The Wall Street Journal ran an intriguing story last month about Netflix’s management overriding recommendations coming from the company’s algorithms.
Analysis showed that promotions for the comedy “Grace and Frankie” were more successful when they only featured one of the two stars of the show.
Apparently fearful of alienating one of their stars, Netflix’s management decided to include both in promotions—even though that would produce a sub-optimal response.
The subtitle of the article mentions
overriding the metrics
However, this isn’t how I see it.
Data science produces inputs to the decision-making process—not recommendations to be followed slavishly. Netflix’s management presumably considered all the information at their disposal and made a decision that they believed would maximise their long-term rewards.
This is as it should be…even at Netflix.
The formal analysis could have been extended to include information on the excluded star’s contract, longevity as an asset, propensity to be offended, etc.
Maybe game theory could have been applied…and some bright Netflix quant could have developed a “diva scale”. But, this would have complicated the analysis considerably and compromised its accuracy.
Looks like data and judgement might have been combined effectively in this case.
