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Why Your OTT Platform Needs a Recommendation Engine? 24 May 2021

 

The recommendation engine has become quite popular across diverse industries in the recent years. Starting from OTT (Over the Top) platforms to e-commmerce stores, the recommendation engine is gaining rapid traction. Whether you have just started your OTT platform or have plan to scale it up, recommendation engines can improve your profitability significantly.

A recommendation engine or recommendation system is basically an information filtering system, which offers the most relevant and appropriate recommendations to your customers. The main goal of a recommendation engine is improving your customer experience. But there are several other factors which make it a must-have for your OTT business. 

The ‘Top picks for you’ section on Netflix and ‘Recommended movies’ on Amazon Prime are the results of using recommendation engines on these platforms. 

Here we will discuss why it is important to have a recommendation engine for your OTT platform-

  • Personalized Streaming Experience 

Having a recommendation engine lets you focus on every customer’s preferences equally. Whenever new customers opt for your OTT channel, they expect to find the contents as per their taste. A recommendation engine provides your customers with what they are looking for instead of bombarding them with unwanted contents. This results in a personalized OTT store for every user. For instance, a customer who is into watching thriller series on Netflix, will get different recommendations from the one preferring comedy or action genre. Even if you have exclusive and unique contents for your OTT subscribers, without personalization, those fail to grab an audience.

Do you know Netflix has over 1000 recommendation clusters based on user preferences? The leading OTT player produces approximately US$ 1 Billion a year from customer retention through its personalized recommendations.

Also Read: Product Recommendation to Improve the UX of Your OTT Store

  • Easy Content Discoverability

A recommendation system does justice to all your OTT contents by giving them higher visibility. It enables your customers to explore more of your content as per their preferences. “Because you watched [the content name]” on Netflix and “Watch in Your Language” on Amazon Prime are the outcomes of recommendation engines used on these platforms. This helps your customer discover more of your contents offered on your OTT channel. Without using a recommendation engine, it is probable that many of your contents will remain unexplored.

Research shows that more than 70% of the OTT viewers are overflowed with content and they often get frustrated with the lack of content they actually like.

 

 

  • Big Data Management

An OTT business needs to deal with multi-dimensional data, such as data from feedback and review portals, social media, online forums, and others. A recommendation engine  requires these data to yield satisfactory results. It does the job of data collection and modelling using the huge pool of data such as-

  • User Behavioral Data-  Data related to user searches, product and page views, clicks on emails, push notifications, and other on-site and off-site activities
  • Contextual Data- Data associated with user current location, device type, referral URL if any, and others
  • Product Details- Data representing product description, language, genre, etc.

A recommendation system analyzes and manages these large data sets in order to compute patterns, trends, and other significant parameters associated with user behavior. As a result, it reduces your cost and overhead for big data management while offering better customer experience. 

Big data management for your OTT platform may cost over US$ 1000 per TB. A recommendation engine saves your money by taking care of it.

  • Comprehensive Video Analysis

Video analysis or video content analysis (VCA) is the process of automatically analyzing video to detect various temporal and spatial events. In video analysis, extracted metadata from raw videos are used to identify the building blocks of your VOD (Video-on-Demand). This in turn helps in finding video similarity in a more precise way.

A recommendation engine equipped with deep learning can do comprehensive video analysis. This makes the streaming experience more enjoyable for the end users.

Modern recommendation engines analyze catalogue by thumbnails, trailers, closed captions, and others apart from keywords and ratings. Furthermore, they come with suitable Application Programming Interface (API).

For instance, the ‘X-ray’ feature while watching movies or series on Amazon Prime, identifies the cast information of a particular scene. Now, its recommendation engine uses this information to suggest movies or web series with similar cast. Some of the most used video analysis tools are-

  • Motion Detection- Identifying relevant motion in a particular scene
  • Object Detection- Detecting various objects in a specific scene such as car, house, tree etc.
  • Face Recognition- Automatically recognizing human faces by using deep learning solution for facial recognition
  • Optical Character Recognition- Extracting texts such as license plates in a specific scene

And others. No doubt, this enhances the customer experience drastically.

Wrapping Up

Owning a good recommendation system can aid in your business growth considerably. And a little bit of research can help you make the right decision.

Muvi Recommendation Engine Alie comes with quick integration, advanced algorithms, real-time recommendations, and scalability which can increase your business potential manyfold. Furthermore, Alie has great adaptability across all types of data-driven industries. Thinking of trying it? Take a 14-day free trial today without giving any card details!

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ishita
Ishita is a Content Writer with Muvi Marketing Team. Apart from business writing, she is also an acclaimed author of three best seller romantic thriller novels. In 2020, she got featured in The Hindustan Times, a leading news portal as an inspirational Indian author.

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