India is witnessing a data revolution owing to high internet bandwidth, easy access to smartphones across the length and breadth of the country, low mobile tariffs, and new upcoming technologies. As a result, consumers are not dependent on their television sets for entertainment anymore.

Above all, mandatory social distancing, limited outdoor movements for months (even for a year) led to a substantial change in how Indians consume video and audio content.

Did you know the fact: The OTT sector in India witnessed a 30% rise in paid subscribers, from 22.2 million to 29.0 million, between March and July 2020. A PwC Global Entertainment & Media outlook: 2020-2024 report revealed that India is the fastest-growing OTT market at 28.6 % CAGR; to become the sixth-largest market in 2024.

The love for Over the Top (OTT) platforms probably started with the younger population wanting to binge-watch their favorite web series. Can you believe that India currently has 95 OTT platforms across the video, music, podcast, and audio streaming categories?

Some of the top OTT platforms in India are Disney+ Hotstar, Netflix, Amazon Prime Video, Zee5, Sony LIV, Voot, AltBalaji, JioCinema, and MX Player.

With the continuous spike in user base and viewership, OTT platforms are looking to present unique products to their viewers, whether it is original content, excellent OTT recommendation algorithm, highly relevant content discovery, or any other element to provide a personalized user experience (UX).

This is precisely how the biggest OTT players differentiate themselves from their competitors. In this blog, Selectra will decode how and why the Artificial Intelligence (AI) OTT algorithm contributes to an OTT platform’s success and profitability.

Are Recommendation Engines Essential for OTT Platforms?

With the stretch of OTT services to the remote corners of the world, it is of paramount importance for the recommendation engines to match the ever-changing needs of the viewers.

Selectra can safely say that recommendation is indeed the secret sauce for OTT businesses.

Why is it essential for the top OTT players in India to have a solid and accurate AI-recommended engine? The simple answer is to personalize the viewing content for the individual customer and increase customer loyalty and site stickiness.

Additionally, there could also be business reasons to recommend content to a specific audience that may or may not match their preferences.

OTT Ecosystem Challenges

The OTT media is highly competitive with high churn rates. If customers don’t like some content, they can switch to other platforms almost real-time and never return.

The top OTT players in India have started to get a deep understanding of their audience, in which case, personalization has become a key differentiator.

What is the OTT ecosystem like? What kind of challenges does OTT face? How does OTT algorithm help solve these challenges?

High churn rate, content discovery, omnichannel viewing, releasing quality and relevant content, and increased customer acquisition cost are some of the challenges prevailing in the OTT businesses. OTT recommendation works well with factors like content, UX, UI, and discovery, thus helping top players set themselves apart from the crowd.

Meaning and Benefits of Customization

Customization is information filtering, prioritizing, classifying, and adjusting content to offer a personalized UX to individual viewers.

AI’s machine learning (MI) can churn different data points to generate apt recommendations based on an individual’s viewing history, preferences, and content type.

• Customer Data (demographics, gender, age, viewing history, seasonal viewership, total movies watched),
• Content Data (video play stats, finished and watch time, devices used, regional content, video views),
• Sales Data (real-time site traffic, growth metrics, conversion rate, customer churn, popular products by region).

An efficient AI recommendation system can help the OTT platforms reap several benefits like increased customer loyalty, improved routine-based consumption, deeper insights of user behavior, and targeted tailor-made solutions to meet specific business needs.

Machine Learning Techniques for Prediction and Personalisation

To be future-ready, the top OTT platforms of India are investing in high-powered AI systems to meet the constantly changing consumer interests, genre choice, and viewing habits/patterns.

Some of the critical elements of the customization journey include:

• knowing the customer and prospect profiles (demographics, expressed or shared interest),
• knowing the customer’s history (actions, play, buy),
• reaching the viewer with the right product recommendation at the right time (preferences/interests)
• delivering customization based on their individuality, location, and the time of the year,
• including a feedback action plan to be reinforced back into the OTT algorithm.

Below are the different personalization and prediction MI techniques that OTT platforms use for their OTT algorithm.

1. Matrix Factorization and Markov Chains

   The Matrix Factorizing technique learns a user’s general taste by considering the matrix from observed user-item preferences. And the Markov Chains technique involves sequential behavior by creating a transition graph to predict the course of action basis the user’s recent actions.

2. Clustering Algorithms

   Clustering Algorithms are unsupervised ML algorithms used to study unlabeled data, segregate it into groups with like traits, and assign it to clusters.

3. Regression Analysis

   Regression Analysis is a supervised ML algorithm used for defining relationships between independent and dependent variables. Regression aims to find a straight line that can accurately depict the relationship between the two or more variables.

4. Association Rules

   Association Rules discover interesting associations and relationships among large data points depicting how frequently an item set (e.g., a movie) occurs in a transaction. A typical example is a market-based analysis.

Conclusion

We hope that this blog decoded the recommendation algorithm of the top OTT platforms the way you want us to. We will continue to release blogs on topics that interest you. So keep following our page and do not miss on the next blog.