3 Ways Video Recommendation Drives Video Lifetime Value

Video recommendation and discovery are very hot topics across video publishers looking to drive higher returns on their video lifetime value. Attracting a consumer to watch more videos isn’t simple in this attention deficit society we live. However, major video publishers are creating better experience using video intelligence to delight and enhance discovery and keep you coming back for more. In this post we’ll explore the intelligence behind visual recommendation and how to enhance consumer video discovery.

Industry Challenge

Google Video Intelligence Demo At Google Next 17

Google Video Intelligence API demo of video search finding baseball clips within video segments.

Last year we posted on Search Engine Journal How Deep Learning Powers Video SEO describing the advantages of video image labeling and how publishers can leverage valuable data that was otherwise trapped in images. Since then, Google announced at Next17 Video Intelligence . (InfiniGraph was honored to be selected as a Google Video Intelligence Beta Tester) The MAJOR challenge with Google cloud offering is pushing all your video over to Google Cloud, cost per labeling the video at volume and loosing control of your data. So how do you do all this on a budget?

Not all data is created equal

Trending Content - Lacks Image Based Video Machine Learning

Trending Content is based on popularity vs content context and the consumer content consumption.

And, not all video recommendation platforms are created equal  The biggest video publishers are advancing their offerings with intelligence. InfiniGraph is addressing this gap between using video intelligence and creating affordable technology otherwise out of reach.

Outside of the do not track, creating a truly personalized experience is ideal. For VOD / OTT apps creates the best path to robust personalization. For web a more generalized grouping of consumer is required.

See how “Netflix knows it has just 90 seconds to convince the user it has something for them to watch before they abandon the service and move on to something else”.

Video recommendation platforms

Video Recommendation Mantis Powers by KRAKEN Video Machine Learning

Image based video recommendation “MANTIS”. Going beyond simple meta data and trending content to full intelligent context. Powered by KRAKEN.

All video recommendation platforms are reliant on data entered (called Meta data) when it was uploaded to a video content management system.  Title, discription etc. The other main points of data capture plays, time on video and completion indicating watchablity. There is so much more to a videos than raw insights. Did someone watch a video is important but understanding the why in context of other like videos with similar content is intelligence. Many site have trending videos, however, promoting videos that get lots of plays creates a self fulfilling prophecy because trending is being artificially amplified and doesn’t indicate relevance.

An Intelligent Visual Approach

Video Machine Learning, Going beyond meta data is key to a better consumer experience. Trending only goes so far. Visual recommendation looks at all the content based on consumer actions.

Going beyond meta data is key to a better consumer experience. Trending only goes so far. Visual recommendation looks at all the content based on consumer actions.

Surfacing the right video at the right time can make all the difference if people are staying or going.  Leaders like YouTube have already become to leverage artificial intelligence in their recommending videos producing 70% greater watch time. Recently they included animated video previews for their thumbnails pushing take rates even high. This is more proof consumer desire intelligent recommendation and slicker visual presentation.

InfiniGraph provides a definitive differentiation using actions on images and in-depth knowledge of what’s in the video segments to build relevance. Consumer know what they like when they see it. Understand this visual ignition process is key to unlocking the potential of visual recommendation. How do you really know what people would like to play if you really don’t know much about the video content? Understanding the video content and context is the next stage in intelligent video recommendation and personalized discovery.

3 Ways Visual Video Recommendation Drives Video Lifetime Value

1. Visual recommendation – Visual information within video creates higher visual affinity to amplify discovery. Content likeness beyond just meta data opens up more video content to select from. Mapping what people watch is based on past observation, predicting what people will watch requires understand video context.

2.  Video scoring – A much deeper approach to video had to be invented where the video is scored based on visual attribution inside the video and human behavior on those visuals. This scoring lets the content SPEAK FOR ITSELF and enables ordering play list relative to what was watched.

3. Personalized selection - Enhancing discover requires getter intelligence and context to what content is being consumed. Depending on the video publishers environment like OTT or a mobile app can enable high levels of personalization. For consumers using the web a more general approach and clustering consumers into content preferences powers better results while honoring privacy.

The Future is Amazing for Video Discovery

We have learned a great deal from innovative companies like: Netflix, HULU, YouTube and Amazon who have all come a long way in their approach to advanced video discovery. Large scale video publishers have a grand opportunity to embrace a new technology wave and be relevant while creating a visually conducive consumer experience. A major challenge going forward is the speed of change video publishers must adapt if they wish to stay competitive. With InfinGraph’s advanced  technologies designed for video publishers there is hope. Take advantage of this movement and increase your video lifetime value.

Top image from post melding real life with recommendations.