Snackable Content™
A prodigy in the video industry! Snackable Content™ revolutionises TV Operators’ offering by enabling them to automatically create short clips based on a consumer’s interest, in real-time. Our technology distils the video content based on a consumer’s favourite person, topic or interest. This metadata can be used to tailor your video recommendations for every individual consumer. A consumer, in turn, will be able to closely follow their favourite person, topic or interest on a wide range of TV channels.
Real-time insights
Snackable Content™ enables you to identify relevant segments of a TV program for each of your individual viewers and recommend those to them as soon as they’ve been broadcast.
Accuracy and precision
Our technology distills content based on faces, objects, interests and topics, among others, which is what makes it highly accurate and precise.
Optimised recommendations
Based on the rich metadata we’re able to collect from your content, you can provide customers with video recommendations, tailored specifically to their interests and viewing history.
Using machine learning
to create personalised Snackable Content™
Despite having a large amount of media content available, Pay TV services have not been able to offer a user experience similar to OTT and VoD offerings, which can cater to their audiences with short snippets and binge watch possibilities. In order to address this shortcoming, TV providers offer short-form content by manually “cutting” the linear content into VoD assets. However, this is often neither feasible nor a scalable solution. Besides, it is not the only aspect that frustrates consumers: research shows consumers are also struggling to discover new content.
Read the full paper!
Read the full 10-page paper in which we present our platform that utilizes state-of-the-art Machine Learning algorithms for the real-time analysis of thousands of hours of multilingual multimedia content, including television and VoD. These analyses enable us to obtain rich metadata from the content of videos and suggest small chunks of personalised content (”snacks”) to users based on their preferences.