The Ensemble Effect
Netflix, Crowdsourcing, and Supercharging Prediction
To crowdsource predictive analytics—outsource it to the public at large—a company launches its strategy, data, and research discoveries into the public spotlight. How can this possibly help the company compete? What key innovation in predictive analytics has crowdsourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds?
Casual Rocket Scientists
A buddy and I are thinking of building a spaceship next year. The thing is, we have absolutely no training or background. But who cares? I want to go to outer space.
This may sound outlandish, but in the realm of predictive analytics (PA), it is essentially what Martin Chabbert and Martin Piotte did. In 2008, this pair of Montrealers launched a mission to win the $1 million Netflix Prize, the most high-profile analytical competition of its time. Incredibly, with no background in analytics, these casual part-timers became a central part of the story.
The movie rental company Netflix launched this competition to improve the movie recommendations it provides to customers. The company challenged the world by requiring that the winner improve upon Netflix’s own established recommendation capabilities by 10 percent. Netflix is a prime example of PA in action, as a reported 70 percent of Netflix movie choices arise from its online recommendations. Product ...