超越狂欢:充分利用游戏后时刻提高用户粘性
在看完最新一季的美剧后，你是否感到有些失落 Stranger Things? 失去了，没有更多的情节 Obi-Wan Kenobi? 第三季还没到 The Boys? Maybe you’ve just worked your way through all the movies in the Marvel Cinematic Universe and have forgotten what it was like to not have the next one ready to watch?
We’ve all felt that void that’s left when our current binge-watching obsession is over. So why are so many streaming services missing out on a chance to feed our need for something new to watch at this crucial moment?
刷剧现在是一种普遍现象, 而且许多流媒体服务都支持它的“自动播放”功能. 在你正在看的电视节目的演职员表滚动的几秒钟内, the next episode will auto-play unless you disable the feature or hit the pause button. 他们甚至可能给你机会跳过片头!
Auto-play is great for encouraging binge behaviors and is pretty easy to implement in episodic content. But how many streaming services are really optimizing the post-play behavior for content that doesn’t have an obvious sequel - like a standalone movie or the final episode of a series?
Successfully filling the hole in a consumer’s life that comes when their current favorite show comes to an end is not just good for engagement, 这对消费者的满意度很有帮助. 这对于应对当前市场的动荡至关重要.
Our experience suggests that adding personalized recommendations at this moment is an excellent way to increase conversions. The BBC agrees. When I 采访了BBC iPlayer前产品总监丹·泰勒-瓦特, he told me the moment at the end of playback was “one of the most effective areas” for their use of personalization. In fact, he said that “moving from an opportunity to offer a generic recommendation to actually something specific in that moment,给了他们从个性化课程中看到的一些“最大的提升”.
建立所有正确的联系
那么，如何在游戏结束后给出正确的建议呢? We typically recommend our clients use the same algorithm that’s behind their “More Like This” and “Because You Watched” categories on their homepage. 在我上一篇关于推荐心理学的文章中 I noted that these models should usually be tuned to place more emphasis on metadata correlation than techniques like user clustering. 当然，这需要高质量的元数据匹配, a topic so fraught with difficulty that it will get an article of its own in the coming weeks.
然而，关于用户行为仍然有重要的考虑因素. 当我写到如何平衡 自动化和编辑管理, I highlighted how algorithms can be used to make sure you don’t waste too much time recommending the latest episode of a series to someone who has already watched it. 那么，以我之前举的一个例子来说，为什么Disney+会推荐《百家乐软件app最新版下载》系列呢. 在我看完最新的漫威电影之后, 尽管它的数据应该清楚地表明，我已经观看了奥巴马的节目. 漫威在过去几周的表现? I wonder if there’s an automated rule in play here that is triggering the latest MCU series after the latest MCU film? 但为什么不把用户行为也考虑进去呢?
当然，推荐人们重看自己喜欢的旧剧也是有价值的. But there’s also a chance you’ll waste this golden opportunity to introduce them to something new. I’ve often found myself graduating to a third or fourth rewatch of a much-loved series rather than wasting my evening scrolling through category after category looking for something new. Once again, testing can confirm which approach works best for your particular audience. Perhaps the answer is to have a UX which offers two post-play recommendations to maximize the real-estate. 这是一种流动的旧押韵:有旧有新?
To auto-play? Or not to auto-play? That is the question!
Speaking of UX, a debate we often get drawn into with customers is what should the post-play experience look like? Many of our streaming customers consider auto-play to be a bit too intrusive in scenarios where there’s not another episode coming right up. They choose to offer one or more recommendations that the consumer can opt-in to very easily, 但不是替他们做决定. One leading streaming service we’ve worked with calls it “auto-suggest” rather than “auto-play” for just this reason.
相反，其他服务完全支持自动播放. This comes down to understanding your customer and giving them an experience that fits their needs. If you’re confident that your customer wants to find good content but also avoid too much decision-making after a long hard day at work, 那么自动播放就是一个很好的解决方案, 只要你对你的内容的相关性有信心.
In many ways, streaming services that do this are replicating the experience of a linear channel in a VOD scenario. Just as in the world of live TV - and especially the “laid-back” experience of big-screen viewing that 24i knows so well - many consumers enjoy not having to touch the remote too often. 从服务提供者的角度来看, 有可能当客户找到遥控器或暂停按钮时, 他们会看得足够多，从而相信推荐的节目值得一看.
If you’d like to learn more about optimizing your metadata for effective recommendations, 你可以等我下周的下一篇文章, 或者你可以下载24i的电子指南: 现在，每个流媒体服务都应该采用五种提高参与度的策略.
[编者注:这是来自 24i. 流媒体接受供应商署名完全基于它们对我们读者的价值.]
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