编辑策展vs. Automation: What’s the Most Successful Way to Increase Engagement?
在推动流媒体应用的内容参与度方面，哪个更有效:是编辑选择的一行内容，还是算法选择的一行内容? 这是个刁钻的问题. 根据我们的经验，提高用户粘性的答案不是选择其中之一，而是两者兼而有之! 您需要编辑和数据科学协同工作，以最可能吸引个人消费者的方式选择和安排给定行中的内容.
Successful though this approach may be, you may face a battle to make it happen. 数据科学团队在 24i 在个性化方面与许多流媒体服务合作过，它们似乎都在走同一条路. Editorial and data science teams start off hating each other. 编辑团队认为，推荐引擎不可能理解人类对内容进行分类的细微差别. 数据团队认为，人类不可能选择对流媒体服务的所有用户都具有同等吸引力的单一内容. 他们都是对的.
对于内容专家来说，策划视频点播和直播内容库，并以一种有吸引力的方式向每个流媒体服务的独特受众呈现这些内容，一直是——而且将永远是——一个重要的角色. Editorial teams have a great sense for which upcoming shows will be hot. 为了获得最大的版权投资回报，确保报头内容得到推广也是一种强大的商业动机. 如果他们对 权力的游戏, there’s a temptation to assume every viewer will be too. But what if you layer data science on top of those editorial decisions?
Making the Hero Banner Work Smarter for You
让我们从顶部开始. Your hero banner is the most prominent piece of on-screen real estate in each app. 它通常是你想要呈现给用户的关键内容的旋转木马，例如你投入大量资金的独家电视连续剧或体育特许经营权. So, it makes sense to have your editorial team choose what appears in this location. 但如果你让算法为每个用户动态地优化选择，就会对用户粘性产生巨大的影响. 让数据定义编辑团队的前10个项目中哪5个应该向给定用户推广, 按照什么顺序.
例如, if a specific user has never watched a single reality TV show on your service, 该算法可以确保你不会把最新的歌唱选秀节目作为他们登录后看到的第一个项目向他们推广. 相反, 过去看过相关内容的人可能会立即与选秀节目的宣传相匹配. 同样, if a user watched your flagship new drama series yesterday, 该算法可以确保你从编辑列表中向他们推广其他内容，直到几天后下一集播出.
Many editorial teams also vary the hero banner by day parts such as “morning,日间电视,“傍晚时分”,和“深夜。.” This has been shown to increase engagement, but algorithms can vary the hero banner by the hour, as well as based on the typical viewing patterns for that household at that time. 这在家庭中最为明显，孩子们的内容主导了放学后和之前(希望)的早睡时间. 然而, if a viewer or household does not have kids, promoting Peppa Pig at this time would be lost engagement.
这种编辑和算法定位的成功结合可以也应该应用到你的应用中. When we interviewed Dan Taylor-Watt, the former Director of Product for BBC iPlayer, 他告诉我们，当他们添加个性化算法，根据用户的个人历史调整iPlayer“最新和趋势”栏的顺序时，“完成游戏”增加了36%.
编辑和数据——一个动态的组合
Any row of content that is populated by humans can be optimized by making subtle, 每个用户的自动调整. 这里有一个例子. 我们为我们的一个客户执行了A/B测试，以展示个性化策展行的价值. One group of visitors to the service was shown, 就在英雄旗帜下面, a row of content that had been personalized in terms of order. 再往下看，他们看到了一排刚刚由人类整理的，没有外部干预来决定顺序.
In the other testing group, these rows were reversed. The purely curated row was positioned higher, and the personalized row pushed down the page. 结果显示，整体转换率(游戏邦注:即玩家所玩内容的数量比玩家所玩内容的数量高出50%). 当个性化行显示在更显眼的位置时，显示的内容项数.
了解你
So what about brand new users who haven’t had time to build up a viewing history? Or ad-supported services that don’t require a user to login? 在这种情况下，算法能做什么?
对这群人来说，关键是要向他们展示各种各样的内容，以增加他们找到自己喜欢的东西的机会. Once your editors choose the “featured” content that best showcases the breadth of your library, 让算法确保在用户每次登录时切换一行中内容项的顺序, 或者为每一个独特的页面浏览量. The curated selection of content will remain the same, but there’s less risk of the app appearing repetitive on a return visit. You’ll also increase the chance of users discovering something new to watch.
全动态应用是未来趋势吗?
The logical extension of this approach is to think vertically as well as horizontally. Just as the data can be used to define the order of content items within a given horizontal row, it can also be used to refine the vertical order in which rows are displayed on the page. 如果数据显示个人用户喜欢喜剧，但从未对古装剧表现出任何兴趣, 让你的算法把你的一整排脱口秀节目移到页面的顶部，把历史传记片的那排放到下面.
How far will this dynamic UI approach go? 从理论上讲, you could have a completely dynamic page with no two consumers seeing the same experience, 很像谷歌的搜索结果. 如果数据告诉你这个特定的用户以前从未点击过该类别中的某项内容，你可以将“趋势”栏向下移动.
然而, I can’t see many streaming services taking their commitment to dynamic UI this far. 如果“趋势”轨道的整个想法是帮助用户找到他们下一个最喜欢的剧集, you’re going to want to keep it prominent just in case. 作为人类，我们喜欢一些熟悉的东西. If you’ve ever relaunched the UI of a website you’ll know users can be very, very vocal in their anger at a change to the status quo. 这些用户在两三年后你的下一次更新时也会同样生气, because once again it upsets their desire for the familiar. So I wouldn’t advise that any streaming service dives straight-in to a fully dynamic page layout.
你从哪里开始这个策略?
If you haven’t tried combining algorithms and editorial in your service yet, I’d recommend running some tests on your hero banner to begin with. I guarantee you’ll be impressed with the results.
这是流媒体系列文章中的第二篇，在这篇文章中，我将分析五种新兴的个性化策略，我们看到这些策略被我们的客户和其他领先的流媒体服务所使用，并产生了巨大的影响. 你可以找到 overview of the other four strategies here.
下个星期, 我将研究如何使用不同的数据科学技术和消息来为您的业务实现不同的结果. If you can’t wait that long, you can check out our e-guide: Five engagement-boosting strategies every streaming service should adopt right now.
[Editor's note: This is a contributed article from 24i. 流媒体 accepts vendor bylines based solely on their value to our readers.]
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