As a newbie to the K-drama phenomenon, the first one I watched on the recommendation of friends and family was Squid Games last year. The next thing I knew, the OTT platform I was on showed recommendations to several K-dramas and even similar shows in my local language. With new shows on my list, in Korean and local languages, I had fortunately bucked the 2021 trend of peak COVID boredom. I was making full use of my subscription expenses with new and highly relevant content areas to explore. AI was at play, and in ways that really addressed my needs as a consumer.
AI and Machine Learning (ML) are already changing how we consume content on OTT platforms to benefit both users and platform owners. As OTT channels supplant cable TV, AI and ML technologies have become a key in obtaining and retaining viewers by delivering an enhanced experience. That’s precisely what I want to talk about today.
User Experience: Context, Time and Place
Your well-known recommendation engine is the first and most widely used AI-driven feature. With thousands of viewing options available and shortening attention spans, it is essential that users quickly get served up entertainment that they prefer without them having to search for it. AI helps OTT platforms deliver hyper-personalized content that increases user ‘stickiness’, keeping them returning for more.
Recommendation engines employ different filters to hyper-personalize content. The first is based on the content you’ve already watched. So, if you’ve been viewing The Tomorrow War, you’ll be offered more war and science fiction movies the next time you visit. Similarly, if you watch a cooking show, you’ll be prompted to watch other shows for foodies, including filtering for more cooking shows featuring the same cuisine.
Further, Machine Learning algorithms map viewer usage patterns to deliver content at the right time. So, if you’re a football fan, you will be given a heads-up about coming matches, and best-of-breed sites will incentivize you to provide the service with your preferences by sport, league, team, etc. Or, if you have a movie night on weekends, you’ll be given a choice of movies just when you’re sitting down for your weekly watch. These techniques will ensure that future visits lead more quickly to your desired content.
Besides timing, AI also has the ability to deliver content based on geography. So, a viewer in France may be served up French-subtitled content. The same content would be Bahasa-subtitled for a viewer in Indonesia. AI can also see what’s trending on social media in particular geography and offer viewers relevant content – a feature especially handy for news channels. AI’s capability to hyper-localize content makes it relevant in different geographies simultaneously.
An interesting use case of AI emerged during the COVID pandemic. With sporting events being played in empty stadiums, teams, leagues, and broadcasters worried that the lack of crowd participation would dull the viewing experience. To address this, content providers used AI and ML to scan thousands of past well-attended sporting events and recognize the crowd’s response to particular events – goals, misses, and fouls in football matches, for example. They then regenerated these sounds as the crowd-less event progressed, giving viewers the experience of watching a typical sporting event with all its bells and whistles. In fact, AI can take the sporting experience further by enabling viewers to generate match highlights themselves and, if they are particularly partisan, to get only highlights of the team they support.
If your spouse prefers one actor in a movie and you prefer another, AI can even hyper-personalize the preview you see when you log on to your OTT app. So, the next time you see the preview of The Rum Diary on an OTT platform, you could be seeing Johnny in the preview image, and your spouse might see Amber.
The potential of AI and ML-based hyper-personalization on OTT platforms is limitless.
Monetization: Giving Platform Maximum Bang for Their Buck
Using AI and ML also makes sound business sense for platform owners and advertisers to maximize their advertising effectiveness. For example, rather than serving up ads based on guesswork, AI tools allow platforms to hyper-personalize their ads.
This could be based on location – both in terms of context and language. Then, going deeper into contextual advertising, AI can determine which ads fit with the content being viewed and even during the viewing experience to serve it up. So, a viewer watching a baseball match could be served up a beer ad at half-time. Or, an Italian Tourism ad could be served up when a viewer watches Roman Holiday.
Making these ads shoppable on the back of AI is yet another capability that can empower both advertisers and OTT brands.
Sharply-focused ad dissemination helps maximize product demand and improves the platform’s credibility with advertisers, resulting in more substantial ad revenues over time.
AI & ML are transforming the OTT space, allowing for better & data monetization and a vastly-enhanced user experience.
Therefore, platform owners must embrace these tools and deliver the most optimal user experience that keeps viewers coming back for more – and the content owners laughing all the way to the bank.