Use of Artificial Intelligence (AI) and Machine Learning (ML) in OTT Personalization & Prediction

In recent years, the over-the-top (OTT) media acted as a game-changer in the media market. It allowed viewers to access streaming services anywhere and at any time and bypassed cable, broadcast, and satellite television platforms, allowing for more cost-efficient marketing directed towards hyper-targeted audiences.

It made us survive the year 2020. With limited outdoor entertainment options and compulsory social distancing, OTT media came through, thereby recording a great spike in their viewership. They were able to keep their viewers continuously engaged by providing what their viewers wanted to see.

In the early weeks of March 2020, the United States went under complete lockdown, leading to a rise in the usage of OTT platforms.

According to estimates by a CII-BCG report, the number of paid OTT subscriptions surged to about 100–125 million in 2020, up nearly 55–60 percent from 49 million subscriptions in 2018 and it was the OTT personalization strategy that played an important role in this growth.

What is OTT personalization?

OTT personalization refers to the process of recommending, filtering, ranking, and prioritizing content according to the users’ online behavior, popularity rate, and the segment to which a particular user belongs to.

OTT personalization serves the purpose of delivering the right content to the right user at the right time across all touchpoints.

Five R’s of OTT personalization are:

• Recognize: Recognizing the user, including demographics, geography, and expressed interests
• Remember: Remembering the user’s preferences, watching patterns, and habits for further analysis
• Recommend: Recommending what the user would like to watch based on their actions, preferences, and interests
• Relevance: The content recommended should be relevant to the user’s location, preferred language, and time of the year
• Reinforce: Recording the user’s response towards the recommendations and further using the analysis to recommend better

OTT personalization plays an important role in OTT media’s success, such as:

• Content: The content’s script, whether it’s a movie, series, or documentary, is decided according to the viewer’s interest and most preferred genre, identified using the OTT personalization strategy
• Discoverable: OTT personalization strategy will recommend what the user wants to see, hence increasing the content discoverability
• User Interface and User Experience: Making personalized recommendations increases user interface, thereby providing a unique experience to each viewer and enhancing the user’s experience

So the question that remains now is how exactly OTT personalization is done?

Role of Machine Learning and Artificial Intelligence in OTT personalization

OTT media like Netflix, Hulu, and Amazon Prime depend on software like Machine Learning (ML) and Artificial Intelligence (AI) to grab the attention of the users in the first 30 seconds of the interaction phase.

They have realized that changing consumer interests, genre preferences, viewing patterns, and habits are key factors for success. With ML and AI, OTT media uses algorithms to analyze the most relevant content and experience for each user.

With modern approaches like ML and AI personalization, OTT media are able to provide a more scalable way to achieve a unique experience for individual users rather than segments of people. It allows delivering of one-to-one experience in the recommendation of the content, its categories, genre, etc.

Five ways in which OTT media uses AI and ML:

1. Personalizing Content Recommendations: With the help of Artificial Intelligence and Machine learning, OTT platforms analyze the watch history of users with similar tastes and interests, to recommend content to them that they’re most likely to watch. This results in successfully engaging the user and continuation in the monthly subscriptions.

2. Deciding Location for the Shoot: Using data to help decide the best shooting location for a movie according to the user’s preferred shooting spots.

3. Personalizing Thumbnails: Using the movie’s original art as the only thumbnail for every person most likely won’t yield the highest click rates. Thus, OTT media, with the use of Artificial Intelligence and Machine Learning, personalized thumbnails for each user.
In the above movie, Netflix put out thumbnail recommendations of supporting actors/actresses who don’t really represent what the movie is about but resulted in a higher click rate among certain ethnic audiences.

4. Personalizing Email and SMS: Sending out personalized emails and SMS, based on their watch history for engaging viewers.

5. Automate Compliance Marking: Detect potentially inappropriate content to avoid compliance issues in global markets and increase brand safety for advertisers.

Wrapping Up

OTT media like Netflix and Amazon prime have done a phenomenal job of applying Machine Learning and Artificial Intelligence the “right way” , proving that when applied properly, they can do wonders.

Effective AI solutions and Machine Learning can personalize the experience of the user, benefiting both OTT media in terms of subscriptions, users, and overall satisfaction.

Understanding the need for AI and ML for the success of OTT media, leading companies like Jellyfish Technologies offer world-class Machine Learning and AI software development services, taking your business to greater heights. Jellyfish Technologies team has adapted different predictive models like decision trees, linear and regression models, and neural networks for providing the best solutions.