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Video: How Targeted Recommendations Drive OTT Engagement

In this excerpt from their panel at Streaming Media West 2016, Richard Au of Amazon and Anthony Layser of Dailymotion discuss the growing importance of recommendation algorithms and customized, user-specific discovery in the personalized enhancement of the OTT video user experience, as well as the growth of brands and the increase in OTT content engagement beyond what any degree of individual human content curation could achieve.

Learn more about OTT engagement at Streaming Media East.

Read the complete transcript:

Richard Au, Amazon: Amazon, as a company, has been built on generating really targeted recommendations based off of not only our customers' behaviors, but also what other customers are doing who are looking for very similar or the same things. I'm sure you have gone to the Amazon website to go buy something, and you'll see a certain recommendation--components of our website where customers who bought this also bought that.

We do a lot of quite sophisticated personalization based on all the data we see from our customers in terms of their behaviors. When they stream content, they stream a particular type of content during, let's say, the evening versus during the morning. We draw inferences from that. Then we start to figure out what content makes sense to surface to that customer based on their previous viewing history, based on what others are doing, based on the time of day that we see it, based on the type of content that they're looking at at that time.

It goes to the core of what we feel is going to be innovation around what the customer experiences, such that you're not just turning something on and kind of have a lean-back experience of a static kind of grid or guide. It's increasingly personalized to what it is that you want to watch and your family wants to watch.

Anthony Layser, Dailymotion: One of the most drastically changing things that we're seeing is customized, user-specific discovery being a much more important piece. Curation just can't keep up, like an individual human curation can't keep up with customized, user-specific discovery based on algorithms.

When we do A/B testing around implementing certain tools for our partner managers to curate certain series or certain pieces of content, we do want to drive to specific types of content. But at the end of the day, if you can customize what's being fed based on the user history, you end up with more engagement, and that is the key KPI for a platform like ours.

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