OTT提供商如何提供能引起观众共鸣的推荐
OTT providers are quickly catching on to the benefits of user recommendation services being integrated into their platforms. 然而，集成的两种主要选择都有缺点. 现成的解决方案可能会带来昂贵的成本, 但更便宜的定制部署需要合适的内部专业知识. What’s needed is a meet-in-the-middle solution that saves on expenditure and is intuitive to use, while crucially providing recommendation services that impress viewers and ultimately helps to retain them.
当前战略的陷阱
A heavily saturated market and sustained pressure on business finances is leading OTT providers to restrict their budgets. It’s unlikely that they’ll want to invest heavily in an off-the-shelf solution with high capital and per subscriber costs, despite the ability of these platforms to help teams deploy personalized recommendation services quickly. 需要立即看到显著的结果, or else the board won’t hesitate in demanding answers from decision-makers as to the expense.
因此，OTT提供商正在探索内部解决方案的替代方案, but they frequently find they don’t get far when the skills of their employees are insufficient to effectively build it. 而不是, 一个汇集数据的解决方案, analytics and machine learning (ML) in a flexible and cost-effective package is vital to delivering the most innovative recommendation services to end users.
不止一种方式的个性化
The answer is a unique personalization platform with serverless and scalable features, AWS个性化就是一个突出的例子. It requires little working knowledge of ML, and is highly secure when implemented correctly. This kind of technology fits all the requirements for OTT providers looking to leverage the opportunities provided by recommendation services. 而“推荐给你”的旋转木马是一个伟大的功能, 消费者现在也可以通过各种其他方式参与进来.
Providers have the opportunity to integrate a “more like this” or “other users also watched” carousel with the help of user interaction data. A viewer that’s just finished watching a documentary about the history of the Olympics might be interested in the origins of association football, 例如. Historical viewing habits can also help shape the journey for other viewers with shared interests.
数据的最佳使用甚至可以影响搜索结果的显示方式. 这些列表可以根据单个查看器重新排序, 或者推动编辑策划的内容推荐. This is an exciting prospect as search results can be individually tailored to the user and their interests. 虽然AWS个性化为众多市场提供了价值, its personalized recommendation algorithms can be ideally implemented into content services.
进一步参与的机会
这项技术还可以将OTT提供商带到其他一些令人兴奋的方向. Say the OTT provider is aiming to promote an individual content item in recommendation results. The recommender can be tweaked to show a certain amount of content from a particular promotions category. 背景建议也开始崭露头角. 这些建议可以根据所使用的设备提供, 一天中的时间或用户的位置.
然而, it must be said that using AWS Personalize in a media context is more complicated than its other managed services solutions. 来缓解这个痛点, there are external solutions out there that can take the hard graft out of provisioning AWS Personalize. 一个是Merapar开发工具包(MDKs), 哪一种能够实现转换和摄取大量数据的能力. 有了这个解决方案，OTT提供商可以立即启动并运行. 模块可以允许灵活地使用多少技术, 根据需求和可用预算. 想要快速部署新功能的OTT提供商, 在市场上保持竞争力，试用新服务很容易做到这一点.
测试新功能对于判断哪些是有效的，哪些是无效的至关重要, 并且能够推动持续的改进. 这项技术有一些巧妙的方法可以做到这一点. One example is the ability to deploy multiple recommendation models that are driven by user data. Parameters are adjusted automatically and then the best performer in the trial is selected to be used moving forward, 随着时间的推移，建议会变得越来越准确.
同时不断提高准确性, it is of course essential to know the levels of user engagement with suggestions provided to them. Clickstream data can be exported out to an analytics platform to enable organizations to measure performance against their main KPIs. Some examples of these include conversion rate of journeys from a recommendation click, the click-through rate (CTR) or even the levels of engagement from customers that watch or click on recommended content. These insights can power optimization of services and ultimately help increase user engagement and reduce churn.
独树一帜
在一个不断有新进入者的市场上, 差异化从未如此重要. This has been made even more pressing by the cost-of-living crisis as more consumers query the value provided by their current subscriptions, 他们可能会把收入的很大一部分花在哪些方面. 各种各样的推荐服务, 从基本的旋转木马到上下文建议, 关键工具是推动竞争优势的工具吗. The question that remains is how these capabilities can be brought in without breaking the bank and relying on the limited skillsets of current employees. 由一个专门的工具包赋予生命, serverless, scalable personalization platforms can deliver recommendation services that meet viewer expectations.
[编者注:这是来自 Merapar. 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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