How Disney Streaming Approaches OTT Personalization
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Dave Lankford: I think there is a value to the human element and being able to curate the content. But how do you scale that to a global audience? How do you scale that to the wide breadth of audiences across multiple ages? And that's where we really lean into personalization and machine learning, as well as experimentation. And on the personalization side, we start developing that consumer profile after about 30 seconds of viewing. Obviously we may have some information as you come in, in terms of the path in which you purchased, the country in which you purchased, the device on which you purchased.
But at the end of the day, we start looking at that signal of what are you watching. and there are a couple of different areas that we do look at. Obviously, there's what you're presenting on that landing screen on that hom experience. That's an important surface for us. What is the content that's presented, what is the order in which it's presented, both top to bottom and left to right. Those are very important vectors, especially depending on the audience. If it's a left-to-right audience in most Western worlds, that top-left corner starts to be part of that experience. If you're looking at right-to-left in Arabic-speaking or Hebrew-speaking areas, you're going to be looking in a different direction. So we understand those patterns of how people are looking at our content and making sure that we're surfacing the most relevant content up top.
But curation comes in play, especially when you have content that you don't have a lot of signal about. There is importance about priming the pump, making sure that you are exposing that content to folks and those stories, because you'll start to see who's clicking on that, who's watching it, who's consuming it all the way through and not. So I do think that you have to understand what you know, and what you don't know, and accept what you don't and find ways around that. And I would say there are other surfaces--the post-play experience, or the up next, when a video is done, what is that next thing you present to them? That's a really important surface for us, as well as if someone's browsing or searching. Sometimes if someone's searching, they're searching for a title, maybe not because that's what they want to watch, but it's like something they want to watch. And being able to use personalization to understand that using knowledge graphs of our content as well, understanding the deep connections from thematic to story, to arcs, to character types, those all are things that we invest in to say, "Let's know more about our content."
I would also say that the context in which you present it--being able to say something's recommended for you or because you watched this--or even other contexts in which you present that is important. The images that you present, the titles that you present, those all provide the way in which people understand, "Why is this being presented to me? And do I agree with that assessment?"
On top of that, it's not a one size fits all. We have a philosophy on my team which is "Yes, And." If you've ever been to improv, and you've heard Del Close, Del Close always said Yes, And." And that why bring that up is, a lot of times when we were first starting, I would hear teams come to present and they would say, "Well, Dave, here's option A, here's option B, here's option C." And where we've gotten to is, "Let's test option A and option B and option C."
We need to have a hypothesis, but in order to personalize and create the best experience, we have to remove our own personal bias as people--or as teams--and say, "Let's experiment across different algos or different contexts or different ways of presenting content so that we're actually letting our consumers speak to us." So again, I think experiment, you can't really talk about personalization and machine learning without talking about experimentation.
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