Hal Varian is the Chief Economist at Google, engaged primarily in the design of the company’s ‘advertising auctions’; the auctions that happen every time a search takes place in order to determine the advertising that appears on the results page.

After introducing us to this concept, Steven Levy looks at Google’s “across-the-board emphasis on engineering, mathematical formulas, and data-mining” and how these ‘Google-style auctions’ are applicable to all sorts of applications.

You can argue about [AdWords’] fairness, but arbitrary it ain’t. To figure out the quality score, Google needs to estimate in advance how many users will click on an ad. That’s very tricky, especially since we’re talking about billions of auctions. But since the ad model depends on predicting clickthroughs as perfectly as possible, the company must quantify and analyze every twist and turn of the data. Susan Wojcicki, who oversees Google’s advertising, refers to it as “the physics of clicks.”

[…] “Google needs mathematical types that have a rich tool set for looking for signals in noise,” says statistician Daryl Pregibon, who joined Google in 2003 after 23 years as a top scientist at Bell Labs and AT&T Labs. “The rough rule of thumb is one statistician for every 100 computer scientists.”

Keywords and click rates are their bread and butter. “We are trying to understand the mechanisms behind the metrics,” says Qing Wu, one of Varian’s minions. His specialty is forecasting, so now he predicts patterns of queries based on the season, the climate, international holidays, even the time of day. “We have temperature data, weather data, and queries data, so we can do correlation and statistical modeling,” Wu says. The results all feed into Google’s backend system, helping advertisers devise more-efficient campaigns.