. Churn rates tell you how many users cancel subscriptions, so if you find that parents of younger children tend to drop off quickly after a price hike, try adjusting the cost or adding perks.

Predictive analytics can actually forecast viewer actions once there's enough data to go off of. Use your platform tools to analyze past behavior, and then feed this to your predictive models to figure out the best time and place to target viewers in different segments with, for example, upsell prompts during login.