In Online Advertising Auctions, DataXu’s Rocket Science Turns the Tables in Buyers’ Favor
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move its 20-some employees out of their original space at the Cambridge Innovation Center to funky offices in Boston’s Leather District.
When I visited the location in October, staffers were still hanging blueprints of Apollo-era Saturn V rockets on the walls—an homage to the company’s beginnings, which really are in rocket science. MIT aeronautics and astronautics professor Edward Crawley developed the original technology behind DataXu’s decision-support system with students in his lab as an entry in a NASA competition designed to find an automated tool that could judge the best way to get astronauts to Mars and back.
“It was basically a design tool that looked through 30 billion variables, searching vast combinatorial spaces very quickly, to find the successful combinations,” says Bruce Journey, DataXu’s chief revenue officer, who originally joined the company when Crawley asked him to find the most lucrative commercial application for the system. “We looked at a number of markets like commercial aviation, logistics, financial trading execution, but there were none that were nearly as compelling as the online advertising market,” Journey says. (Full disclosure: Journey was CEO at MIT’s Technology Review magazine when I was a senior editor there.)
The DataXu system connects with eight different auction-based ad exchanges, including Google/Doubleclick, Yahoo/Rightmedia, Microsoft, AdMeld, AppNexus, and PubMatic. It continuously sucks in data about their available inventory—which changes from millisecond to millisecond, since real-time ad bidding is all about serving individual impressions to Web surfers at the moment they request a Web page—and picks the best deals for each of DataXu’s customers, depending on the goals they’ve set for their advertising campaigns.
Explains Baker: “It’s looking at the stream of ads and saying ‘No, no, no, no, yes to this one at 47 cents per click, no, no, no, no, yes to that one at 17 cents per click, in an ongoing dialogue” with the ad providers’ networks. Except that the system is also learning as it goes, and it’s making the recommendations very fast—about half a million ad impressions per second were available in October, a number that’s expected to rise to 2.5 million per second by January.
With all that inventory to sort through, ad buyers need powerful software on their side to find the best prices and the most effective venues, says Jeffrey Bussgang, a general partner at Flybridge who is on DataXu’s board. “The dirty secret of online advertising is that it is incredibly inefficient to purchase and optimize and impossible to do it in real-time,” Bussgang says. “With its highly automated, machine-learning approach, DataXu solves both problems. There’s no reason every advertiser on the planet wouldn’t want to use the technology to improve the performance of their online advertising.”
For the moment, Havas is DataXu’s only announced customer. Whether more advertisers will come to see DataXu’s services as a worthwhile investment, and whether ad networks will release more of their high-quality inventory for sale through auction platforms, are two of the big questions that will determine what kind of year the startup will have in 2010.
Right now, the major online ad networks still sell the bulk of their display ads through direct contracts negotiated by their sales forces; only the unsold inventory goes into the auction-based exchanges. But Baker thinks rising demand will create more supply. “We think a lot of media buyers and brands will invest aggressively in this way of buying,” he says. “So, put simply, that’s where the money will be. If you’d like to participate in those revenue streams as a seller, you will need to make inventory available.”