Recommendation engines for OTT platforms are not a new concept. There are extensive classes of web applications that use recommendation engines today. And the number is growing at a scale. Predicting consumer interests and turning these responses into options, such a facility is called a recommendation engine or recommendation system. Indulging this technology widens the scope of new-age marketing and enriches consumer engagements. The best examples of recommendation engine applications are:
- Content-based recommendations: these systems examine the properties of the items recommended. For instance, when a user searches for a movie in a particular genre, the system recommends all the best-rated movies in that genre.
- Collaborative filtering systems: these recommendation engines recommend items based on a similarity between search/kind of user personality. For instance, shows and episodes are recommended based on the search and preferences of other users who watch the same show that the consumer watches.
These are several models for recommendation systems that are used across the different OTT vertices. Due to the recent pandemic effect, most of the population shifted to OTT platforms rather than traditional media. And it increased the internal market competition between top online media players in the entertainment industry, and most of them think the recommendation engine can be the game-changer.
Why are recommendation engines for OTT platforms?
Online media is taking over, and the expanse of OTT and VOD is continuing to reach millions of customers. With a rapid shift in consumer behavior and perceptions, digital media platforms a digging in for applications that read the user’s mind and facilitate a recommended list of products/shows/items that they are more like to get attracted to. And the same rule applies to the online entertainment industry.
Calling recommendations boosting the OTT growth and revenue in several ways. Leading OTT and VOD services indulging recommendation systems to fuel, their subscription-based/transactional/ad-supported models and retain consumers over time. Today recommendation engines are beyond selling personalized content. The hybrid recommendation system not only customizes the online streaming experience for an individual user but improves the site’s stickiness and loyalty.
Applications of recommendation engines in OTT
The significance of AI-driven recommendation engines for OTT platforms is mountainous, and the key to unlocking the potential of AI for recommendation engines is ‘data’. The till-data applications of recommendation systems in OTT or VOD completely run on the insights driven by consumer data. And below, we described a few applications of recommendation systems that you can leverage on the go.
- Custom recommender systems
A custom recommendation system analyzes the past data history of a user and predicts future insights that are more likely to engage the user.
- Cloud-based recommender systems
Cloud-based recommender systems help OTT or VOD service providers better understand whether a service satisfies the user requirements or not.
Examples: Rotten Tomatoes and Pandora Radio
- Knowledge graph-based recommendation systems
Knowledge graph-based recommendation system is an effective class of recommendation system. The system maps the user activities and heatmaps over a period and then drives the insights for better recommendations.
- Reinforcement learning
Reinforcement learning optimizes the recommendation system by closely observing the user preferences, interests, and tastes, and time to scale the recommendation funnel.
Unexplored Potential of Recommendation Systems in Digital Media.
The futuristic applications of recommendation systems are limitless. And in the information age leveraging the advantage of data and streamlining recommendation systems takes over the digital media space in offering a personalized and seamless streaming experience. And offering tailor-made content promoting the quality of the streaming platform. Integrating AI automates the recommendation arena. Smart recommendations are building the future of hybrid audio streaming or video streaming. And it can do wonders we never thought of.
The below mentioned are a few practical advantages of using a recommendation engine.
- Faster and efficient content discovery
- Promote binge streaming
- Valuable advertising
- Real-time insights to decrease churn ratio
- Turn a non-subscribed user into a subscribed user
Data explosion, reinventing the technologies, harnessed to build recommendation systems. And the evolution of hybrid recommendation applications transforming the OTT platforms in offering better services and improving customer outcomes. And the continuum of change in technology space keeps up-rooting the video or audio streaming platforms in providing a seamless experience and retaining more customers. to know more about recommender systems connect with us.
In this article, we tried to analyze the importance and applications of a recommendation engine for OTT. if you find the article relevant, share your thought with us.
2 Comments
Thank you for this article. 🙂 We’re a small retailer and have been looking into getting an online recommendation engine after reading about it on Data Hunters but we weren’t sure whether to use a DaaS provider or build one of our own.
Very informative & insightful. Thanks for this.