Wednesday, March 30, 2016

Netflix: Geography, Age, Gender Not Good Predictors

David Morris wrote an article this week for Fortune titled, "Netflix: Geography, Age, and Gender are 'Garbage' for Predicting Taste." Morris writes, 

"'Geography, age, and gender? We put that in the garbage heap,' VP of product Todd Yellin said. Instead, viewers are grouped into “clusters” almost exclusively by common taste, and their Netflix homepages highlight the relatively small slice of content that matches their taste profile. Those profiles could be the same for someone in New Orleans as someone in New Delhi (though they would likely have access to very different libraries)."

Why is this statement so fascinating?  To me, it speaks directly to Netflix's competitive advantage.  If geography, age, and gender were, in fact, accurate predictors of viewers' preferences, then Netflix would have a far less formidable advantage over rivals.  Why?  Well, those variables are easy to identify and measure.  Others can get access to that data quite easily and build predictive algorithms using that information.  However, if more accurate predictive algorithms involve data that are not as publicly available, then Netflix has a key advantage.  In other words, if the predictive power rests with variables that come from proprietary data that Netflix has collected about us, then the sustainability of Netflix's advantage over competitors rises substantially.  The same holds true for any company trying to take advantage of "big data" to develop predictive algorithms.   The key is to unearth variables that matter, but hopefully, variables that are not easily identified and measured by others.  

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