如何通过自动化改善OTT客户体验

预计OTT (over- top)服务市场将持续发展, 在经历了近年来的显著增长之后. 预计收入将达到 $158.840亿年到2024年，收入将是2018年的两倍多. Netflix在全球拥有超过2亿付费用户, 因此，世界上许多最大的内容提供商, 比如迪斯尼和亚马逊, are investing in this space with the offerings of new and highly competitive OTT services. But it's not just for superbrands; OTT is attracting many niche entrants as demand for such services extends beyond the popular mainstream media.

虽然, the ability to effectively use automation in routine tasks is one thing that all OTT media providers have in common. 首席运营官约翰·格里菲思辣芒果, explores how automation can play a vital role when it comes to enhancing both the consumer experience and commercial efficiency within compliance monitoring.

数据自动化

从自动化到人工智能(AI)和机器学习(ML), advancements in technology are constantly evolving and the increased adoption of these technologies has been witnessed across OTT platforms for many years. 事实上，十多年前， Netflix向一个开发团队颁发了100万美元的奖金 for an algorithm that improved the accuracy of the company's recommendation engine by just 10%.

从那时起，内容推荐有了一定的进展, 因为OTT提供商增加了对丰富元数据的访问. When you consider the vast amounts of content available on the platforms such as Netflix and Amazon Prime Video, 结合相当数量的服务用户, depending on manual processes to analyze data consumption in order to recommend content was never going to be an efficient approach.

Sports OTT services is another key area where data automation can be adopted to create an improved consumer experience. 体育数据被认为是世界上最复杂、最多样化的数据之一, mainly because major sporting verticals are being consumed across numerous different countries and languages. 所以如何收集的挑战, 规范化和丰富大量的数据到电力计划, 游戏排行榜, 球员, 事件和场地的实时信息对观众来说是复杂的, 但重要的.

虽然这个过程可以手动到达, embracing an automated solution to standardize the common challenges related to data differences, 身份验证, ephemerality and dependability when working with a large array of supplying partners is far more commercially efficient. 随着技术的不断发展, the ability for sports OTT services to harness and apply automation and ML to the vast data libraries that have been accumulated over time, 提供了进一步提升消费者体验的机会，反过来, 将投资货币化.

自动化法规遵从性监控

最近, Netflix也使用了人工技术协作将年龄分级应用于其所有内容. Netflix的员工负责将整个内容库流式传输, 标记不适合儿童的场景, 然后将所有这些元数据发送到一个算法中. 虽然这是一个相当大的任务, this algorithm can now be used to detect unsuitable scenes in new content and automatically assign an age rating.

Countless OTT providers have also depended on automation to support compliance monitoring activities. YouTube, 例如, has thousands of minutes of user generated content uploaded every hour globally. Manually screening every bit of content would be totally commercially inefficient, 如果不是物理上不可能的话. But that's not to say that human input isn't still essential; human reviewers are necessary to the process of removing content and training ML systems. When it comes to screening content on YouTube for what is considered appropriate content for younger audiences, 在短短几个月的时间里, ML能够审查并标记不合适的内容 18万人工作40小时一周时间评估.

迈出第一步

The first steps to automation reside in current workflows and identification of the monotonous time-consuming and low value tasks. These are the tasks that normally offer themselves well to automation because systems can be developed and trained with relative ease, 每小时可进行数千次操作, 与人工操作相比，这可能需要更长的时间.

Metadata is an area in which AI/ML offers a significant opportunity to improve efficiency and realise greater benefits. 从输入不完整的元数据和自然语言处理, 增强现有元数据并生成字幕文件, to automating translation services for cross border services - all of which can be trained to be delivered at speed and scale. 这些增强功能不仅支持企业管理成本, but also help to generate revenue by reducing churn through a better user experience, providing increasingly accurate recommendations and launching in new geographies more efficiently.

结论

拥抱自动化的好处, ML和AI技术 within the OTT sector to improve the consumer experience and enhance commercial efficiencies is obvious. 在蓬勃发展的市场中, 服务用户的需求和期望在哪里增加, there are further opportunities for OTT providers to capitalize on the data that they have access to, in order to achieve a competitive advantage and secure a greater return on investment. While compliance monitoring is part of a wider and more complex movement to safeguard online consumers, a more efficient human-technology collaboration can be supported through automation, ML和AI技术. These vital innovations have an important role to play in the future of OTT and improving the consumer experience.

[编者注:这是来自辣芒果. 流媒体 accepts vendor bylines based solely on their value to our readers.]