Most people have access to a wide variety of platforms that offer audio and video services. Known collectively as OTT services, these platforms include paid streaming services such as Netflix and Amazon Prime, or free-to-air platforms such as YouTube.
OTT video services continuously look for ways to effectively and efficiently meet the demands of their individual users, and the personalization of content recommendations is one very important means of doing so for content servers.
What is content personalization?
It’s a content personalization marketing strategy that makes OTT video platforms look specifically tailored to the individual consumer by highlighting their consumption patterns and viewing preferences so that customer interaction with the platform is as practical, dynamic and enjoyable as possible.
This personalization tool customizes content recommendations, helping the user to find the movies, series and videos that spark their interest and will bring them pleasure.
How does it work?
The art of personalizing content recommendations takes data that is collected on individual users relating to their behavior and interaction with the OTT service and combines it with certain machine learning algorithms, to activate a broad range of content recommendations, such as genre, category and rating, and others related to users with similar tastes and consumption behaviors.
The personalization of content recommendations is key to maintaining user participation and interaction. tNetflix, for example, has one of the most accurate systems to predict recommendations, which no doubt has helped make it one of the most popular streaming platforms in the world. A few years ago, only the big players had access to these competitive technologies, but nowadays almost any OTT company can take advantage of ML to offer their users accurate and individualized content recommendations.
Successful OTT content recommendation mechanisms are largely based on user interaction with the streaming service, which covers things like the ratings given to a particular title and viewing history.
Of course, these OTT platforms also study and analyze preferences and information on the available metadata, as well as the time of day the user watches the content, the medium or device used to access the OTT service and view the content and many other variables.
Advantages of personalizing content recommendations
Beyond significantly improving user engagement, the main objective of the OTT service provider is of course to attract new subscribers and generate a higher profit margin. This can be achieved through strategies such as the personalization of content recommendations. Here are some of its advantages:
- It maintains the user’s interest, preventing the platform from becoming boring, repetitive or low category.
- It minimizes the risk of cancellation or abandonment of the OTT service.
- It allows the consumer a practical and dynamic interaction with the service.
- It can save the company millions of dollars per year .
- It makes it easier for the user to select the content he or she wants to view from the wide variety of titles or videos available on the platform.
- Browsing fatigue is avoided by reducing the search for content to the user’s particular preferences and tastes.
How can the personalization of content recommendations be improved?
Properly anticipating consumer preferences is not enough. It’s important to keep improving their interaction with the OTT service. JUMP Data-Driven Video helps do just that, so that platforms can also offer a better service, to maximize the experience of consumers and users in general.
Today’s renewed media and entertainment industry is constantly evolving. Users are becoming more numerous and more demanding by the day. JUMP’s Big Data and Artificial Intelligence technologies put commercial data to optimal use for the purposes of improving the personalization of content recommendations for OTT services.
User interaction behavior with the service is monitored to improve the content recommendation system, as are user playback activity and preview tools.
What are previews?
This feature allows the user to briefly view a specific content before playing it in full. It helps pique their interest with a glance at the plot or main topic of the previewed content.
Users can also be given video previews or thumbnail previews that are customized with images of a title or content and regularly accompanied by a brief synopsis and information such as director, cast, duration, etc.
Previews should capture the user’s attention by highlighting the title of the selected content and enhancing the expected viewing experience.
Previews further enhance the synopsis segment or section, where a particular title is briefly described, by giving the user a quick glimpse at the highlights of the selected content, and influence the decision to watch the full content then and there, or at a later time.
Many entertainment companies have opted to include personalization of content recommendations in their services because it works. In an era where customer expectations are continuously growing and where consumers expect personalized experiences rather than standardized content, this is the best alternative.
