AI is truly a wonderous thing. From providing a baseline for advertising operation to making sure Google's engines return the right content, AI is learning to serve us, humans, better. It's the same within video content and why consumers are consuming more and more video. Over 90% of internet traffic is video, and it's only going to grow. But, all of these companies have to make money somehow.
So, to break it down simply how does AI increase monetization for video?
There are three primary ways:
Content recommendations are one way to increase video monetization. If you can keep a user on your content longer, you'll be able to increase the possibilities for serving ads and for further monetization. Netflix, for example, has an AI-based recommendation engine that recommends what you should watch next. The same is true for YouTube or any other streaming video site out there. Unless the content creators are paying not to be part of those recommendation engines, they're usually present and making sure users get exactly what they want when people see an ad on these services - which makes sense because right now, AI is the best way to understand what users want.
Netflix uses machine learning and algorithms to ensure that its subscribers are kept happy. Since Netflix operates on a video on demand (VOD), it makes money by keeping its users happy and continually testing what types of content that each individual person engages with.
It's not just about understanding user intent and providing them with more of what they want, but also about making sure those videos are as engaging as possible. This is where machine learning comes in, which can help make sure that ads (or other video content) are placed in front of viewers in the right context, at the right time, and with the right frequency.
This is important because if you're not providing a good user experience, people will simply go elsewhere. And as more and more services enter the market - each vying for our attention - it's becoming increasingly difficult to keep users engaged without AI.
That's why it's such a surprise that mid-roll ads are still so prevalent within video advertising. Over two-thirds of all online video ads have these types of ads, which interrupt the contint that people are watching. They do have the highest completion rate (98%) compared to pre-roll which has a completion rate in the 70% range. This is because only 30% of users are actually watching them.
But this is where AI can help. With machine learning, we can understand what makes a good ad and what doesn't. We can also serve contextual ads through machine learning algorithms that are better related and integrated into the content and more likely to keep a user watching your videos for longer, therefore increasing your monetization value.
By adding On-stream ads to your videos, you can increase the ad space available on your videos. Usually, by increasing ad-space, you run the risk of annoying your users and damaging your view-through times, but with On-Stream, the ads appear on or around your video, not interrupting it, and providing your user an integrated ad and content viewing experience. Get started today with one line of code!
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