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Abstract Details
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Activity Number:
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448
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and Marketing
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| Abstract - #300051 |
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Title:
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The Effectiveness of Display Ads
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Author(s):
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Tim Hesterberg*+ and Diane Lambert and David X. Chan and Or Gershony and Rong Ge
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Companies:
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Google Inc. and Google Inc. and Google Inc. and Google Inc. and Google Inc.
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Address:
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651 N. 34th Street, Seattle, WA, 98103, United States
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Keywords:
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ad effectiveness ;
display ads ;
causal modeling ;
irrelevant outcomes ;
observational data ;
selection bias
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Abstract:
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Display ads proliferate on the web, but are they effective? Or are they irrelevant in light of all the other advertising that people see? We describe a way to answer these questions, quickly and accurately, without randomized experiments, surveys, focus groups or expert data analysts. Causal modeling protects against the selection bias that is inherent in observational data, and a nonparametric test that is based on decoy outcomes provides further defense. Computations are fast enough that all processing, from data retrieval through estimation, testing, validation and report generation, proceeds in an automated pipeline, without anyone needing to see the raw data.
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Authors who are presenting talks have a * after their name.
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