Data between User and Content Metadata

Years ago, I worked with a company that was creating email lists for new film releases.  Studios had outsourced this function to be able to rapidly deploy with “cutting edge” services.  This firm found intriguing data between movie releases by genre and demographics that the studios themselves could not ferret out.  Studios individually had only a certain number of releases in that genre or with that fan demographic each year.  A few years later, one of my fellow instructors at UCLA was working with a studio to connect purchased household data with online DVD purchases.  The resulting recommendation engine mixed purchase information with customer psychographics.

We are way beyond that now.

The marketing and ongoing relationship upside with a SaaS storage provider—knowing what I own and who I am—is tremendous.  Netflix has thrived versus many past competitors (including Wal*Mart’s earlier rent-by-mail incarnation, which it sold to Netflix) by using psychographic data and usage data.  Netflix not only suggests content, but also suggests us into their deep library instead of the revenue-sharing new release bucket.  Vevo, on the music video streaming side, connects user information with behavioral actions and who we seem to be to sell advertising.

Individual media segments have been approaching this question for a while.  Street dates of single SKUs in music have turned into flows of potentially hundreds of SKUs per product, with backhauls of information from disparate and diverse distribution outlets, which now need to be re-digested.  Audience information of who is listening to what has turned into entirely new businesses for companies like Next Big Sound, which tracks listing and usage across  various streams as a freemium product.  It then provides more sophisticated analysis services to the various music labels for the majority of its revenue.

Data between User, Content Metadata, and Place

Privacy, a concern with content in the cloud, becomes more sensitive when the cloud can tell what you are doing, where and when.  The transition now to mobile consumption adds a layer of value to the SaaS: Services can figure out what users are listening to or watching right before they make a purchase decision at a location.   The content consumed, combined with behavior patterns, shares with that service both psychographics and decision-patterns.  Not only are consumers not using the content “privately,” but they also are sharing vast amounts of data about propensity to act and purchase.  This adjacency to decisions will be of extreme value as these models mature and the data market ripens.