As the world gets more digitized, OTT or Over-the-top platforms have become a significant part of the way we consume content today. Since the masses are congregating online, OTT has become a lucrative model and the number of video streaming platforms entering this market are growing by the dozens. And the best way that these platforms compete with each other is by doling out a good mix of quality and quantity of content. Sure everyone would readily welcome quality content but when there is a lot of quantity available, it is bound to get overwhelming at some point. At the end of the day, the irony is that an audience having innumerable choices is likely to feel more at a loss than when they have fewer choices at their disposal. This means that it becomes the responsibility of the OTT provider to help make meaning out of the madness for its audience. And the best way to do that is by personalizing recommendations for each person.
Have you noticed how Netflix or Amazon Prime has a ‘Top Picks for You’ list ready each time you log in and it changes it up for you time and again? Even Youtube drops an entire list of videos based on what they think you would like to watch. This is happening because of recommendation engines. Recommendation engines are one of the most prominent ways to suggest new content based on one’s viewing habits. It is essentially an information filtration system that offers the most relevant and appropriate recommendations to your audience. If you have watched a few action movies on Netflix then their recommendation engine analyzes this pattern to suggest something in the same genre rather than a romantic comedy which would fail to grab your attention. This is the kind of personalized attention a viewer will appreciate and is going to really keep them around. So one can say, personalization not only increases site stickiness but also improves customer loyalty.
In simple terms, a recommendation engine works using a combination of data and machine learning technology. Data is the crucial element from which patterns are derived. The more data it has, the more efficient and effective it will be. There are 3 main types of recommendation engines:
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Collaborative Filtering
Collaborative Filtering uses a matrix style formula. It doesn’t need to analyze or understand the content (be it books, films, shopping items etc). This system picks items to recommend based on what it knows about the user.
Content-based Filtering
This system works on the foundation that if you as the audience likes X item then you will also like Y item. The drawback of content-based filtering is that the system is limited to recommending products or content similar to what the person is already buying or using. It can’t go beyond to recommend other types of products or content. For example, if a customer only watches one genre, say horror, it wouldn’t recommend anything beyond horror content.
Hybrid Model
A hybrid system looks at both the data i.e collaborative and content-based and outperforms both. Natural language processing tags can be generated for each product or movie or song and vector equations are used to calculate the similarity of products. Following this based on the behaviors, activities and preferences of the users, a collaborative filtering matrix can then be used.
In conclusion, a recommendation engine is needed in order to give justice to all your OTT content and give each of them a fair chance at higher visibility. It gives your audience an opportunity to explore more of your content. It is an essential asset in your business and improves the growth of your platform by a considerable measure. Enveu is here to guide you in your research and help you make the right decision for a recommendation engine that can precisely suit your requirements. Not to mention we have the right technical expertise to offer for an overall OTT vision which will ensure every aspect of your journey is thorough and in safe hands. So get in touch with us right away.
