Blog post: In a blog post, Chen writes that the story of the prize should put a damper on ad networks’ claims that they can distinguish themselves by offering better ad-targeting technology. “This means if you combine dozens of the best machine-learning people in the world, some of the cleanest datasets, you get a measly 10.5 percent increase,” he writes. “Compare this to starting a new ad network where you end up with noisy datasets, lots of crappy traffic, and a small team looking at the problem – that’s not an easy path to disruptive change.”

“In general, 10 percent is not a big enough number to counteract the other economic drivers in the ad market, which revolves around better deal terms, a larger selection of advertisers, better ad inventory, etc.”

Post-script: I asked Chen why—if there is, in fact, not much difference between the targeting technology of various ad networks—the ad networks nevertheless were making those claims. He responded: “First off, they are geeks and prefer to emphasize targeting. Second, they (and by extension, their investors) have Google (NSDQ: GOOG) envy, and they think data + algos can solve everything. Third, they look at the ad industry and see how backward it is from a technology standpoint, but don’t appreciate the tremendous amount of sales and marketing excellence in the industry. So as geeks, of course they think they can one-up the ad industry on technology, even though that’s not sufficient to win in the ad industry.

There are just a ton of variables that go into the performance of an actual campaign, of which targeting is merely one variable. And because most ad networks are black-boxes, you can’t really say, ‘we’ll run both of these campaigns side by side but use different targeting techniques and we’ll compare them.’ Furthermore, some of these variables clearly swamp the other ones - for example where the ads run has a huge effect, larger than the targeting technique in most cases.”

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