The introduction of pay-per-click advertising in the early 2000s by Overture, Google, Yahoo, and others made the Web a lot more appealing to advertisers, since it meant they only had to pay for ads when Web surfers actually clicked through to their sites. A more recent innovation—real-time bidding for individual display-ad impressions—also helps advertisers, by allowing them to spend only on ads that will be shown to qualified leads.
But much of the power in online advertising markets still rests with the sellers of ad space rather than the buyers. To guess which display ads will have the highest click-through rates, for example, buyers in real-time auctions are still dependent on just a few sparse pieces of data from sellers, such as the names of the publications where the ads will appear.
Now a startup in Boston, DataXu, is working to change all that. DataXu’s bidding engine, based on optimization software originally developed by aerospace engineers at MIT, analyzes the ad slots available to a buyer at any given moment and predicts which are most likely to induce clicks or conversions, based on dozens of parameters such as the location of the viewer, the day of the week and the time of day, and the content of the ad itself. It does this 100,000 times per second or more, learning as it goes by measuring actual click-through rates for ads the engine has placed.
“The Internet advertising industry has never really created powerful tools for buyers of advertising,” says Michael Baker, DataXu’s CEO and president. “Yield management and analytics—all that stuff was invented by the sellers, the Yahoos and the Googles and the Microsofts. For the first time, we are going to have those kinds of powerful next-generation tools available for the buyers.”
DataXu’s system could mark the start of a major shift in power in the Web advertising world. Imagine, for instance that an auto company is trying to attract prospective customers to a website where they can sign up for test drives. DataXu’s software would examine the performance of all the prior ads the company has run for the site. “We might learn that from Tuesday to Thursday, from 2:00 to 5:00 in the afternoon, in the Northeast and the South Central regions of the country, there is a much higher likelihood of that ad actually causing a consumer to fill out the test drive form,” Baker says. The software would advise the car company to bid only on ad slots that fit those criteria, and avoid slots that don’t.
Not only does such a system allow companies to figure out which half of their ad budget they’re wasting—to cite the old saying about advertising—but it does away with the whole concept of a “media plan,” the carefully planned list of outlets where ad buyers would traditionally look for ad inventory (i.e., available impressions). “We’re saying there’s a new paradigm, which is that you can plan your media buy based on empirical results taken from the Internet on the fly,” says Baker. At least one major customer, the Havas network of advertising agencies, has signed up to use DataXu’s system.
DataXu was founded in 2007 but flew under the radar until its coming-out at the TechCrunch50 conference in San Francisco this September. (The “Xu” in the name comes from the Mandarin word for “need,” according to Baker.) A $6 million round of venture funding from Flybridge Capital Partners and Atlas Venture last spring allowed DataXu to
