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Role of AI in the Future of OTT

Over the Top (OTT) content distribution has changed the way video or audio content is consumed and it seems to stay for a while. Consumption of home entertainment via Internet-connected devices has increasingly become the trend for most people.

Even though linear TV today continues to offer traditional TV packages along with OTT, viewing habits have shifted towards OTT-only content over the years. Studies suggest that if your platform is easily discoverable, if you have great content, if you offer an intuitive user experience, and if it is reasonably priced, people will subscribe and get hooked onto your OTT service.

OTT customer acquisition and retention is quite a challenge, especially in a market with many players that offer original content, that are attractively priced, and that offer consistent user experience.

Another challenge is to reduce churn. Offering content and an experience that’s sticky is very critical in deciding the fate of your platform.

One of the ways to ensure a competent platform is to build a robust recommendation engine and content discovery mechanism by bringing in Artificial Intelligence (AI).

[ Listen to our expert, speak on the application of Artificial Intelligence in OTT. Play podcast now. ]

AI has a big role to play in content recommendation. We all know that Netflix has been making good use of AI to drive content recommendation for a few years now. It is important because user experience driven by AI allows users to navigate easily and find relevant content. A robust AI driven platform thus ensures lower bounce rate.

Artificial Intelligence algorithms have become enormously relevant in the OTT space, especially with the enhanced processing power of devices.

AI adds a sensory capability to the OTT platform. It detects viewing pattern by recognizing how long a content is being consumed, what kind of content is being consumed, at what time of the day is a content being played, and when they move away from a particular content.

Based on this data, AI will be able to throw better recommendations to the user. The current recommendation engine is based on a generalized trend. If on a Sunday, you end up watching 2 superhero movies, it will recommend you similar content based on the algorithm. It may recommend you superhero movies from a different universe or it may propose the purchase of a related action figure. This is a great way to have users stick around for a longer time.

Probably on a Saturday, I like to spend time at home with my spouse and I would want to switch to a
Rom-Com movie. AI will be able to detect such time and event-based trends and will be able to throw content recommendation at your consumers.

It may also be able to run an artist based content marathon, depending on your favorite actor’s, director’s, or singer’s birthday or anniversary.

AI may also lead to the better prediction to the platform owner about the success of the content and better revenue analysis for them. It may be able to give indicators to the platform owner about a particular kind of video, movie, music album or TV show such as when to release it, at what time, when are the sentiments going to be high, when is it going to be caught on to and watched the most, etc.

It may be able to give some predictive analysis on the pricing of the content. AI can also be hooked onto social media. It will be able to sense the sentiment about an upcoming title and be able to tell the platform owner whether a particular pricing strategy will work or not.

AI can be used to Live stream sports events for creating personalized highlight reels. Let’s say you are watching a match and you are unfortunately on the losing side. When the highlights come on, you do not want to watch your side lose again. So, AI can sense that you are supporting a losing side and it may probably recap and throw customized highlight to you which shows only the high points of your team in the game.

AI may be able to sense users’ emotion and show a particular advertisement that may work well during a high point in the game or another one that may be apt to a low point. So using AI, advertisements can be customized to achieve maximum impact.

It can also be used for content analysis that producers can use to their advantage. They can find out whether consumers are liking changes in a particular show because it is linked to social media. It helps to analyze whether users are staying or dropping off. It can give analysis to the producers of the show whether a particular new segment is being liked or not, and that can enable producers to change it in real-time. If viewership drops because of a change in character or a host, producers can act accordingly. AI will play a very important role in the success of an OTT platform and it will drive all data analysis, predictions, and trends for a business. It’s no longer an option, it’s a must-have for any OTT platform to be successful.

[ Muvi.com allows you to build a platform that integrates with social media and suggests recommended titles based on your viewing habits. ]

Can AI go wrong?

The AI technology per se need not necessarily go wrong, but related data can be misused. It’s up to the platform owners and regulatory bodies to form a framework to comply with certain security standards in order to protect user data. The other thing is that unauthorized retrieval and sale of user data can lead to illegitimate Ad placements.

With AI, OTT businesses are using data to improve the user experience. It’s being used to deliver a much richer and a much more engaging experience. Improved AI systems can be seen as a response to the magnitude of data being included in OTT platforms, as they adopt data-driven personalized content and services. Essentially, AI is being used by content owners, content aggregators, production houses, platform owners, and pretty much anyone who is in the business of OTT as a means to manage the output and drive personalization.

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  • Thanks for sharing valuable information, looking forward to see more articles in similar context. please keep sharing 🙂

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