One of every five beauty purchases online is made via the Amazon marketplace, according to a new report.
Like online movie maven Netflix, Overstock figures it can come up with a better way to present personalized offers to shoppers by enticing the experts of the world to devise a new method.
Following a script from online movie maven Netflix Inc., which in 2009 awarded $1 million to the contestants who devised a better method for predicting which flicks a Netflix subscriber would like, Overstock.com Inc. is offering to pay $1 million to e-commerce technology experts if they can develop a better algorithm for recommending products to shoppers on Overstock.com.
Any researcher, or research team, whose algorithm can produce 10% more sales than the recommendations technology Overstock.com currently employs will win $1 million. And if the team works at a university, the e-retailer will donate an additional $250,000 to the educational institution. If no team produces a 10% lift, then the algorithm that produces the biggest increase will win a prorated prize. For instance, an 8% lift will earn a prize of $800,000, Overstock.com said in announcing the contest last month.
Overstock.com is working with its current provider of recommendations technology, RichRelevance, on what the two companies call the RecLab Prize.
"This is a phenomenal opportunity to benefit our customers, who will get early, exclusive access to the most advanced recommendations possible through the participation of top educational and research institutions worldwide," says Overstock.com CEO Patrick Byrne. As part of the contest, Overstock.com will be able to exclusively license the winning algorithm for 18 months.
"The Netflix Prize did a great job of mobilizing the research community around a new and interesting problem," says Darren Vengroff, chief scientist at RichRelevance and creator of RecLab, the vendor's testing and research platform. "The RecLab Prize on Overstock.com takes the next step by offering researchers the chance to solve a multidimensional, real-world problem and see how their best algorithms perform when put in front of live shoppers."
Byrne says he believes researchers may be able to achieve 10% better results than Overstock.com's current recommendations technology. "I would be thrilled," he says, "if someone got 10% better lift."