Utilizing Analytics to Improve OTT User Engagement

Utilizing OTT Analytics to Improve User Engagement

We all know that feeling of scrolling through a streaming app and just finding nothing that grabs us. In OTT terms, that's called low engagement, and it's a silent killer for subscription services. You see, engagement is more than just the amount of time viewers watch. It counts the whole spectrum of interactions they have with you, from likes and shares to playlist creations and repeat visits. High engagement means users are invested and less likely to churn.

Analytics works by taking all the raw data about what viewers are doing and turning it into insight that you can do something with. It tracks metrics like session duration, completion rates, and drop-off points so you're not just guessing what works. Global OTT revenues are projected to hit over $200 billion by 2027, but only those who prioritize data-driven strategies like this are going to be able to claim the lion's share of that.

Key Metrics to Track

Start with the basics, like which titles are hits and which flop. If a show's completion rate is under 50%, users are likely dropping off due to slow pacing or poor subtitles. Then there's behavioral data, like what your users are searching for and the navigation paths they take to get to content they want. If, for example, your users frequently search for "sci-fi series" but mostly abandon the app without watching anything, then you have a good clue that your recommendation engine is off target.

Engagement scores will then give you a more holistic view. Don't forget to get your demographic breakdowns, either. Your younger audiences might be binging on mobiles during the evenings, while families prefer using smart TVs on weekends. With all these pieces of the puzzle on the table, you'll be able to spot the patterns and put the puzzle together to find out how to boost engagement and make money.

Personalization

Let's say someone logs into your OTT app and immediately sees a homepage curated just for them, with recommendations based on their past watches, and even taking into account their mood by inferring from their recent skips. The algorithm could even consider time-of-day preferences. That's personalization powered by analytics, and it's a game-changer that can keep people satisfied and sticking with you.

This is nothing like guesswork. You're doing collaborative filtering and content-based recommendations based on real information, and you're doing A/B testing to find out what previews, images, or algorithm tweaks work best. For smaller OTT players, starting simple with user profiles and a watch history can yield big wins. Just remember that over-personalization feels creepy if it's not handled right. Monitor that feedback loop!

For more help on using analytics and getting the most from your platform, visit us at Lightcast.com today.