To our best knowledge, this work is the first systematic study for [behavioral targeting] (BT) on real world ad click-through [logs].One issue with the study is that it looks only at coarse-grained user segments, at most 160 segments, not fine-grained, one-to-one, personalized advertising. I would suspect there would be added benefit from fine-grained, personalized targeting to past behavior rather than clustering users into large groups. In fact, as Figure 5 in the paper shows, they do not appear to have hit the point of diminishing returns even on splitting into more segments.
[We] empirically answer ... whether BT truly as the ability to help online advertising ... how much BT can help ... [and] which BT strategy can work better than others.
We observe that the users who clicked the same ad can be over 90 times more similar than the users who clicked different ads .... [which verifies] the basic assumption of BT.
We observe that .... the ads CTR can be improved as high as 670% by the simple user segmentation strategies used for behavioral targeted advertising .... [More] advanced user representation and user segmentation algorithms [yielded improvements of] more than 1,000%.
Through comparing different user representation strategies for BT, we draw the conclusion that user search behavior, i.e. user search queries, can perform several times better than user browsing behavior, i.e. user clicked pages. Moreover, only tracking the short term user behaviors are more effective than tracking the long term user behaviors.
Another issue is that they explicitly did not look at using demographic data or locality. It is possible that many of the BT user segments might be grouped roughly by locality, having derived it from search or browsing behavior. If that is true, then much of the gains they saw from BT could be reaped much more easily by targeting the ads to implicit local information. And, if we target to locality, then the additional gains we could expect from behavior targeting then might be much smaller.
But, limitations aside, it is a great paper. The authors clearly and cleanly state their key question -- what is the value of behavioral targeting for advertising? -- and then analyzes a massive historical log to convincingly derive the likely value. It also provides much guidance for those who might seek to build these or similar systems.
The paper has an interesting conclusion that recent search queries are the most useful indicators of people's interests when targeting ads. Yahoo's Andrei Broder said something similar recently when thinking about targeting advertising. It is also worth noting that others who were looking at the value of personalized search came to similar conclusions ( ).
For more on fine-grained personalized advertising, please see my earlier posts, "What to advertise when there is no commercial intent?" and "A brief history of Findory".