How can personalization on the OTT platform enhance user-experience?

Posted On November 12th 2020

One of the best features of the OTT platforms is that unlike Television, the users can choose what they want to watch and for how long. These platforms have a better accessibility, higher quality of content, user experience, and superior video quality than traditional Television channels. Hence, more and more viewers are subscribing to these OTT platforms.

However, the OTT platforms still face some significant challenges. Due to the market being very competitive for the attention of the users, these platforms try their level best to provide quality content to their users. But this raises the problem of choice.

The problem of choice

Decision paralysis or the problem of choice is when there are too many options available, effort around searching for the content of choice could lead to die down of interest. This works counterproductive to the objective of providing more options in first place.

This is the same problem viewers face every time they open any OTT platform. There are so many movies to choose from that they usually are unable to decide what they want to watch, and they end up watching the same kind of show or over and over again. If that is not the case, they then close the platform and choose something else. What they need is a more personalized approach.

Purpose of a Recommendation Engine

What OTT platforms need specifically are recommendation engines. The primary role of a recommendation engine is to personalize the users' experience to increase their engagement. It offers every user a discovery experience tailored to their choice of content. It also helps in keeping the users loyal to streaming services.

There are multiple reasons as to why the recommendation engine is used, such as:

1. For finding content faster and more efficiently.

2. To provide users with more binge-worthy content.

3. For a more targeted advertising

A solution

The solution lies at the ability of service providers to analyze the user's data to deliver relevant recommendations in real-time. The entire cycle of the user, from when they open the streaming service to the time they close it, are to be studied to understand their intent and interest pattern. Not all platforms provide this facility, and the users are generally stuck with finding a needle in a haystack.