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How AI Is Transforming the OTT Experience

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OTT adoption is growing, and Artificial intelligence (AI) is impacting nearly every aspect of the content delivery lifecycle. From content discovery to video indexing, AI can enable customization of the viewer experience and help programmers and marketers learn more about their audience. AI is the lever that can craft a hyper-personalized media experience and offer OTT providers the means to stand out in an increasingly competitive market.

According to Allied Market Research, the OTT market will reach $1.039 trillion by 2027. More than 50% of OTT revenue comes from advertising, 40% from subscription fees, and the remainder from pay-per-view and video downloads. Subscription OTT customers generate nearly twice as much money per user as ad-supported viewers. AdTech experts predict that connected TV ad spending will reach $21.2 billion in 2022, increasing 39% over 2021.

OTT providers and marketers are using AI to manage and deal with the ever-increasing streaming data. Here are a few examples of how AI is transforming the OTT market:

  • Personalized customer experience – Netflix research shows that if subscribers do not find something to watch within 60 to 90 seconds, they lose interest and move on. OTT providers can use AI to track behavioral data points to choose hyper-optimized content recommendations to improve the viewer experience. Machine learning algorithms reveal subscribers’ content preferences, making content recommendations to viewers easier. They also uncover collective-behavior relationships, identifying more significant trends relating to content and titles that may not be obvious. These trends can be invaluable in guiding content licensing.
  • Uninterrupted viewing – Streaming services deliver massive amounts of data each second. Service interruptions are bound to occur. Providers can facilitate data traffic flow by using AI techniques to parse data on what users are streaming and where to expect service spikes. For example, algorithms can determine when to cache websites on regional services for faster load times. Predictive analytics rely on AI methods to identify potential network problems before they occur.
  • Smarter, faster video indexing – Content search and retrieval continue to become more sophisticated. AI-powered search and indexing can extract speech and visual metadata from videos, thereby making content more accessible to viewers. Using AI/ML techniques for video indexing, it is possible to request specific content, such as chase scenes within movies. Faster search and recovery of AI-indexed content improves viewer experience and opens new possibilities for consumer research.
  • Complex data search – AI/ML techniques eliminate the need for manual indexing. Using AI, every second of video content can be analyzed to catalog elements on the screen, character emotions, the nature of the scene, and more. AI can also generate a highly accurate index of all spoken words and metadata with detailed timestamps.
  • Intelligent encoding –With the help of AI algorithms, encoders can go further and select encoding based on content, as not all scenes use the same compression levels. AI-optimized encoding reduces the video's file size without sacrificing quality. Machine learning automates the process rather than manually choosing the best encoding for each scene. AI algorithms compare the content to known parameters for a device or media player, optimizing the bitrate ladder for the best playback quality. With this, we can match the bitrate to available bandwidth for the best quality delivery.
  • OTT captioning and more – Content delivered over different OTT platforms must adhere to industry standards, including offering closed captions in different formats and languages. Content providers now can use AI tools to review and revise machine-transcribed content. The process is faster and more accurate, saving time and resources while meeting requirements for transcripts and captions in different languages.
  • AI speech recognition – Natural language processing (NLP) makes it easier to access content using voice commands. Some cable providers are already using speech recognition, and it’s becoming commonplace as more people use Siri and Alexa. As AI technology advances, speech recognition will become part of every aspect of OTT, including viewing, production, and analytics.
  • AI-recommended thumbnails – Providing compelling visual imagery to increase viewership can be difficult. Streaming media providers are starting to use AI algorithms to create data-driven options for thumbnails. Contextual algorithms use machine learning to rank images and predict the probability of playing the thumbnail based on past content choices. A thumbnail is then chosen based on the likelihood to view.

OTT adoption continues to grow at an incredible rate, and the battle for viewership will be won by content and customer experience. AI and machine learning are making OTT interactions smarter, enhancing the overall viewer experience. Leveraging the power of AI, OTT platforms are creating hyper-personalized experiences for their viewers. As the technology develops and new opportunities to delight customers become more evident, the future will include more sophisticated uses of AI in streaming media.

[Editor's note: This is a contributed article from Tavant. Streaming Media accepts vendor bylines based solely on their value to our readers.]

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