How to Improve the OTT Customer Experience Through Automation
The over-the-top (OTT) services market is predicted to continue advancing, after having experienced significant growth in recent years. Revenue is expected to reach $158.84 billion by 2024—more than double the amount of revenue recorded in 2018. Netflix has more than 200 million paying subscribers globally, so it's no shock that many of the largest content providers in the world, such as Disney and Amazon, 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.
Although, the ability to effectively use automation in routine tasks is one thing that all OTT media providers have in common. John Griffiths, CCO, Spicy Mango, explores how automation can play a vital role when it comes to enhancing both the consumer experience and commercial efficiency within compliance monitoring.
From automation to Artificial Intelligence (AI) and Machine Learning (ML), advancements in technology are constantly evolving and the increased adoption of these technologies has been witnessed across OTT platforms for many years. In fact, over 10 years ago, Netflix awarded a $1million prize to a developer team for an algorithm that improved the accuracy of the company's recommendation engine by just 10%.
Content recommendations have somewhat progressed since then, as OTT providers have increased access to rich metadata. When you consider the vast amounts of content available on the platforms such as Netflix and Amazon Prime Video, combined with the considerable number of service users, 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. Sports data is regarded as some of the most complex and diverse in the world, mainly because major sporting verticals are being consumed across numerous different countries and languages. So the challenge of how to collect, normalize and enrich extensive amounts of data to power schedules, leaderboards, players, events and venue information in real time to the viewer is complex, but important.
While the process could be reached manually, embracing an automated solution to standardize the common challenges related to data differences, authentication, ephemerality and dependability when working with a large array of supplying partners is far more commercially efficient. As technology continues to evolve, the ability for sports OTT services to harness and apply automation and ML to the vast data libraries that have been accumulated over time, presents a chance to further enhance the consumer experience and in turn, monetize the investment.
Automating Compliance Monitoring
More recently, Netflix has also used a human-technology collaboration to apply age ratings to all of its content. Netflix's employees were charged with streaming the whole content library, tagging scenes inappropriate for young children, and then sending all of this metadata into an algorithm. While it was a considerable task, 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, for example, has thousands of minutes of user generated content uploaded every hour globally. Manually screening every bit of content would be totally commercially inefficient, if not physically impossible. 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, in just the short space of a few months, ML was able to review and flag unsuitable content that would have taken 180,000 people working 40 hours a week to assess.
Taking the First Step
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, enabling thousands of operations per hour, compared to human operations that may take much longer.
Metadata is an area in which AI/ML offers a significant opportunity to improve efficiency and realise greater benefits. From inputting incomplete metadata and natural language processing, to enhancing existing metadata and generating subtitle files, to automating translation services for cross border services - all of which can be trained to be delivered at speed and scale. These enhancements not only support businesses to manage costs, 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.
The benefits of embracing automation, ML and AI technology within the OTT sector to improve the consumer experience and enhance commercial efficiencies is obvious. In a booming market, where demand and expectations from service users is increasing, 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 and AI technology. These vital innovations have an important role to play in the future of OTT and improving the consumer experience.
[Editor's note: This is a contributed article from Spicy Mango. Streaming Media accepts vendor bylines based solely on their value to our readers.]
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