The paper turns out to be a quite interesting attempt to measure the impact of online display advertising -- a notoriously difficult problem -- by looking at how it changes people's searching and browsing online. That's hard enough, but these crazy Googlers also are trying to do this without using A/B testing. To do that last trick, they separate people into those the exposed who have seen the ad and the controls who have not seen the ad while carefully limiting the controls only to people who are similar to the exposed.
From the paper:
Traditionally, online campaign effectiveness has been measured by "clicks" ... However, many display ads are not click-able ... and some campaigns hope to build longer-term interest in the brand rather than drive immediate response. Counting clicks alone then misses much of the value of the campaign.By the way, some have speculated in the past ( ) that Google toolbar data is being used for Google's advertising, but there was no public confirmation of that from Google. To my knowledge, this is the first public confirmation that data from Google's ubiquitous toolbar is being used by them in at least some way in their advertising.
Better measures of campaign effectiveness are based on the change in online brand awareness ... [due] to the display ad campaign alone. We ... [find] the change in probability that a user searches for brand terms or navigates to brand sites that can be attributed to an online ad campaign.
Randomized experiments ... are the gold standard for estimating treatment effects ... [but it] requires an advertiser to forego showing ads to some users ... [which] advertisers are not keen to [do] ... Estimation without randomization is more difficult but not always impossible .... Simply put, the controls [we pick] were eligible to be served campaign ads but were not.
Our estimates require summary (not personally identifiable) data on exposed and controls. The summary data are obtained from several sources, including the advertiser's own campaign information, ad serving logs, and sampled data from users who have installed Google toolbar and opted in to enhanced features.
For more on related topics, please see also my November 2008 post, "Measuring offline ads by their online impact", and my July 2008 post, "Google Toolbar data and the actual surfer model".