Recommender Systems are the New Oil Refineries

From Crude to Shrewd

I read a post by a colleague of mine, Richard Kastelein, who covers the Social TV space in which he proclaims “Scene-Level Television Metadata – Tagging TV – is The New Oil in the Industry”.  There is a great deal of merit to this train of thought, and indeed having access to this kind of metadata about television programming has been a long time in coming.

I like his New Oil analogy and, given my interest in recommender systems, thought it appropriate to continue the analogy meme with the statement that Recommender Systems will be the New Oil Refineries.  

What does this mean?

Crude oil has limited applications until it as been processed and refined into myriad useful products.   Likewise, metadata itself provides a modicum of utility, but its full potential becomes realized when combined and processed with usage behavior. 

Until recently, comprehensive television usage data was not captured, as there was no pipeline to transport the information.  A burgeoning well of data was left untapped because of the limitations of the Set Top Box (STB) platform itself.  The STB had limited data bandwidth, processing power and the majority of its logic was burned into its firmware – making it inadaptable and expensive to upgrade in the field. 

Over The Top (OTT) solutions have arrived and are aimed squarely at solving the STB challenge.  These second screen companion devices (e.g. iPads, tablets) provide the facilities to capture quality data as well as the feedback mechanisms for retrieving that data in a timely fashion. 

OTT solutions will deliver the missing data pipeline and concomitant data elements that are the fuel needed to power sophisticated, data hungry recommendation platforms. 

What are the applications? 

Recommender systems have the capacity to analyze and decision against incredibly large datasets.  As noted above television content can be one of the recommended products delivered – but so can advertising; be it imbedded, pre or post roll.

Ready, Fire, Aim 

If it is indeed true that 82% of TV Ads generate negative ROI then surely it makes plain sense to have a powerful machine learning system in place that can accurately target content to users in real time. 

Television advertisers just don’t understand me 

The combination of new user interaction models, the availability of standardized TV metadata and powerful machine-learning infrastructures will deliver a more personalized experience to each user.  Indeed as users move en masse to tablets for companion viewing and in some instances primary viewing, or as systems allow for user logins, there will be an even greater opportunity to understand the individual.  This personalized experience will reach across all content – including advertising.

Not Just “New” Search

One of my favorite VC bloggers, Mark Suster, posted an article on his site not quite a year ago; The Future of Television & The Digital Living Room.  He touches on the subject of better search in in his 7^th point about Content Discovery and the improvement of the user experience.  What’s missing from his discussion is the introduction of recommendation engine services.  Better Search will not be the difference in next generation TV.  The power of understanding the consumer at a heretofore-unavailable scope via this new OTT data rich model of usage interaction combined with a wealth of television metadata is how we’ll be equipped to offer meaningful, relevant content discovery experiences.  

Consumers need relevant content served up to them - let’s fire up the refinery and make it happen.

Photo credits: © 2011 Eric Wilson