Digitalization pervades every area, entertainment is no exception, especially with the arrival of OTT platforms. OTT entertainment industry contributes to a significant part of the consumption of digital content. OTT has turned into a lucrative model to go with and get the instant monetization of the content. These platforms play with the quality of the content on the platform since there is an immense quantity of content available in the market.
This makes the overwhelming availability of the content for the viewers. All this creates a cut-throat competition and it becomes the responsibility of the OTT service providers to make the best of their content. This will help in increasing the audience base and decreasing the churn rate of the platform. One of the tactics to do this is to give them a personalized experience by using recommendation engines. This will help in getting the audience engaged on the platform.
Giving them the personalized recommendation will help in getting more watch time of the content and more engagement on the platform. Every good platform always tries to provide “Top Picks for You” which makes the content more customer-centric. Even YouTube also provides relevant recommendations to get the attention of the viewers and get them engaged on the platform. All this happens due to the recommendation engines that these platforms use.
Understanding Recommendation Engine
Recommendation engines are a well-known way to suggest content related to the viewers’ habits and their viewing history. It is primarily a filtration system of information that ends up bringing the most relevant recommendation for the targeted audience. For instance, if you have watched comedy movies on Netflix for the past two weeks, the recommendation engine analyzes the pattern. With the proper analysis of the viewing pattern of the customer, the platform provides similar recommendations to the viewers. This sort of personalization attention to the viewer will get them glued to the platform. It will not only increase the traffic on the platform but also helps in improving customer loyalty.
In short, it works using a combination of machine learning and data. In recommendation engines, data plays a crucial role from which the patterns are derived. If the more data it has gathered, the more appropriate information it will provide.
Majorly there are three types of recommendation engines, which include:
This sort of filtering uses matrix-style formula. It does not analyze the content whether it is books, shopping items, or even films). Rather it focuses on gathering and analyzing data based on user preferences, behavior, and activities to predict what a particular viewer will like.
It works on the process that if a person likes this thing they will also like that thing. The major drawback of this filtering system is that it is limited to recommending products or content similar to the previous one. This recommendation system does not go beyond that. Say if the viewer like watching a romantic film, the recommendation engine will only provide romantic movies or content to the viewer.
A hybrid model uses the data of both filtering systems i.e. collaborative filtering and content-based filtering. This model outperforms both of them. Language processing tags can be generated for every content/product/movie/song. Vector equations are in use to calculate the similarity of the available product. Afterward, a collaborative filtering matrix can be used based on the behavior, preferences, and activities of the end-user.
In short, it is required to give justice to the available OTT content. Thereafter giving every content a fair chance for visibility to the end-user. It gives the viewer an opportunity to understand and learn more about the available content. A recommendation engine is quite a crucial asset for the OTT business and helps in increasing the growth of the platform.